Novel construction method and system for electricity-carbon one-picture graph of electric power system

Abstract

The invention relates to a construction method and system of a novel power system electricity-carbon one-picture graph, and the method comprises the steps: collecting new energy output data, power grid power flow data, load data, energy storage side charge state and carbon emission data in real time, and carrying out the preprocessing; based on the collected data, establishing an electricity-carbon coupling model of source load storage and carbon emission, calculating wind-light carbon emission reduction, user electricity utilization carbon emission and charging carbon emission, correcting a power transmission unit distance carbon loss coefficient, updating power transmission link carbon loss, and constructing an electricity-carbon graph; when a preset correction period is reached, relative errors between the carbon emission output by the source-load electricity storage carbon coupling model and the actual carbon emission are calculated respectively; when the relative error is larger than a preset error threshold value, the regional thermal power average carbon emission factor and the power transmission unit distance carbon loss coefficient are corrected, and the electricity-carbon map is updated. According to the method, through real-time electricity-carbon coupling modeling, power transmission carbon factor dynamic correction and a parameter self-correction mechanism, accurate calculation and real-time updating of the electricity-carbon graph are achieved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of carbon emission monitoring, and relates to a construction method and system of a new power system electric-carbon one-map. BACKGROUND

[0002] With the promotion of the "double carbon" target and the construction of the new power system, the source-grid-load-storage full-factor data presents the characteristics of multi-source and heterogeneity, and there are significant barriers in the interaction of electric-carbon data. At present, the power system and the carbon management system are independently operated, the electric power data (such as power generation power, load demand, and power grid flow) and the carbon data (such as carbon emission factor, carbon emission, and carbon footprint) are not deeply fused, there is a lack of a unified data interaction model, which leads to insufficient electric-carbon coupling analysis accuracy. At the same time, the existing visualization tools are mostly focused on a single business (such as power dispatching or carbon accounting), and cannot realize multi-dimensional and multi-state presentation of source-grid-load-storage full factors, making it difficult to support electric-carbon full-factor same-map management and accurate perception, and failing to meet the real-time and overall needs of the new power system for electric-carbon collaborative management. Therefore, it is urgent to build a systematic technical system that adapts to the electric-carbon fusion scene.

[0003] At present, the existing carbon emission monitoring methods include: the patent with the publication number CN117436026A provides a carbon verification and electric power data fusion system and method based on a block chain. The method collects original electric power data of a power grid enterprise through a sensor, and converts the data into carbon emission data through an intelligent contract, a carbon emission calculation mathematical model, and a neural network model combined with Squeeze Net and Bi-LSTM in a data fusion unit. After verification by multiple systems, the data is transmitted to a block chain platform for encryption storage and construction of an interactive chain domain, realizing efficient fusion of carbon verification and electric power data, improving data security, preventing tampering, and query efficiency, and transparently and publicly displaying the verification results on the chain. The Chinese patent CN120123411A discloses an electric-carbon one-map data fusion and multi-dimensional representation method. The method deploys an edge computing node network and dynamically allocates key fusion data, uses a deep learning model containing convolution and cycle layers, adjusts parameters combined with reinforcement learning, performs prediction and visual display, and can improve the electric-carbon data processing efficiency and multi-dimensional representation accuracy, reduce the computing burden, and support real-time monitoring and adaptive changes in the power system.

[0004] In summary, although the existing technology can realize the fusion and visualization of electric power data and carbon emission, it cannot perform fine electric-carbon coupling modeling on source-grid-load-storage, and lacks a carbon factor dynamic correction and self-closed loop optimization mechanism, so it is difficult to construct an accurate, real-time, and self-adaptive updated electric-carbon one-map. SUMMARY

[0005] To solve the problems in the prior art, the application provides a novel method and system for constructing an electric-carbon one-diagram of a power system. The method comprises the following steps: collecting new energy output data, power grid power flow data, load data, energy storage side load state and carbon emission data in real time, and preprocessing; based on the collected data, electric-carbon coupling models of source-load-storage and carbon emission are respectively established, wind-solar carbon emission reduction, user electricity carbon emission and charging carbon emission are calculated, the carbon loss coefficient per unit distance of power transmission is corrected and the carbon loss of the power transmission link is updated, and the electric-carbon one-diagram is constructed; when a predetermined correction period is reached, the relative errors of the carbon emissions output by the source-load-storage electric-carbon coupling models and the actual carbon emissions are respectively calculated; when the relative error is greater than a predetermined error threshold, the average carbon emission factor of regional thermal power and the carbon loss coefficient per unit distance of power transmission are corrected, and the electric-carbon one-diagram is updated. Through real-time electric-carbon coupling modeling, dynamic correction of the carbon factor of power transmission and a parameter self-correction mechanism, the application realizes accurate calculation and real-time updating of the electric-carbon one-diagram.

[0006] The application adopts the following technical solutions.

[0007] The first aspect of the application provides a novel method for constructing an electric-carbon one-diagram of a power system, comprising the following steps: S1, collecting new energy output data, power grid power flow data, load data, energy storage side load state and carbon emission data in real time, and preprocessing; S2, based on the preprocessed collected data, electric-carbon coupling models of source-load-storage and carbon emission are respectively established, wind-solar carbon emission reduction, user electricity carbon emission and charging carbon emission are calculated, the line flow fluctuation rate and the change of loss rate of the power transmission line are calculated at each time, when the line flow fluctuation rate is greater than a predetermined flow fluctuation threshold or the change of loss rate is greater than a predetermined loss change threshold, the carbon loss coefficient per unit distance of power transmission is corrected and the carbon loss of the power transmission link is updated; S3, based on the carbon emission data obtained in S2 and the collected power data, an electric-carbon one-diagram is constructed; S4, at each sampling time, the power fluctuation rate of source-grid-load-storage is calculated; when the power fluctuation rate is greater than a predetermined power fluctuation threshold and the annual carbon quota exceeds a quota warning threshold, it indicates that the data is abnormal, the abnormal data is returned to the electric-carbon one-diagram and displayed; when a predetermined correction period is reached, the relative errors of the carbon emissions output by the source-load-storage electric-carbon coupling models and the actual carbon emissions are respectively calculated; when the relative error is greater than a predetermined error threshold, the average carbon emission factor of regional thermal power and the carbon loss coefficient per unit distance of power transmission are corrected, and the electric-carbon one-diagram is updated.

[0008] Preferably, in S1, for the carbon emission data, the monthly regional total carbon emission is divided by the monthly regional total power generation to obtain the regional carbon emission factor of the corresponding month, and then linear interpolation is performed to obtain the regional carbon emission factor at each sampling time; The initial carbon emission value of the power generation link is calculated by multiplying the power generation by the collected carbon emission factor of thermal power. The initial carbon loss value of the transmission link is obtained by multiplying the collected power flow of the power grid by the transmission distance and temporary losses. The temporary loss is taken as the historical average coefficient of the regional power grid. The initial carbon emission value of the power consumption link is obtained by multiplying the electricity consumption by the regional carbon emission factor.

[0009] Preferably, the process for calculating the carbon emission reduction from wind and solar power in S2 is as follows: At each sampling time, the carbon emission factor of all thermal power units in the region is multiplied by the power generation of the corresponding thermal power unit and summed as the numerator, and the power generation of all thermal power units in the region is summed as the denominator to obtain the average carbon emission factor of thermal power in the region at the corresponding sampling time. The wind and solar power output at the sampling time is multiplied by the sampling interval and the corresponding average carbon emission factor of thermal power in the region to obtain the wind and solar carbon emission reduction at the corresponding sampling time.

[0010] Preferably, the process of calculating the user's electricity consumption carbon emissions in S2 is as follows: At each sampling time, the carbon emission factors of different types of power sources are multiplied by the power supplied to the load by the corresponding power source and summed as the numerator. The total power supplied to the load by the power source is used as the denominator to obtain the mixed carbon emission factor of the power source at the corresponding sampling time. The load power at the sampling time is multiplied by the sampling interval duration and the corresponding mixed carbon emission factor of the power source in sequence to obtain the user's electricity consumption carbon emissions at the corresponding sampling time.

[0011] Preferably, the process for calculating the carbon emissions during charging in S2 is as follows: At each sampling time, the carbon emission factors of different types of generating units are multiplied by the power generation of the corresponding generating units and summed to obtain the numerator. The power generation of all generating units is summed to obtain the real-time carbon emission factor of the power grid at the corresponding sampling time. The charging power of the energy storage device at the sampling time is multiplied by the sampling interval and the corresponding real-time carbon emission factor of the power grid to obtain the charging carbon emission at the corresponding sampling time.

[0012] Preferably, the formula for calculating the corrected carbon loss coefficient per unit distance in S2 is as follows: ; In the formula, Let be the carbon loss coefficient per unit distance of power transmission at sampling time t; Marginal carbon emission factor; Let t be the network loss rate at sampling time t; This refers to the line length; Let t be the ambient temperature difference at sampling time t; This refers to the temperature sensitivity coefficient. The regional carbon emission factor at sampling time t; This is the penalty factor for the squared term of the network loss rate.

[0013] Preferably, the process of updating carbon losses in the power transmission link in S2 is as follows: Based on the carbon loss coefficient per unit distance of power transmission at the current sampling time, and combined with the corresponding power flow, calculate the carbon loss of the power transmission link at the current time: ; In the formula, Let t be the carbon loss of the power transmission link at sampling time t, i.e., the carbon loss of the power transmission link. Let t be the power flow of the line at sampling time t; This is the amplification factor, used to control the intensity of nonlinear effects under high loads; This refers to the rated power of the line. To amplify the index.

[0014] Preferably, the process of correcting the regional average carbon emission factor of thermal power and the carbon loss coefficient per unit distance of transmission in S4 is as follows: For each thermal power unit, the carbon emission offset is obtained by multiplying the unit's power generation by the corresponding carbon emission factor and then subtracting the corresponding actual carbon emissions from the transmission link as the numerator, and using the standard deviation of the actual carbon emissions of all thermal power units as the denominator. If the carbon emission offset is not greater than a predetermined offset threshold, the carbon emission offset is squared and divided by 2 to obtain the transmission carbon emission difference term. If the carbon emission offset is greater than the predetermined offset threshold, the carbon emission offset is subtracted by half of the predetermined offset threshold and then multiplied by the predetermined offset threshold to obtain the power generation carbon emission difference term. Based on the power generation carbon emission difference term and the regional average carbon emission factor of thermal power, an optimization objective function for thermal power carbon emissions is constructed. Based on the carbon loss in the transmission links of each transmission line and the actual carbon loss in the transmission links, calculate the carbon loss offset and carbon loss volatility; based on the carbon loss offset, the carbon loss coefficient per unit distance of transmission, and the carbon loss volatility, construct an objective function for optimizing the carbon loss coefficient. The optimal regional average carbon emission factor for thermal power and the carbon loss coefficient per unit distance of power transmission are obtained through optimization algorithms and used as the corrected parameters.

[0015] Preferably, the formula for constructing the objective function for optimizing carbon emissions from thermal power plants is as follows: ; In the formula, This represents the average carbon emission factor for regional thermal power generation. and These represent their upper and lower limits, respectively. For the carbon emission difference item of power generation; This is a priori estimate of the carbon emission factor; t represents the regional average carbon emission factor of thermal power plants at sampling time t; K represents the number of sampling points selected before the current time. , and These represent the weighting coefficients for the power generation carbon emission difference term, the prior carbon emission factor difference term, and the time-series carbon emission factor difference term, respectively.

[0016] Preferably, the formula for constructing the objective function for optimizing carbon emissions from thermal power plants is as follows: ; In the formula, M represents the total number of transmission lines; The adaptive weights for the l-th transmission line are: This is due to carbon loss offset. The standard deviation of carbon loss offset for all transmission lines; This is a priori estimate of the carbon loss coefficient per unit distance in power transmission; This is an indicator function used to penalize the distribution of carbon loss volatility, representing the carbon loss volatility. The value is 1 if it is less than 0, otherwise it is 0; This is a penalty coefficient used to adjust the penalty for carbon loss volatility; and These are the weighting coefficients for the second and third terms, respectively.

[0017] A second aspect of the present invention provides a novel system for constructing a single electrical carbon map of a power system, and a novel method for constructing a single electrical carbon map of a power system, comprising: The carbon emission data acquisition module collects real-time data on renewable energy output, power grid flow, load, energy storage side charge status, and carbon emission, and performs preprocessing. The carbon emission accounting module establishes an electric carbon coupling model for source-load-storage and carbon emissions based on the preprocessed collected data, and calculates the carbon emission reduction of wind and solar power, the carbon emission of user electricity consumption, and the carbon emission of charging. At each time point, it calculates the changes in the line power flow fluctuation rate and the network loss rate of the transmission line. When the line power flow fluctuation rate is greater than the predetermined power flow fluctuation threshold or the network loss rate change is greater than the predetermined network loss change threshold, it corrects the carbon loss coefficient per unit distance of the transmission line and updates the carbon loss of the transmission link. The "Electricity Carbon Map Construction Module" constructs an electricity carbon map based on carbon emission data obtained from S2 and collected electricity data. The electricity carbon data correction module calculates the power fluctuation rate of the source-grid-load-storage system at each sampling time. When the power fluctuation rate exceeds the predetermined power fluctuation threshold and the annual carbon quota exceeds the quota warning threshold, it indicates data abnormality, and the abnormal data is sent back to the electricity carbon map for display. When the predetermined correction cycle is reached, the relative error between the carbon emissions output by the source-load-storage electricity carbon coupling model and the actual carbon emissions is calculated. When the relative error exceeds the predetermined error threshold, the average carbon emission factor of thermal power in the region and the carbon loss coefficient per unit distance of transmission are corrected, and the electricity carbon map is updated.

[0018] A third aspect of the present invention provides a terminal, including a processor and a storage medium; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of a novel method for constructing an electrocarbon map of a power system.

[0019] A fourth aspect of the invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a novel method for constructing an electrical carbon map of a power system. Compared with the prior art, the beneficial effects of the present invention include at least the following: 1. This invention constructs a multi-dimensional electricity-carbon coupling model by real-time acquisition of data on renewable energy output, power flow, load, energy storage status of charge, and carbon emissions. This model can calculate carbon emission reductions from wind and solar power, carbon emissions from user electricity consumption, and carbon emissions from energy storage charging. Compared to methods that only perform data fusion or coarse-grained carbon accounting, this invention achieves real-time, fine-grained, and dynamic calculation of carbon emissions at each stage of the power system, making the expression of the electricity-carbon relationship more accurate.

[0020] 2. This invention calculates the changes in line power flow fluctuation rate and network loss rate in real time at each sampling moment, and dynamically corrects the carbon loss coefficient per unit distance of transmission when the value exceeds a preset threshold. Simultaneously, it updates the carbon loss of the transmission link by combining power flow data. This method integrates influencing factors such as network loss rate, ambient temperature difference, and marginal carbon emission factor, accurately reflecting the impact of actual operating conditions on transmission carbon emissions, significantly reducing errors, and making the carbon emission accounting of the transmission link adaptive, timely, and highly accurate.

[0021] 3. This invention continuously monitors the power fluctuation rate of power generation, grid, load, and storage during operation, automatically identifying and transmitting abnormal data. Within the correction period, it compares the relative error between the model's output carbon emissions and the actual carbon emissions. When the error exceeds a threshold, it constructs optimization objective functions for thermal power carbon emissions and transmission carbon loss coefficients, respectively. The optimal carbon factor is obtained through optimization algorithms, achieving dynamic correction of the regional average carbon emission factor for thermal power and the carbon loss coefficient per unit distance of transmission. This mechanism enables the electricity-carbon data map to have self-checking and parameter self-correcting capabilities, ensuring that carbon emission data maintains high accuracy and stability over long-term operation. Attached Figure Description

[0022] Figure 1 The flowchart illustrates a novel method for constructing a single carbon map of a power system, as provided by this invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of this invention.

[0024] Example 1 Embodiment 1 of the present invention provides a novel method for constructing a single electrical carbon map of a power system, see below. Figure 1 This includes the following steps: S1. Real-time acquisition of new energy output data, power grid flow data, load data, energy storage side charge status and carbon emission data, and preprocessing.

[0025] As a preferred implementation method, the real-time data acquisition process is as follows: On the power supply side: real-time wind and solar power output data are collected from the Supervisory Control and Data Acquisition (SCADA) system of renewable energy power plants, with a sampling frequency of 15 minutes / time; daily average carbon emission factors of thermal power plants are collected from the Continuous Emission Monitoring System (CEMS) of thermal power plants, and converted into time series data with a time interval of 15 minutes through linear interpolation to obtain the carbon emission factors of each thermal power unit at each collection time. On the grid side: Obtain nodal power flow data from the Energy Management System (EMS); calculate the line loss rate every 15 minutes based on the power of the starting and ending nodes of the line. ;in, For the power of the first node, Power at the end node; Load side: User electricity demand load is collected every 15 minutes from the electricity consumption information collection system. The average value of user electricity demand load at the corresponding time point in the three working days prior to the data collection time will be used as the baseline load for that time point. The actual load reduction is obtained by subtracting the actual user electricity demand load at the corresponding time from the baseline load. ; Energy storage side: Real-time charging and discharging power data are obtained from the power conversion system (PCS); every 15 minutes, the charging and discharging power is integrated over time to obtain the current energy level at the corresponding moment. Combined with the rated capacity of energy storage Calculate the state of charge at the corresponding time. ; For carbon emission data, the monthly total regional carbon emissions are divided by the monthly total regional power generation to obtain the regional carbon emission factor for the corresponding month. Then, through linear interpolation, the regional carbon emission factor with a time interval of 15 minutes is obtained. The power generation on the power source side is obtained, and the initial value of carbon emissions in the power generation stage is calculated by multiplying the power generation by the collected carbon emission factor of thermal power. The initial value of carbon loss in the transmission stage is obtained by multiplying the collected power flow of the power grid by the transmission distance and temporary losses, respectively. The temporary loss is taken as the historical average coefficient of the regional power grid. The initial value of carbon emissions in the power consumption stage is obtained by multiplying the electricity consumption by the regional carbon emission factor. Annual carbon allowance data is obtained from regional carbon trading platforms and then distributed equally to generate monthly carbon allowance data.

[0026] As a preferred implementation, the preprocessing includes outlier removal and data alignment; In addition to adopting the 3σ principle, outlier removal will also identify power data exceeding ±20% of the equipment's rated power as outlier and replace it with the value from the previous moment; if carbon emission data is negative, it will be directly replaced with 0. To ensure that all data are time-series aligned, carbon emission data were converted from 1-hour intervals to 15-minute intervals using linear interpolation.

[0027] As a preferred implementation, the collected multi-source data is stored in a distributed data warehouse built on the Hadoop architecture, using a three-level storage directory structure: First-level categories: categorized by electricity data and carbon data; Second-level categories: Power data is divided into power source side, grid side, load side and energy storage side based on different sources; carbon data is divided into carbon emission factors, emissions at different stages and carbon quotas. Three-level directories: named according to timestamps, each directory stores structured data files corresponding to the time (in Parquet format, supporting column storage for fast querying); at the same time, a data index table is created to record "data type - collection time - storage path - data check code" to ensure the efficiency of real-time writing and historical query; among which, the latency of real-time writing is ≤1 second; the response time of historical query is ≤3 seconds.

[0028] S2. Based on the preprocessed collected data, establish the source-load-storage and carbon emission coupling models respectively, and calculate the carbon emission reduction of wind and solar power, the carbon emission of user electricity consumption, and the carbon emission of charging. Calculate the line power flow fluctuation rate and network loss rate change of the transmission line at each time point. When the line power flow fluctuation rate is greater than the predetermined power flow fluctuation threshold or the network loss rate change is greater than the predetermined network loss change threshold, correct the carbon loss coefficient per unit distance of transmission and update the carbon loss of the transmission link.

[0029] For the power supply side, at each sampling time, the carbon emission factors of all thermal power units in the region are multiplied by the power generation of the corresponding thermal power unit and summed to obtain the numerator. The power generation of all thermal power units in the region is summed to obtain the denominator, thus obtaining the average carbon emission factor of thermal power in the region at the corresponding sampling time. The wind and solar power output at the sampling time is multiplied by the sampling interval and the corresponding average carbon emission factor of thermal power in the region to obtain the wind and solar carbon emission reduction at the corresponding sampling time. The specific calculation formula is as follows: ; in, This refers to the carbon emission reduction caused by wind and solar power generation. Powering wind power; Contribute to photovoltaic power; The sampling interval is 15 minutes in this embodiment; The average carbon emission factor for regional thermal power; Let be the carbon emission factor of the i-th single thermal power unit; This refers to the power generation of the corresponding generating unit; Multiply the power generation of all units at time t by the regional average carbon emission factor of thermal power, and then subtract the corresponding carbon emission reduction of wind and solar power to obtain the carbon emission of the power generation process at time t. For the load side, at each sampling time, the carbon emission factors of different types of power sources are multiplied by the corresponding power supply to the load, and the sum is used as the numerator. The total power supply to the load is used as the denominator to obtain the mixed carbon emission factor of the power sources at the corresponding sampling time. The load power at the sampling time is then multiplied by the sampling interval and the corresponding mixed carbon emission factor of the power sources to obtain the user's electricity consumption carbon emissions at the corresponding sampling time. The specific calculation formula is as follows: ; in, This refers to the carbon emissions from electricity consumption by users, i.e., the real-time carbon emissions from the electricity consumption process. This refers to the load power, i.e., the power consumed by the user. The mixed carbon emission factor of the power source corresponding to the load; For the j-th type of power source carbon emission factor; Let be the power supplied to the load by the j-th type of power source; This refers to the total power supplied by the power source to the load. For the energy storage side, at each sampling time, the carbon emission factors of different types of generating units are multiplied by the corresponding generating power and summed to obtain the numerator. The generating power of all generating units is summed to obtain the real-time carbon emission factor of the power grid at the corresponding sampling time. The charging power of the energy storage device at the sampling time is multiplied by the sampling interval and the corresponding real-time carbon emission factor of the power grid to obtain the charging carbon emission at the corresponding sampling time. The specific formula is as follows: ; in, Carbon emissions from charging; The charging power for energy storage devices; Real-time carbon emission factor for the power grid; Let be the carbon emission factor of the u-th unit; Let be the power generation capacity of the u-th unit; since the discharge does not consume new electrical energy, but only releases stored electrical energy, the carbon emissions from the discharge of the energy storage device are 0.

[0030] In a preferred embodiment, when the power flow fluctuation rate of any transmission line exceeds a predetermined power flow fluctuation threshold or the change in network loss rate exceeds a predetermined network loss change threshold, the carbon loss coefficient per unit distance of transmission is corrected and the transmission carbon loss is updated; the specific process is as follows: Divide the absolute value of the difference between the power flow of the transmission line at the current sampling time and the power flow at the previous sampling time by the power flow at the previous sampling time. The line power flow volatility at the current sampling moment Divide the absolute value of the difference between the network loss rate of the transmission line at the current sampling time and the previous sampling time by the network loss rate at the previous sampling time. The change in network loss rate at the current sampling time In this embodiment, the predetermined power flow fluctuation threshold is 15%, and the predetermined network loss change threshold is 5%. When the power flow fluctuation rate of any transmission line exceeds a predetermined power flow fluctuation threshold or the change in network loss rate exceeds a predetermined network loss change threshold, the carbon loss coefficient per unit distance of transmission at the current sampling time is corrected: ; In the formula, Let be the carbon loss coefficient per unit distance of power transmission at sampling time t; The marginal carbon emission factor is obtained by dividing the total increase in carbon emissions of the power system by the increase in load. This refers to the line length; Let t be the ambient temperature difference at sampling time t, that is, the difference between the ambient temperature and the reference temperature at time t; The temperature sensitivity coefficient represents the degree to which temperature changes amplify network losses. The regional carbon emission factor at sampling time t; The penalty factor is the squared term of the network loss rate; Based on the carbon loss coefficient per unit distance of power transmission at the current sampling time, and combined with the corresponding power flow, calculate the carbon loss of the power transmission link at the current time: ; In the formula, Let t be the carbon loss of the power transmission link at sampling time t, i.e., the carbon loss of the power transmission link. Let t be the power flow of the line at sampling time t; This is the amplification factor, used to control the intensity of nonlinear effects under high loads; in this embodiment, it is set to 0.1. This refers to the rated power of the line. To amplify the exponent and reflect the degree of nonlinearity, p=2 in this embodiment.

[0031] In this embodiment, carbon loss in the power transmission link is calculated every 15 minutes based on a calculation process, including the carbon emissions on the power supply side, the carbon emissions on the load side, and the carbon loss in the power transmission link at the corresponding time.

[0032] S3. Based on the carbon emission data obtained in S2 and the collected electricity data, construct a single graph of electricity and carbon emissions.

[0033] S3.1. Access the 1:5000 high-precision map of the power geographic information system (GIS) and extract vector data of the spatial location and attributes of the equipment, including: the coordinates and voltage level of the substation, the direction, length and material of the line, and the location and capacity of the source-grid-load-storage connection point; import the equipment management ledger and the real-time status data of the equipment; among which, the equipment management ledger includes the equipment model, commissioning time and rated parameters, and the real-time status data of the equipment includes the switch opening and closing status and the equipment operating power; S3.2. Using the adjacency matrix method, substations are abstracted as core nodes, lines as edges, and access points as terminal nodes. Based on the physical connection attributes in the equipment management ledger (such as the substation numbers connected at both ends of the line and the line number to which the access point belongs), an adjacency matrix is ​​generated. For example, in the adjacency matrix A, A[i][j]=1 indicates that node i and node j are directly connected, and A[i][j]=0 indicates that there is no connection. The matrix is ​​updated once per hour according to the real-time status data of the equipment (such as line outages). Based on the core nodes, terminal nodes, edges, and the corresponding adjacency matrix, a topology graph is constructed. S3.3. Compare the connection relationships in the topology graph with the power flow data. If a certain line is connected in the topology graph (i.e., the connection matrix value between the nodes at both ends of the line is 1), but the power flow value is 0, automatically trigger a re-verification of the connection relationships and correct the adjacency matrix between devices. At 2:00 AM every day, perform a comprehensive verification of the topology graph using power flow data from the past month to ensure that the topology accuracy is ≥99.5%. S3.4. Based on power data, set up a power layer; based on carbon emission data, create a carbon layer; based on the topology map, power layer, and carbon layer, set up an interactive layer; based on the power layer, carbon layer, and interactive layer, construct a single power-carbon map; the specific process is as follows: Set up a power layer to directly access real-time power generation, user electricity demand load, and nodal power flow data for wind, solar, thermal, and energy storage; power values ​​are mapped using color gradients; for example, 0-100MW is light green, 100-500MW is dark green, and above 500MW is blue; Set up a carbon layer to display carbon emissions by stage, carbon emission reductions from wind and solar power, carbon emissions from electric charging, and remaining carbon allowances; use statistical charts to display the data, with the height of the bar chart corresponding to the emission value (unit: tCO2), the line chart corresponding to the remaining carbon allowances, and the data labels directly displaying the specific values. Set up an interactive layer and, through a distributed data warehouse, return the output curves and carbon reduction trend data for the selected elements over the past 24 hours.

[0034] In this embodiment, for the time dimension, real-time data is directly retrieved from the latest output of the electric carbon coupling model within 15 minutes; historical data is queried from the distributed data warehouse by time range (last 24 hours / 7 days) and data type, and trend charts are automatically generated; for the spatial dimension, equipment nodes and data within the corresponding range are filtered by administrative region boundaries (province / city / county), and the topology map automatically hides equipment outside the region; data on lines and substations of the corresponding voltage level (500kV / 220kV) are filtered, and low-voltage level equipment is collapsed by default; for the business dimension, preset data filtering rules are used, such as displaying only power generation and carbon emission reduction on the power supply side; displaying only electricity load demand and electricity carbon emissions on the load side, and directly calling the corresponding rules to filter data when switching between the power supply side and the load side.

[0035] S4. At each sampling time, calculate the power fluctuation rate of the source-grid-load-storage system. When the power fluctuation rate is greater than the predetermined power fluctuation threshold and the annual carbon quota exceeds the quota warning threshold, it indicates that the data is abnormal. The abnormal data is then transmitted back to the electricity-carbon graph and displayed. When the predetermined correction period is reached, calculate the relative error between the carbon emissions output by the source-load-storage electricity-carbon coupling model and the actual carbon emissions. When the relative error is greater than the predetermined error threshold, correct the average carbon emission factor of thermal power in the region and the carbon loss coefficient per unit distance of transmission.

[0036] As a preferred implementation, for the source-grid-load-storage system, the absolute value of the difference between the power at the current time and the power at the previous time is calculated and then divided by the power at the previous time. The corresponding power fluctuation rate is obtained. .

[0037] In this embodiment, the predetermined power fluctuation threshold is 10%; the quota warning threshold is 80% of the annual quota.

[0038] As a preferred real-time method, when an anomaly is triggered, the abnormal data is marked, the carbon emissions are recalculated according to the corresponding electro-carbon coupling model, and the index of the abnormal device is synchronized to the electro-carbon map. After receiving the data, the electro-carbon map highlights the abnormal device (i.e., it is outlined with a red border) and pops up the anomaly details, including the abnormal value, threshold, and suggested measures. The entire process from marking the data to sending it back to the electro-carbon map is completed within 3 seconds. As a preferred implementation, data in the distributed data warehouse is filtered by inputting region, time, and business type (such as load-side carbon emissions), and the corresponding charts are generated and returned to the Electricity Carbon Chart. The response time of this process is ≤5 seconds.

[0039] As a preferred implementation, every hour, for each electric carbon coupling model, the absolute value of the difference between the carbon emissions output by the model and the actual carbon emissions is divided by the actual carbon emissions and converted into a percentage to obtain the corresponding relative error; when the relative error of any model is greater than 5%, the parameters are updated.

[0040] As a preferred implementation method, the process of correcting the regional average carbon emission factor of thermal power and the carbon loss coefficient per unit transmission distance is as follows: For each thermal power unit, the carbon emission offset is calculated by multiplying the unit's power generation by the corresponding carbon emission factor and then subtracting the corresponding actual carbon emissions from the transmission link. The standard deviation of the actual carbon emissions from all thermal power units is used as the denominator. If the carbon emission offset is not greater than a predetermined offset threshold, the square of the carbon emission offset is divided by 2 to obtain the transmission carbon emission difference term. If the carbon emission offset is greater than the predetermined offset threshold, half of the carbon emission offset is subtracted from the predetermined offset threshold, and then multiplied by the predetermined offset threshold to obtain the power generation carbon emission difference term. The specific formula is as follows: ; In the formula, For the carbon emission difference item of power generation; This represents the actual carbon emissions of the i-th thermal power unit in the power generation process. This represents the standard deviation of the actual carbon emissions from thermal power units. The predetermined offset threshold; Based on the difference in carbon emissions from power generation and the regional average carbon emission factor for thermal power, an objective function for optimizing carbon emissions from thermal power is constructed: ; In the formula, This represents the average carbon emission factor for regional thermal power generation. and These represent their upper and lower limits, respectively. The carbon emission factor is a priori estimate; in this embodiment, the historical average value within a predetermined historical period is used. t represents the regional average carbon emission factor of thermal power plants at sampling time t; K represents the number of sampling points selected before the current time. , and The weighting coefficients for the carbon emission difference term in power generation, the prior carbon emission factor difference term, and the time-series carbon emission factor difference term are respectively obtained by fitting data within a predetermined historical period; For each transmission line, the carbon loss in the transmission process will be... Subtract carbon losses in actual power transmission Then divide by the standard deviation of carbon losses in the actual power transmission process. As carbon loss offset ; The carbon loss volatility is calculated by subtracting the actual carbon loss from the carbon loss in the transmission process and then dividing by the actual carbon loss in the transmission process. ; Based on carbon loss offset, carbon loss coefficient per unit transmission distance, and carbon loss volatility, an objective function for optimizing the carbon loss coefficient is constructed: ; In the formula, M represents the total number of transmission lines; The adaptive weights for the l-th transmission line are: The standard deviation of carbon loss offset for all transmission lines; The prior estimate of the carbon loss coefficient per unit distance of power transmission is taken in this embodiment as the historical average value within a predetermined historical period; This is an indicator function used to penalize the carbon loss volatility distribution, indicating that it is 1 when the carbon loss volatility is less than 0, and 0 otherwise. This is a penalty coefficient used to adjust the penalty for carbon loss volatility; and The weighting coefficients for the second and third items are obtained by fitting data within a predetermined historical period; For each objective function, the weight coefficients of each term are fitted based on data within a predetermined historical period; based on the determined weight coefficients, the optimal regional average carbon emission factor of thermal power and the carbon loss coefficient per unit distance of power transmission are solved by an optimization algorithm and used as the corrected parameters; in this embodiment, the optimization algorithm includes, but is not limited to, genetic algorithm, particle swarm optimization algorithm, whale algorithm and corresponding improved algorithms.

[0041] Substitute the corrected average carbon emission factor of thermal power in the correction area and the carbon loss coefficient per unit distance of transmission into the electric carbon coupling model, and recalculate the corresponding carbon emissions. If the relative error between the carbon emissions and the actual carbon emissions is no more than 5%, save the corrected parameters and update the electric carbon map synchronously. If it is greater than 5%, expand the historical data sample size (from the last 30 days to the last 60 days) and repeat the fitting and optimization process until the relative error is no more than 5%.

[0042] In this embodiment, the electricity-carbon graph is updated regularly based on the sampling interval (15 minutes). Carbon emissions are recalculated based on the updated electricity data. The corresponding data on the electricity-carbon graph is updated based on the updated electricity data and carbon emissions, along with the power color of the electricity layer, the height of the bar / line chart of the carbon layer, and the historical data query results of the interactive layers. When abnormal data triggers an update, the regular data queue is skipped, and a priority channel is activated to ensure data replacement is completed within 3 seconds. If the fluctuation rate between the updated data and the previous data exceeds 30%, the data is automatically rolled back to the previous timeframe, and a data verification alarm is triggered and transmitted to maintenance personnel.

[0043] Example 2 Embodiment 2 of the present invention provides a novel system for constructing a single electrical carbon map of a power system, comprising: The carbon emission data acquisition module collects real-time data on renewable energy output, power grid flow, load, energy storage side charge status, and carbon emission, and performs preprocessing. The carbon emission accounting module establishes an electric carbon coupling model for source-load-storage and carbon emissions based on the preprocessed collected data, and calculates the carbon emission reduction of wind and solar power, the carbon emission of user electricity consumption, and the carbon emission of charging. At each time point, it calculates the changes in the line power flow fluctuation rate and the network loss rate of the transmission line. When the line power flow fluctuation rate is greater than the predetermined power flow fluctuation threshold or the network loss rate change is greater than the predetermined network loss change threshold, it corrects the carbon loss coefficient per unit distance of the transmission line and updates the carbon loss of the transmission link. The "Electricity Carbon Map Construction Module" constructs an electricity carbon map based on carbon emission data obtained from S2 and collected electricity data. The electricity carbon data correction module calculates the power fluctuation rate of the source-grid-load-storage system at each sampling time. When the power fluctuation rate exceeds the predetermined power fluctuation threshold and the annual carbon quota exceeds the quota warning threshold, it indicates data abnormality, and the abnormal data is sent back to the electricity carbon map for display. When the predetermined correction cycle is reached, the relative error between the carbon emissions output by the source-load-storage electricity carbon coupling model and the actual carbon emissions is calculated. When the relative error exceeds the predetermined error threshold, the average carbon emission factor of thermal power in the region and the carbon loss coefficient per unit distance of transmission are corrected, and the electricity carbon map is updated.

[0044] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0045] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0046] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0047] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0048] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A novel method for constructing a single carbon map of a power system, characterized in that, include: S1. Real-time acquisition and preprocessing of new energy output data, power grid flow data, load data, energy storage side charge status and carbon emission data; S2. Based on the preprocessed collected data, establish the source-load-storage and carbon emission coupling models respectively, and calculate the carbon emission reduction of wind and solar power, carbon emission of user electricity consumption, and carbon emission of charging. At each moment, the line power flow fluctuation rate and network loss rate change of the transmission line are calculated. When the line power flow fluctuation rate is greater than the predetermined power flow fluctuation threshold or the network loss rate change is greater than the predetermined network loss change threshold, the carbon loss coefficient per unit distance of transmission is corrected and the carbon loss of the transmission link is updated. S3. Based on the carbon emission data obtained in S2 and the collected electricity data, construct a single graph of electricity and carbon emissions. S4. At each sampling time, calculate the power fluctuation rate of the source-grid-load-storage system. When the power fluctuation rate is greater than the predetermined power fluctuation threshold and the annual carbon quota exceeds the quota warning threshold, it indicates that the data is abnormal. The abnormal data is then sent back to the electricity-carbon graph and displayed. When the predetermined correction period is reached, calculate the relative error between the carbon emissions output by the source-load-storage electricity-carbon coupling model and the actual carbon emissions. When the relative error is greater than the predetermined error threshold, correct the regional thermal power average carbon emission factor and the transmission unit distance carbon loss coefficient, and update the electricity-carbon graph.

2. The method for constructing a novel single-map electrical carbon diagram of a power system according to claim 1, characterized in that: In S1, for carbon emission data, the monthly total regional carbon emissions are divided by the monthly total regional power generation to obtain the regional carbon emission factor for the corresponding month, and then the regional carbon emission factor for each sampling time is obtained through linear interpolation. The initial carbon emission value of the power generation link is calculated by multiplying the power generation by the collected carbon emission factor of thermal power. The initial carbon loss value of the transmission link is obtained by multiplying the collected power flow of the power grid by the transmission distance and temporary losses. The temporary loss is taken as the historical average coefficient of the regional power grid. The initial carbon emission value of the power consumption link is obtained by multiplying the electricity consumption by the regional carbon emission factor.

3. The method for constructing a novel single-map electrical carbon diagram of a power system according to claim 1, characterized in that: The process for calculating carbon emission reductions from wind and solar power in S2 is as follows: At each sampling time, the carbon emission factor of all thermal power units in the region is multiplied by the power generation of the corresponding thermal power unit and summed as the numerator, and the power generation of all thermal power units in the region is summed as the denominator to obtain the average carbon emission factor of thermal power in the region at the corresponding sampling time. The wind and solar power output at the sampling time is multiplied by the sampling interval and the corresponding average carbon emission factor of thermal power in the region to obtain the wind and solar carbon emission reduction at the corresponding sampling time.

4. The method for constructing a novel single-map electrical carbon diagram of a power system according to claim 1, characterized in that: The process for calculating user electricity carbon emissions in S2 is as follows: At each sampling time, the carbon emission factors of different types of power sources are multiplied by the power supplied to the load by the corresponding power source and summed as the numerator. The total power supplied to the load by the power source is used as the denominator to obtain the mixed carbon emission factor of the power source at the corresponding sampling time. The load power at the sampling time is multiplied by the sampling interval duration and the corresponding mixed carbon emission factor of the power source in sequence to obtain the user's electricity consumption carbon emissions at the corresponding sampling time.

5. The method for constructing a novel single-map electrical carbon diagram of a power system according to claim 1, characterized in that: The process for calculating carbon emissions from charging in S2 is as follows: At each sampling time, the carbon emission factors of different types of generating units are multiplied by the power generation of the corresponding generating units and summed to obtain the numerator. The power generation of all generating units is summed to obtain the real-time carbon emission factor of the power grid at the corresponding sampling time. The charging power of the energy storage device at the sampling time is multiplied by the sampling interval and the corresponding real-time carbon emission factor of the power grid to obtain the charging carbon emission at the corresponding sampling time.

6. The method for constructing a novel single-map electrical carbon diagram of a power system according to claim 1, characterized in that: The formula for calculating the corrected carbon loss coefficient per unit distance in S2 is as follows: ； In the formula, Let be the carbon loss coefficient per unit distance of power transmission at sampling time t; Marginal carbon emission factor; Let t be the network loss rate at sampling time t; This refers to the line length; Let t be the ambient temperature difference at sampling time t; This refers to the temperature sensitivity coefficient. The regional carbon emission factor at sampling time t; This is the penalty factor for the squared term of the network loss rate.

7. A method for constructing a novel single-map electrical carbon diagram of a power system according to claim 1 or 6, characterized in that: The process of updating carbon losses in the power transmission link in S2 is as follows: Based on the carbon loss coefficient per unit distance of power transmission at the current sampling time, and combined with the corresponding power flow, calculate the carbon loss of the power transmission link at the current time: ； In the formula, Let t be the carbon loss of the power transmission link at sampling time t, i.e., the carbon loss of the power transmission link. Let t be the power flow of the line at sampling time t; This is the amplification factor, used to control the intensity of nonlinear effects under high loads; This refers to the rated power of the line. To amplify the index.

8. The method for constructing a novel single-map electrical carbon diagram of a power system according to claim 1, characterized in that: The process for correcting the regional average carbon emission factor of thermal power and the carbon loss coefficient per unit distance of transmission in S4 is as follows: For each thermal power unit, the carbon emission offset is obtained by multiplying the unit's power generation by the corresponding carbon emission factor and then subtracting the corresponding actual carbon emissions from the transmission link as the numerator, and using the standard deviation of the actual carbon emissions of all thermal power units as the denominator. If the carbon emission offset is not greater than a predetermined offset threshold, the carbon emission offset is squared and divided by 2 to obtain the transmission carbon emission difference item. If the carbon emission offset is greater than the predetermined offset threshold, the carbon emission offset is subtracted by half of the predetermined offset threshold and then multiplied by the predetermined offset threshold to obtain the power generation carbon emission difference item. Based on the difference in carbon emissions from power generation and the regional average carbon emission factor of thermal power, an objective function for optimizing carbon emissions from thermal power is constructed. Based on the carbon loss in the transmission links of each transmission line and the actual carbon loss in the transmission links, calculate the carbon loss offset and carbon loss volatility; based on the carbon loss offset, the carbon loss coefficient per unit distance of transmission, and the carbon loss volatility, construct an objective function for optimizing the carbon loss coefficient. The optimal regional average carbon emission factor for thermal power and the carbon loss coefficient per unit distance of power transmission are obtained through optimization algorithms and used as the corrected parameters.

9. The method for constructing a novel single-map electrical carbon diagram of a power system according to claim 8, characterized in that: The formula for constructing the objective function for optimizing carbon emissions from thermal power plants is as follows: ； In the formula, This represents the average carbon emission factor for regional thermal power generation. and These represent its upper and lower limits, respectively; For the carbon emission difference item of power generation; This is a priori estimate of the carbon emission factor; t represents the regional average carbon emission factor of thermal power plants at sampling time t; K represents the number of sampling points selected before the current time. , and These represent the weighting coefficients for the power generation carbon emission difference term, the prior carbon emission factor difference term, and the time-series carbon emission factor difference term, respectively.

10. The method for constructing a novel single-map electrical carbon diagram of a power system according to claim 1, characterized in that: The formula for constructing the objective function for optimizing carbon emissions from thermal power plants is as follows: ； In the formula, M represents the total number of transmission lines; The adaptive weights for the l-th transmission line are: This is due to carbon loss offset. The standard deviation of carbon loss offset for all transmission lines; This is a priori estimate of the carbon loss coefficient per unit distance in power transmission; This is an indicator function used to penalize the distribution of carbon loss volatility, representing the carbon loss volatility. The value is 1 if it is less than 0, otherwise it is 0; This is a penalty coefficient used to adjust the penalty for carbon loss volatility; and These are the weighting coefficients for the second and third terms, respectively.

11. A novel system for constructing a single carbon map of a power system, using the method described in any one of claims 1 to 10, characterized in that, include: The carbon emission data acquisition module collects real-time data on renewable energy output, power grid flow, load, energy storage side charge status, and carbon emission, and performs preprocessing. The carbon emission accounting module, based on the preprocessed collected data, establishes an electric carbon coupling model for source-load-storage and carbon emissions, respectively, and calculates the carbon emission reduction of wind and solar power, the carbon emission of user electricity consumption, and the carbon emission of charging. At each moment, the line power flow fluctuation rate and network loss rate change of the transmission line are calculated. When the line power flow fluctuation rate is greater than the predetermined power flow fluctuation threshold or the network loss rate change is greater than the predetermined network loss change threshold, the carbon loss coefficient per unit distance of transmission is corrected and the carbon loss of the transmission link is updated. The "Electricity Carbon Map Construction Module" constructs an electricity carbon map based on carbon emission data obtained from S2 and collected electricity data. The electricity carbon data correction module calculates the power fluctuation rate of the source-grid-load-storage system at each sampling time. When the power fluctuation rate exceeds the predetermined power fluctuation threshold and the annual carbon quota exceeds the quota warning threshold, it indicates data abnormality, and the abnormal data is sent back to the electricity carbon map for display. When the predetermined correction cycle is reached, the relative error between the carbon emissions output by the source-load-storage electricity carbon coupling model and the actual carbon emissions is calculated. When the relative error exceeds the predetermined error threshold, the average carbon emission factor of thermal power in the region and the carbon loss coefficient per unit distance of transmission are corrected, and the electricity carbon map is updated.

12. A terminal, comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the method according to any one of claims 1-10.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method according to any one of claims 1-10.

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