A carbon emission intensity evaluation method and device based on dynamic region division, a terminal device, and a storage medium

Abstract

The application discloses a carbon emission intensity evaluation method and device based on dynamic area division, a terminal equipment and a storage medium, relates to the technical field of power grid analysis, and comprises the following steps: acquiring real-time operation data of a power grid and a winning amount of a new subject, wherein the real-time operation data comprises a node impedance matrix and a time sequence of net power injection of each node; calculating an electrical distance according to the node impedance matrix; calculating a power fluctuation similarity according to the time sequence of net power injection; constructing a comprehensive similarity matrix according to the electrical distance and the power fluctuation similarity, then constructing a Laplacian matrix and obtaining an eigenvector; dividing a sub-region through clustering; for each sub-region, determining a regional carbon emission factor according to the real-time operation data and the winning amount, and evaluating the carbon emission intensity. Through implementation of the application, the problem that the existing carbon emission factor calculation is limited by administrative regions and ignores the physical correlation of the electrical distance and power flow of the power grid can be solved, and the accuracy of carbon accounting results is improved.

Description

Technical Field

[0001] This invention relates to the field of power grid analysis technology, and in particular to a method, apparatus, terminal equipment, and storage medium for assessing carbon emission intensity based on dynamic regional division. Background Technology

[0002] Driven by the "dual carbon" goals, the power industry, as a key sector for achieving carbon neutrality, ensures energy supply while simultaneously generating substantial carbon emissions during energy consumption. Therefore, accurately assessing the carbon emission status of the power industry is a crucial prerequisite for achieving these goals. Currently, the carbon emission factor, as a key indicator for measuring the carbon emission intensity in the power production process, directly determines the reliability of carbon emission assessment results through its accurate calculation.

[0003] In existing methods for calculating the carbon emission factor of power grids, the fixed regional average emission data method is usually adopted. This method uses administrative regions as accounting units. By statistically analyzing the total carbon emissions and total power generation of all power generation entities (mainly traditional power sources such as thermal power plants and hydropower plants) within a certain period, the average carbon emission factor of the region is calculated and used as a fixed value. This value is then applied to the carbon accounting work of all power production and consumption links in the region throughout the entire accounting period.

[0004] However, this fixed-area average emissions data method uses administrative boundaries to delineate the physical connections between electrical distances and power flows in the power grid. It separates electrically strongly coupled nodes and binds weakly coupled nodes, which fails to truly reflect the actual flow of electricity and carbon emission responsibility between different regions and nodes, ultimately leading to distorted carbon accounting results. Summary of the Invention

[0005] This invention provides a method, apparatus, terminal equipment, and storage medium for assessing carbon emission intensity based on dynamic regional division. It can solve the problem that existing carbon emission factor calculations use administrative regions as boundaries and ignore the physical correlation between electrical distance and power flow in the power grid, thereby improving the accuracy of carbon accounting results.

[0006] An embodiment of the present invention provides a method for assessing carbon emission intensity based on dynamic regional division, comprising: Acquire real-time operation data of the power grid and the winning bids for electricity by new entities; real-time operation data includes: node impedance matrix and net power injection time series of each node; Calculate the electrical distance between nodes in the power grid based on the node impedance matrix; Calculate the power fluctuation similarity among nodes in the power grid based on the net power injection time series of each node; A comprehensive similarity matrix is ​​constructed based on the electrical distance and power fluctuation similarity between all nodes; Based on the comprehensive similarity matrix, a Laplacian matrix is ​​constructed, and the eigenvectors of the Laplacian matrix are calculated. Clustering of feature vectors determines the sub-region to which each node of the power grid belongs; For each sub-region, the regional carbon emission factor is determined based on real-time operational data and the winning bid electricity volume of the new entities. The carbon emission intensity of the corresponding sub-region in the power grid is assessed based on the regional carbon emission factor of each sub-region.

[0007] Furthermore, after obtaining real-time operation data of the power grid and the winning bid volume of the new entities, it also includes: Data cleaning and time alignment are performed on the real-time running data to obtain preprocessed real-time running data.

[0008] Furthermore, the node impedance matrix includes: the self-impedance of each node and the mutual impedance between nodes; Calculate the electrical distances between nodes in the power grid based on the node impedance matrix, including: For each node pair in the power grid, the self-impedance of each node in the node pair is summed, and then twice the mutual impedance between the nodes in the node pair is subtracted to obtain the calculation result; a node pair consists of any two nodes in the power grid. The absolute value of the calculation result is taken to obtain the electrical distance between the nodes in the node pair.

[0009] Furthermore, based on the net power injection time series of each node, the power fluctuation similarity among the nodes of the power grid is calculated, including: For each node pair in the power grid, calculate the covariance between the net power injection time series of each node in the node pair, and calculate the standard deviation of the net power injection time series of each node in the node pair respectively. Divide the covariance by the product of the standard deviations of the nodes in the node pair to obtain the power fluctuation similarity between the nodes in the node pair.

[0010] Furthermore, based on the electrical distance and power fluctuation similarity among all nodes, a comprehensive similarity matrix is ​​constructed, including: For each node pair in the power grid, the electrical distance between the nodes in the node pair is transformed by a Gaussian kernel function according to the preset scale parameters to obtain the corresponding electrical similarity. Multiply the electrical similarity with the power fluctuation similarity between nodes in the corresponding node pair to obtain the comprehensive similarity between nodes in the corresponding node pair; The comprehensive similarity matrix is ​​obtained by combining the overall similarity between all nodes in all node pairs.

[0011] Furthermore, real-time operational data also includes: the power generation of traditional generating units and the total power consumption of all new entities on the grid; the new entities include: electric vehicles and distributed energy storage charging; Based on real-time operational data and the winning bids for electricity by new entities, the regional carbon emission factor is determined, including: For each traditional unit, the direct carbon emissions of the traditional unit are determined based on the unit's power generation and the preset carbon emission intensity coefficient. The total direct emissions of all conventional units are obtained by summing up the direct carbon emissions of all conventional units. Obtain the regional carbon emission factor for the previous time period; The total indirect emissions of all new entities are determined based on the total electricity consumption of the grid of all new entities and the regional carbon emission factor of the previous period. The total indirect emissions of all new entities are weighted by the winning bid volume of the new entities to determine the indirect emissions of each new entity. Obtain the net input power of the sub-region; The total power supply of the sub-region is obtained by summing the power generation of all traditional units in the sub-region and adding it to the net power input. The total direct emissions of all traditional units are summed with the total indirect emissions of all new types of units to obtain the total carbon emissions of the sub-region. The ratio of total carbon emissions to total electricity generation in a sub-region is used as the regional carbon emission factor for that sub-region.

[0012] Furthermore, after assessing the carbon emission intensity of the corresponding sub-region in the power grid based on the regional carbon emission factor of each sub-region, the process also includes: Acquire the various power interaction behaviors and the power consumption of the interactive subjects; the interactive subjects include: traditional units or new subjects; Based on the various electricity interaction behaviors of the interactive subject and the amount of electricity interacted, calculate the carbon responsibility generated by the corresponding electricity interaction behavior of the interactive subject; The total net carbon footprint is obtained by algebraically summing the carbon responsibility generated by all electricity interaction behaviors of the interactive subject. Generate a corresponding carbon footprint report based on the total net carbon footprint of the interacting entity.

[0013] Based on the above method embodiments, the present invention provides corresponding device embodiments, including: a data acquisition module, an electrical distance calculation module, a power fluctuation calculation module, a similarity matrix construction module, a spectral feature decomposition module, a clustering and partitioning module, a regional carbon emission factor calculation module, and a carbon emission assessment module; The data acquisition module is used to acquire real-time operating data of the power grid and the winning bids of the new entities; the real-time operating data includes: node impedance matrix and net power injection time series of each node; The electrical distance calculation module is used to calculate the electrical distance between nodes in the power grid based on the node impedance matrix. The power fluctuation calculation module is used to calculate the power fluctuation similarity between nodes in the power grid based on the net power injection time series of each node. The similarity matrix construction module is used to construct a comprehensive similarity matrix based on the electrical distance and power fluctuation similarity between all nodes; The spectral eigenvalue decomposition module is used to construct the Laplacian matrix based on the comprehensive similarity matrix and calculate the eigenvectors of the Laplacian matrix. The clustering and partitioning module is used to cluster feature vectors to determine the sub-region to which each node of the power grid belongs; The regional carbon emission factor calculation module is used to determine the regional carbon emission factor for each sub-region based on real-time operating data and the winning bid electricity of the new entity. The carbon emission assessment module is used to assess the carbon emission intensity of the corresponding sub-region in the power grid based on the regional carbon emission factor of each sub-region.

[0014] Based on the above method embodiments, the present invention provides a corresponding terminal device embodiment, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the steps of the carbon emission intensity assessment method based on dynamic region division as described in the present invention.

[0015] Based on the above method embodiments, the present invention provides a corresponding computer-readable storage medium embodiment, including: a stored computer program, which, when the computer program is running, controls the device where the computer-readable storage medium is located to perform the steps of the carbon emission intensity assessment method based on dynamic region division as described in the present invention.

[0016] Compared with the prior art, the beneficial effects of this embodiment are as follows: This invention acquires real-time operational data of the power grid and the winning bids for electricity by novel entities. The real-time operational data includes node impedance matrices and net power injection time series for each node. The node impedance matrix reflects the physical topology of the power grid, while the net power injection time series reflects its dynamic operational characteristics. Based on the node impedance matrix, the electrical distance between nodes is calculated, quantifying the physical connection tightness between nodes. Based on the net power injection time series for each node, the power fluctuation similarity between nodes is calculated, quantifying the coordinated change law of power flow between nodes. Based on the electrical distance and power fluctuation similarity between all nodes, a comprehensive similarity matrix is ​​constructed, achieving a fusion modeling of the power grid's physical topology and dynamic operational characteristics. Based on the comprehensive similarity matrix, a Laplace matrix is ​​constructed, and its eigenvectors are calculated. Clustering of the eigenvectors determines the sub-regions to which each node belongs, ensuring that the divided sub-regions perfectly match the electrical coupling relationships and power flow laws of the power system. For each sub-region, a regional carbon emission factor is determined based on the real-time operational data and the winning bids for electricity by novel entities. Finally, based on the regional carbon emission factors of each sub-region, the carbon emission intensity of the corresponding sub-region in the power grid is assessed.

[0017] In summary, this invention quantifies the similarity between electrical distance and power fluctuations, constructs a comprehensive similarity matrix, and uses a spectral clustering algorithm to dynamically divide the power grid into sub-regions. This makes carbon emission accounting more aligned with the electrical coupling relationship and power flow patterns of the power grid, thus solving the problem that existing carbon emission factor calculations use administrative regions as boundaries and ignore the physical correlation between electrical distance and power flow in the power grid, thereby improving the accuracy of carbon accounting results. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating a carbon emission intensity assessment method based on dynamic region division provided in an embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of a carbon emission intensity assessment device based on dynamic region division provided in an embodiment of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] In the description of this invention, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated.

[0021] like Figure 1 As shown, in order to address the problem that existing carbon emission factor calculations use administrative regions as boundaries and ignore the physical correlation between grid electrical distance and power flow, an embodiment of the present invention provides a carbon emission intensity assessment method based on dynamic regional division. This method includes at least the following steps: Step S1: Obtain real-time operation data of the power grid and the winning bid amount of the new entity; real-time operation data includes: node impedance matrix and net power injection time series of each node; In a preferred embodiment, the node impedance matrix includes: the self-impedance of each node and the mutual impedance between nodes; In a preferred embodiment, the real-time operating data further includes: the power generation of conventional generating units and the total power consumption of all new entities on the grid; the new entities include: electric vehicles and distributed energy storage charging. For step S1, real-time operational data of the target power grid is collected from the real-time data acquisition and monitoring control (SCADA) module of the dispatch center, specifically including power grid topology data, operational characteristic data, and power production and consumption data; among which, power grid topology data, such as the node impedance matrix, is collected. Node impedance matrix This includes the self-impedance of each node and the mutual impedance between any two nodes, used to characterize the electrical connection characteristics between power grid nodes; operational characteristic data, such as the net power injection time series of each node. This is used to reflect the dynamic changes in power at various nodes of the power grid; electricity production and consumption data, such as the power generation of traditional generating units. Total electricity consumption of the power grid of all new entities wait.

[0022] In this embodiment, traditional generating units include power generation units such as thermal power, coal power, and gas power; while new types of entities include terminal entities that are not "self-generated and self-consumed" distributed resources such as wind power, photovoltaic power, electric vehicles, and distributed energy storage charging.

[0023] It should be noted that for areas or nodes not connected to the dispatch center, in order to ensure the comprehensiveness of data collection, data can be collected from the substation automation system and then uploaded through the edge nodes designated by the dispatch center to achieve real-time operation data collection across the entire power grid.

[0024] In addition to the real-time operational data mentioned above, the results of the electricity spot market clearing are also obtained through the electricity interaction platform. Specifically, this includes the winning bid volume, winning bid price, interaction period, injection node information, and extraction node information of the new entities. Among these, the injection node information corresponds to the access node where the new entities output electricity to the grid, and the extraction node information corresponds to the access node where the new entities obtain electricity from the grid.

[0025] In a preferred embodiment, after acquiring the real-time operating data of the power grid and the winning bid amount of the new entity, the method further includes: Data cleaning and time alignment are performed on the real-time running data to obtain preprocessed real-time running data.

[0026] In one embodiment of the present invention, in order to ensure the accuracy of subsequent analysis, the real-time running data is preprocessed, including data cleaning and time alignment.

[0027] Specifically, data cleaning is mainly used to handle missing, outlier, and jump values ​​in the data. Linear interpolation is used to fill in short-term missing unit output data to ensure the continuity of the data sequence; simultaneously, thresholding methods (such as...) are employed. The standard deviation (10 ...

[0028] Time alignment uses a time window aggregation method to unify all data onto the same computational time scale. It takes the average or cumulative value of data within a certain period to ensure that different types of data, such as unit output, load, market-bid electricity volume, and electric vehicle charging power, are comparable on the same time segment, providing a unified data foundation for subsequent dynamic spatiotemporal calculations.

[0029] After the above data cleaning and time alignment processes, preprocessed real-time operating data can be obtained, providing high-quality data support for subsequent power grid node analysis, carbon emission factor calculation, and other processes.

[0030] Step S2: Calculate the electrical distance between nodes in the power grid based on the node impedance matrix; In a preferred embodiment, calculating the electrical distance between nodes of the power grid based on the node impedance matrix includes: For each node pair in the power grid, the self-impedance of each node in the node pair is summed, and then twice the mutual impedance between the nodes in the node pair is subtracted to obtain the calculation result; a node pair consists of any two nodes in the power grid. The absolute value of the calculation result is taken to obtain the electrical distance between the nodes in the node pair.

[0031] For step S2, based on the node impedance matrix obtained in step S1 or the preprocessed node impedance matrix, the electrical distance between each node of the power grid is quantitatively calculated, thereby characterizing the degree of electrical correlation between nodes. The specific calculation process is as follows: Based on the node impedance matrix, the electrical distance between nodes in the power grid can be calculated using the following formula: ; in, Represents a node With nodes Electrical distance between them and It is the self-impedance in the node impedance matrix, used to characterize the electrical impedance of a node to ground. Represents a node Self-impedance, Represents a node Self-impedance, Represents a node With nodes The mutual impedance between nodes is used to characterize the transfer impedance between nodes. and These are the node indices in the power grid, and they satisfy... , , This represents the number of nodes in the power grid.

[0032] In the above formula, the term refers to a node pair consisting of any two nodes in the power grid. First, extract the nodes from the nodal impedance matrix. self-impedance and nodes self-impedance Then sum them up and subtract the nodes. With nodes mutual impedance between Double the result, and finally, take the absolute value of the result; the resulting value is the node. With nodes electrical distance between .

[0033] After calculating the electrical distance between all node pairs in the power grid using the above formula, a dimension is formed. electrical distance matrix Electrical distance matrix Each element The magnitude of the value directly reflects the tightness of the electrical connection between the corresponding node pairs. The smaller the value, the more likely it is to be a node. With nodes The closer the electrical connection between them, the lower the power transmission loss, and the more likely they are to belong to the same power supply balance zone.

[0034] Step S3: Calculate the power fluctuation similarity between nodes in the power grid based on the net power injection time series of each node; In a preferred embodiment, the power fluctuation similarity between nodes in the power grid is calculated based on the net power injection time series of each node, including: For each node pair in the power grid, calculate the covariance between the net power injection time series of each node in the node pair, and calculate the standard deviation of the net power injection time series of each node in the node pair respectively. Divide the covariance by the product of the standard deviations of the nodes in the node pair to obtain the power fluctuation similarity between the nodes in the node pair.

[0035] For step S3, the net power injection time series of each node is based on the preprocessed data from step S1. The power fluctuation similarity between nodes in the power grid is quantified to characterize the degree of correlation between power changes between nodes. The specific calculation process is as follows: Based on the net power injection time series of each node, the power fluctuation similarity among the nodes of the power grid is calculated using the following formula: ; in, Represents a node With nodes Similarity of power fluctuations between them Represents a node Net power injection time series, Represents a node Net power injection time series, Represents a node Net power injection time series With nodes Net power injection time series The covariance between the two nodes is used to characterize the degree of linear correlation between the net power injection sequences of the two nodes. Represents a node Net power injection time series standard deviation Represents a node Net power injection time series Standard deviation, standard deviation and This is used to characterize the fluctuation amplitude of the net power injection sequence at each node.

[0036] In the above formula, the term refers to a node pair consisting of any two nodes in the power grid. First, compute the nodes. Net power injection time series With nodes Net power injection time series Covariance between and calculate respectively Standard deviation and Standard deviation Subsequently, the covariance Divide by the product of two standard deviations The result is the node. With nodes power fluctuation similarity between .

[0037] After calculating the power fluctuation similarity of all node pairs in the power grid using the above formula, a dimension is formed. Power fluctuation similarity matrix Power fluctuation similarity matrix Each element The magnitude of the value directly reflects the degree of correlation in power changes between corresponding node pairs. The range of values ​​is The closer the absolute value is to 1, the stronger the synchronicity or correlation of the net power injection behavior of the two nodes in the time dimension, and the more likely they are dominated by the same source load fluctuation factors (such as changes in solar irradiance leading to a simultaneous increase in photovoltaic output).

[0038] Step S4: Construct a comprehensive similarity matrix based on the electrical distance and power fluctuation similarity between all nodes; In a preferred embodiment, a comprehensive similarity matrix is ​​constructed based on the electrical distance and power fluctuation similarity among all nodes, including: For each node pair in the power grid, the electrical distance between the nodes in the node pair is transformed by a Gaussian kernel function according to the preset scale parameters to obtain the corresponding electrical similarity. Multiply the electrical similarity with the power fluctuation similarity between nodes in the corresponding node pair to obtain the comprehensive similarity between nodes in the corresponding node pair; The comprehensive similarity matrix is ​​obtained by combining the overall similarity between all nodes in all node pairs.

[0039] For step S4, based on the electrical distance calculated in step S2 and the power fluctuation similarity calculated in step S3, a comprehensive similarity matrix is ​​constructed to achieve a multi-dimensional quantitative representation of the correlation between power grid nodes. The specific construction process is as follows: First, based on the electrical distance and power fluctuation similarity among all nodes, the overall similarity is determined using the following formula: ; in, Represents a node With nodes The overall similarity between them This indicates that the electrical distance has been transformed using a Gaussian kernel function to convert it into a metric of electrical similarity, with a value range of 0 to 1. This represents the scaling parameter, used to control the rate at which the overall similarity decays.

[0040] In the above formula, the term refers to a node pair consisting of any two nodes in the power grid. According to the pre-set scale parameters Electrical distance between two nodes in a node pair A Gaussian kernel function transformation is performed to convert the distance into electrical similarity, thus achieving a mapping from distance to similarity. This ensures that node pairs with smaller electrical distances have higher electrical similarities. Subsequently, the electrical similarity is compared with the power fluctuation similarity of the corresponding node pairs. Multiplying them together, the result is the node. With nodes Overall similarity between .

[0041] After calculating the comprehensive similarity of all node pairs in the power grid using the above formula, the comprehensive similarity of all node pairs is combined to obtain the dimension. Comprehensive similarity matrix .

[0042] In the comprehensive similarity matrix In this process, two nodes are only considered highly similar if they are electrically connected and their power fluctuations are synchronized, ensuring that the final sub-region is not only electrically integrated but also that its carbon characteristics change in a consistent manner.

[0043] Step S5: Based on the comprehensive similarity matrix, construct the Laplacian matrix and calculate the eigenvectors of the Laplacian matrix; For step S5, after constructing the comprehensive similarity matrix, dynamic partitioning of sub-regions is achieved through spectral clustering. First, based on the comprehensive similarity matrix... Construct the Laplace matrix The specific formula is as follows: ; in, Represents the Laplace matrix, The degree matrix is ​​a diagonal matrix, and the elements on the diagonal are... Equal to the first in the comprehensive similarity matrix The sum of all elements in the row, i.e. This construction process transforms the comprehensive similarity relationship between nodes into a Laplace structure of the graph, which can effectively characterize the association strength and topological properties of nodes in the power grid.

[0044] Subsequently, the Laplace matrix Perform eigenvalue decomposition and calculate the Laplacian matrix. forward The eigenvectors corresponding to the smallest eigenvalues ​​are combined into a single eigenvector. A dimensional feature matrix.

[0045] The Laplacian matrix is ​​decomposed and its features extracted as described above. The feature vector corresponding to the smallest feature value maps the high-dimensional node relationships to a low-dimensional feature space. This not only preserves the core relationship information between nodes but also reduces the computational complexity of subsequent clustering, providing a compact and discriminative feature representation for sub-region partitioning.

[0046] Step S6: Cluster the feature vectors to determine the sub-region to which each node of the power grid belongs; For step S6, after obtaining the feature matrix, traditional clustering algorithms such as K-Means are used to cluster the row vectors of the feature matrix. Each row vector corresponds to the representation of a power grid node in the low-dimensional feature space. The clustering algorithm divides the nodes into groups based on the similarity of these vectors. Each node is assigned a different cluster, and each cluster corresponds to a sub-region. This allows the output of the clustering label for each node, clearly indicating the sub-region to which it belongs.

[0047] By clustering the row vectors of the feature matrix as described above, the low-dimensional features obtained from spectral decomposition are transformed into intuitive sub-region division results, so that not only are the electrical connections tight and the power fluctuations synchronized within the sub-regions, but the changes in their carbon characteristics are also consistent.

[0048] Meanwhile, by setting trigger cycles or conditions, such as every hour, or when the total system load changes beyond a certain threshold, the entire process from data acquisition to spectral clustering can be re-executed to achieve dynamic updates of sub-regions and ensure that the division results can dynamically change with changes in new energy output, load migration, and network topology adjustments.

[0049] Step S7: For each sub-region, determine the regional carbon emission factor based on real-time operating data and the winning bid electricity of the new entity; In a preferred embodiment, the regional carbon emission factor is determined based on real-time operational data and the winning bid electricity volume of the novel entity, including: For each traditional unit, the direct carbon emissions of the traditional unit are determined based on the unit's power generation and the preset carbon emission intensity coefficient. The total direct emissions of all conventional units are obtained by summing up the direct carbon emissions of all conventional units. Obtain the regional carbon emission factor for the previous time period; The total indirect emissions of all new entities are determined based on the total electricity consumption of the grid of all new entities and the regional carbon emission factor of the previous period. The total indirect emissions of all new entities are weighted by the winning bid volume of the new entities to determine the indirect emissions of each new entity. Obtain the net input power of the sub-region; The total power supply of the sub-region is obtained by summing the power generation of all traditional units in the sub-region and adding it to the net power input. The total direct emissions of all traditional units are summed with the total indirect emissions of all new types of units to obtain the total carbon emissions of the sub-region. The ratio of total carbon emissions to total electricity generation in a sub-region is used as the regional carbon emission factor for that sub-region.

[0050] For step S7, after completing the dynamic division of the power grid sub-regions, for each sub-region... Based on the preprocessed real-time operating data and the winning bid electricity of the new entities, the regional carbon emission factor of the sub-region is calculated to achieve a quantitative characterization of the carbon emission characteristics of each sub-region. The specific calculation process is as follows: First, for each traditional unit Based on the power generation of traditional generating units and a pre-set carbon emission intensity coefficient, the direct carbon emissions of traditional generating units are calculated using the following formula: ; in, Indicates traditional units exist Direct carbon emissions over a given period of time. Indicates traditional units exist Electricity generation during a given period, in units of , Indicates traditional units The corresponding carbon emission intensity coefficient, in units of This coefficient is usually a constant determined in advance through actual measurement or standard value based on factors such as fuel type, power generation technology, unit efficiency, and whether emission reduction facilities are installed.

[0051] Subsequently, the same sub-region Direct carbon emissions of all traditional units in China Summing these values ​​yields the total direct emissions of all conventional units. .

[0052] It should be noted that traditional methods only calculate direct emissions from the power generation side and treat new entities as having zero carbon responsibility, which leads to significant errors in systems with a high proportion of renewable energy and new loads. This invention considers the energy consumption of new entities as a contribution to the carbon emissions of the power grid, thus requiring them to bear corresponding indirect emission responsibilities.

[0053] To calculate the indirect emissions of new actors, the regional carbon emission factor for the previous time period was obtained. ; It represents the overall carbon emission intensity of the sub-region in the previous calculation period, providing a traceable carbon emission intensity benchmark for the electricity consumption behavior of new entities in the current period.

[0054] Based on the total electricity consumption of all new actors in the power grid and the regional carbon emission factor of the previous period, the total indirect emissions of all new actors are calculated using the following formula: ; in, Subregion exist Total indirect emissions of all new entities during the period Subregion exist The total electricity consumption of all new entities on the power grid during the period. Subregion exist Regional carbon emission factors during a given period.

[0055] By multiplying the total electricity consumption of the aforementioned new entities by the regional carbon emission factor of the previous period, the electricity consumption behavior of the new entities is correlated with the carbon emission intensity of the power grid in the previous period. This enables reasonable tracing of the indirect emissions of the new entities and makes up for the shortcomings of traditional methods that ignore the carbon responsibility of the new entities.

[0056] It should be noted that if the total indirect emissions of all new entities in a sub-region are directly allocated equally according to the number of new entities, without considering the actual participation behavior, electricity consumption scale and impact on the power grid of each new entity in the electricity market, the carbon responsibility allocation will be disconnected from the actual contribution of the entity to the power grid, and the differentiated impact of different entities will not be reflected. It will also cause the carbon emission accounting results to deviate from the actual operating state of the power grid.

[0057] To avoid the inaccuracies caused by the simple average allocation mentioned above, this invention introduces the winning bid electricity volume of the new entity as the basis for weight allocation, directly linking electricity interaction behavior with carbon responsibility, so as to more accurately reflect the actual impact and participation of the new entity on the power grid.

[0058] Specifically, this applies to new types of entities participating in electricity interaction. Obtain sub-regions from the power interaction platform exist Total winning bids for all new entities in power interaction during the period For one of the new types of entities , obtain its in Electricity volume won in the period .

[0059] For new types of entities that do not participate in power interaction For example, charging piles under residential photovoltaic systems or small-scale industrial and commercial energy storage that are not connected to the aggregation platform, based on their own power consumption. As equivalent liability.

[0060] Therefore, the total equivalent liability electricity of the new entity is: ; in, This represents the total equivalent liability electricity of all new entities within the sub-region. Representing a new type of subject exist The winning bid volume for the specified time period Representing a new type of subject exist Power consumption during a given time period.

[0061] For new types of entities participating in power interaction Its weight is: ; For new types of entities that do not participate in power interaction Its weight is: ; in, This represents a new type of entity participating in power interaction. The weight, This refers to a new type of entity that does not participate in power interaction. The weight.

[0062] Based on this, for each new type of subject Through new types of entities The weight of the total indirect emissions of all new entities. By weighting, a new type of entity is obtained. The indirect emissions are calculated using the following formula: ; in, Representing a new type of subject Indirect emissions, Representing a new type of subject The weight. It should be noted that the new type of entity Including new types of entities participating in power interaction Or all new types of entities that do not participate in power interaction .

[0063] This invention weights the total indirect emissions according to the equivalent electricity responsibility weight of each new entity, making the carbon responsibility allocation more in line with the actual operating state of the power grid.

[0064] After completing the weighted allocation of indirect emissions from new entities, the sub-regions are obtained. exist Net input electricity during the period Net input power represents the net amount of electricity input from the external power grid to a sub-region, which can be directly obtained from power grid flow data.

[0065] Next, for sub-regions All traditional units in China Traditional generator unit power generation Sum the results and then add them to the net input electricity. The total power supply of the sub-region is obtained by adding them together, as shown in the following formula: ; in, Subregion exist Total power supply during the time period.

[0066] At the same time, the total direct emissions of all traditional units Total indirect emissions of all new entities Summing these values ​​yields the total carbon emissions for the sub-region, using the following formula: ; in, Subregion exist Total carbon emissions over a given period.

[0067] Finally, the total carbon emissions of the sub-regions Total power supply of sub-regions The ratio of these values ​​is used as the regional carbon emission factor for the sub-region, i.e.: ; in, Subregion exist Regional carbon emission factors over a given period.

[0068] It should be noted that the calculated... Regional carbon emission factors during a given period It will be stored for calculating the next time period. The new benchmark factor for indirect emissions ensures that the calculation of carbon emission factors forms a dynamic closed-loop process, which can respond in real time to changes in the system's operating status, so that carbon emission accounting always fits the actual operating status of the power grid.

[0069] Step S8: Assess the carbon emission intensity of the corresponding sub-region in the power grid based on the regional carbon emission factor of each sub-region.

[0070] For step S8, based on each sub-region Regional carbon emission factors The carbon emission intensity level of each sub-region is quantified by comparing benchmark thresholds, thereby assessing the carbon emission intensity of the corresponding sub-region in the power grid.

[0071] Specifically, a low-carbon benchmark value and a high-carbon benchmark value are set in advance, with the low-carbon benchmark value being lower than the high-carbon benchmark value.

[0072] For each sub-region Sub-region Regional carbon emission factors Compared with low-carbon benchmarks, if the regional carbon emission factor If the value is less than or equal to the low-carbon benchmark value, then the sub-region is determined. It is classified as a low carbon emission intensity level; If regional carbon emission factors If it exceeds the low-carbon benchmark value, then the sub-region will be further... Regional carbon emission factors Compared with high-carbon benchmarks, the regional carbon emission factor When the value is greater than the high carbon baseline, determine the sub-region. For high carbon emission intensity levels, in the regional carbon emission factor When the value is less than or equal to the high carbon benchmark value, determine the sub-region. It is classified as a medium carbon emission intensity level.

[0073] Through the above-mentioned classification and determination, regional carbon emission factors are transformed into intuitive carbon intensity level conclusions, accurately identifying the carbon emission characteristics and emission reduction potential of different sub-regions, and providing a feasible quantitative basis for the formulation of differentiated carbon regulation strategies for power grids, optimization of power supply structure, and operation management of new entities.

[0074] This invention uses spectral clustering with electrical distance and power fluctuation similarity between power grid nodes as feature vectors to divide the power grid into multiple dynamic sub-regions with similar "electricity-carbon" characteristics, accurately capturing the spatial heterogeneity and time-varying characteristics of carbon emission factors. Furthermore, by using the carbon emission factors of the previous period to calculate the indirect emissions of new entities, and immediately updating the current factors after the calculation, the method is used for the calculation of the next period, forming a dynamic closed-loop feedback system, which enables the method to respond in real time to sudden changes in the system's operating state.

[0075] Furthermore, by linking the behavior of the novel subject to carbon emissions, this invention fairly reflects the actual impact of its electrical interaction behavior on the system's carbon emissions, thereby improving the accuracy of carbon emission factor calculation.

[0076] In a preferred embodiment, after assessing the carbon emission intensity of the corresponding sub-region in the power grid based on the regional carbon emission factor of each sub-region, the method further includes: Acquire the various power interaction behaviors and the power consumption of the interactive subjects; the interactive subjects include: traditional units or new subjects; Based on the various electricity interaction behaviors of the interactive subject and the amount of electricity interacted, calculate the carbon responsibility generated by the corresponding electricity interaction behavior of the interactive subject; The total net carbon footprint is obtained by algebraically summing the carbon responsibility generated by all electricity interaction behaviors of the interactive subject. Generate a corresponding carbon footprint report based on the total net carbon footprint of the interacting entity.

[0077] In one embodiment of the present invention, in order to realize the implementation and traceable management of carbon responsibility from the regional level to the subject level, the present invention further determines the carbon footprint of the interacting subject and generates a carbon footprint report, providing a feasible quantitative support for differentiated carbon regulation and optimization of low-carbon operation of the power grid.

[0078] Specifically, firstly, the interaction of various electrical energy interactions by the interactive entities during the operation of the power grid is obtained, including the behavior of injecting electrical energy into the power grid and the behavior of extracting electrical energy from the power grid, and the interactive electrical energy corresponding to each electrical energy interaction behavior is obtained simultaneously. The interactive entities include traditional generating units and new entities.

[0079] Subsequently, based on the various electricity interaction behaviors of the interactive subject and the amount of electricity interacted, the carbon responsibility generated by the corresponding electricity interaction behavior of the interactive subject is calculated, as follows: ; in, Indicates the first Carbon responsibility arising from individual electricity interactions Indicates the direction of responsibility, used to distinguish carbon responsibility for electricity purchases and sales. When the interactive entity increases its carbon emissions by consuming electricity from the power grid, that is, by drawing electricity from the power grid, when... At this time, it represents that the interacting entity reduces the system's carbon emissions by providing electricity to the grid, that is, injecting electricity into the grid. Indicates the first The amount of electricity generated by each interaction behavior. Indicates the first The regional carbon emission factor matched by each electricity interaction behavior.

[0080] Next, the carbon responsibility generated by all electricity interaction behaviors of the interactive subject is algebraically summed, and the positive and negative carbon responsibilities are comprehensively superimposed to obtain the total net carbon footprint, thereby realizing the overall quantification of the carbon responsibility of the interactive subject.

[0081] Finally, based on the pre-set standardized report template, the total net carbon footprint of the interacting entity is filled in. At the same time, a detailed list of each electricity interaction behavior is integrated, including the interaction time, the amount of electricity interacted, the matching carbon emission factor, the carbon responsibility calculation value of each behavior, and the carbon footprint summary data by region and time period. A carbon footprint report in a unified format is generated, which presents the composition and change pattern of the carbon footprint of the interacting entity. It also provides a traceable and verifiable quantitative basis for the formulation of grid carbon emission reduction strategies, the assessment of the entity's carbon responsibility, and the optimization of power operation.

[0082] Preferably, visualization methods such as charts and tables are used to present core information such as total net carbon footprint, carbon footprint distribution by region, and carbon footprint change trends by time period in an intuitive way. For example, bar charts can be used to show the carbon footprint percentage of different sub-regions, and line charts can be used to show the carbon footprint changes at different time periods.

[0083] like Figure 2 As shown, based on the above method embodiments, corresponding apparatus embodiments are provided; An embodiment of the present invention provides a carbon emission intensity assessment device based on dynamic regional division, comprising: a data acquisition module, an electrical distance calculation module, a power fluctuation calculation module, a similarity matrix construction module, a spectral feature decomposition module, a clustering and partitioning module, a regional carbon emission factor calculation module, and a carbon emission assessment module; The data acquisition module is used to acquire real-time operating data of the power grid and the winning bids of the new entities; the real-time operating data includes: node impedance matrix and net power injection time series of each node; The electrical distance calculation module is used to calculate the electrical distance between nodes in the power grid based on the node impedance matrix. The power fluctuation calculation module is used to calculate the power fluctuation similarity between nodes in the power grid based on the net power injection time series of each node. The similarity matrix construction module is used to construct a comprehensive similarity matrix based on the electrical distance and power fluctuation similarity between all nodes; The spectral eigenvalue decomposition module is used to construct the Laplacian matrix based on the comprehensive similarity matrix and calculate the eigenvectors of the Laplacian matrix. The clustering and partitioning module is used to cluster feature vectors to determine the sub-region to which each node of the power grid belongs; The regional carbon emission factor calculation module is used to determine the regional carbon emission factor for each sub-region based on real-time operating data and the winning bid electricity of the new entity. The carbon emission assessment module is used to assess the carbon emission intensity of the corresponding sub-region in the power grid based on the regional carbon emission factor of each sub-region.

[0084] In a preferred embodiment, the carbon emission intensity assessment device based on dynamic regional division further includes: a data preprocessing module; The data preprocessing module is used to clean and align the real-time running data to obtain preprocessed real-time running data.

[0085] In a preferred embodiment, the node impedance matrix includes: the self-impedance of each node and the mutual impedance between nodes; The electrical distance calculation module includes: an electrical impedance calculation unit and an absolute value calculation unit; The electrical impedance calculation unit is used to sum the self-impedances of each node in the node pair for each node pair in the power grid, and then subtract twice the mutual impedance between the nodes in the node pair to obtain the calculation result; a node pair consists of any two nodes in the power grid. The absolute value calculation unit is used to take the absolute value of the calculation result to obtain the electrical distance between each node in the node pair.

[0086] In a preferred embodiment, the power fluctuation calculation module includes: a sequence statistical parameter calculation unit and a fluctuation similarity calculation unit; The sequence statistics parameter calculation unit is used to calculate the covariance between the net power injection time series of each node in the node pair for each node pair of the power grid, and to calculate the standard deviation of the net power injection time series of each node in the node pair respectively. The fluctuation similarity calculation unit is used to divide the covariance by the product of the standard deviations of each node in the node pair, and use it as the power fluctuation similarity between each node in the node pair.

[0087] In a preferred embodiment, the similarity matrix construction module includes: a Gaussian kernel transformation unit, a comprehensive similarity calculation unit, and a comprehensive similarity combination unit; The Gaussian kernel transform unit is used to perform Gaussian kernel function transformation on the electrical distance between each node pair in the power grid according to preset scale parameters, so as to obtain the corresponding electrical similarity. The comprehensive similarity calculation unit is used to multiply the electrical similarity with the power fluctuation similarity between each node in the corresponding node pair to obtain the comprehensive similarity between each node in the corresponding node pair. The comprehensive similarity combination unit is used to combine the comprehensive similarity between nodes in all node pairs to obtain a comprehensive similarity matrix.

[0088] In a preferred embodiment, the real-time operating data further includes: the power generation of conventional generating units and the total power consumption of all new entities on the grid; the new entities include: electric vehicles and distributed energy storage charging. The regional carbon emission factor calculation module determines the regional carbon emission factor based on real-time operational data and the winning bid electricity volume of new entities, including: For each traditional unit, the direct carbon emissions of the traditional unit are determined based on the unit's power generation and the preset carbon emission intensity coefficient. The total direct emissions of all conventional units are obtained by summing up the direct carbon emissions of all conventional units. Obtain the regional carbon emission factor for the previous time period; The total indirect emissions of all new entities are determined based on the total electricity consumption of the grid of all new entities and the regional carbon emission factor of the previous period. The total indirect emissions of all new entities are weighted by the winning bid volume of the new entities to determine the indirect emissions of each new entity. Obtain the net input power of the sub-region; The total power supply of the sub-region is obtained by summing the power generation of all traditional units in the sub-region and adding it to the net power input. The total direct emissions of all traditional units are summed with the total indirect emissions of all new types of units to obtain the total carbon emissions of the sub-region. The ratio of total carbon emissions to total electricity generation in a sub-region is used as the regional carbon emission factor for that sub-region.

[0089] In a preferred embodiment, the carbon emission intensity assessment device based on dynamic regional division further includes: a report generation module; The report generation module also includes: an interactive information acquisition unit, a carbon responsibility calculation unit, a total net carbon footprint calculation unit, and a carbon footprint report generation unit; The interactive information acquisition unit is used to acquire the various power interaction behaviors and the power consumption of the interactive subject; the interactive subject includes: traditional units or new subjects; The carbon responsibility calculation unit is used to calculate the carbon responsibility generated by the corresponding electricity interaction behavior of the interactive subject based on the electricity interaction behavior and the electricity amount of the interaction. The total net carbon footprint calculation unit is used to algebraically sum the carbon responsibility generated by all electricity interaction behaviors of the interactive subject to obtain the total net carbon footprint. The carbon footprint report generation unit is used to generate a corresponding carbon footprint report based on the total net carbon footprint of the interacting subject.

[0090] It is understood that the above-described device embodiments correspond to the method embodiments of the present invention, and can implement the carbon emission intensity assessment method based on dynamic region division provided by any of the above-described method embodiments of the present invention.

[0091] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0092] Based on the above embodiments of the carbon emission intensity assessment method based on dynamic region division, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the carbon emission intensity assessment method based on dynamic region division of any embodiment of the present invention.

[0093] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0094] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0095] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting various parts of the terminal device via various interfaces and lines.

[0096] Based on the above-described method embodiments, another embodiment is provided: another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the carbon emission intensity assessment method based on dynamic region division as described in any of the above-described method embodiments of the present invention.

[0097] The modules / units integrated into the carbon emission intensity assessment device / terminal equipment based on dynamic region division, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0098] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for evaluating carbon emission intensity based on dynamic region division, characterized in that, include: Obtain real-time operation data of the power grid and the winning bid volume of new entities; The real-time operating data includes: node impedance matrix and net power injection time series of each node; Calculate the electrical distance between nodes of the power grid based on the node impedance matrix. Based on the net power injection time series of each node, calculate the power fluctuation similarity between the nodes of the power grid; A comprehensive similarity matrix is ​​constructed based on the electrical distance and power fluctuation similarity between all nodes; Based on the comprehensive similarity matrix, a Laplacian matrix is ​​constructed, and the eigenvectors of the Laplacian matrix are calculated. Cluster the feature vectors to determine the sub-region to which each node of the power grid belongs; For each sub-region, the regional carbon emission factor is determined based on real-time operational data and the winning bid electricity volume of the new entities. The carbon emission intensity of the corresponding sub-region in the power grid is assessed based on the regional carbon emission factor of each sub-region. 2.The carbon intensity evaluation method based on dynamic region division according to claim 1, characterized in that, After obtaining real-time operation data of the power grid and the winning bid volume of the new entities, it also includes: Data cleaning and time alignment are performed on the real-time running data to obtain preprocessed real-time running data. 3.The carbon intensity evaluation method based on dynamic region division according to claim 1, characterized in that, The node impedance matrix includes: the self impedance of each node and the mutual impedance between nodes; The step of calculating the electrical distance between nodes of the power grid based on the node impedance matrix includes: For each node pair in the power grid, the self-impedance of each node in the node pair is summed, and then twice the mutual impedance between the nodes in the node pair is subtracted to obtain the calculation result; the node pair consists of any two nodes in the power grid. The absolute value of the calculation result is taken to obtain the electrical distance between each node in the node pair.

4. The method for evaluating carbon intensity based on dynamic region division according to claim 3, characterized in that, Based on the net power injection time series of each node, the power fluctuation similarity among the nodes of the power grid is calculated, including: For each node pair in the power grid, calculate the covariance between the net power injection time series of each node in the node pair, and calculate the standard deviation of the net power injection time series of each node in the node pair respectively. The covariance is divided by the product of the standard deviations of each node in the node pair to obtain the power fluctuation similarity between the nodes in the node pair.

5. The carbon emission intensity assessment method based on dynamic regional division according to claim 4, characterized in that, Based on the electrical distance and power fluctuation similarity among all nodes, a comprehensive similarity matrix is ​​constructed, including: For each node pair in the power grid, the electrical distance between the nodes in the node pair is transformed by a Gaussian kernel function according to the preset scale parameters to obtain the corresponding electrical similarity. Multiply the electrical similarity by the power fluctuation similarity between the nodes in the corresponding node pair to obtain the comprehensive similarity between the nodes in the corresponding node pair; The comprehensive similarity matrix is ​​obtained by combining the overall similarity between all nodes in all node pairs.

6. The carbon emission intensity assessment method based on dynamic regional division according to claim 1, characterized in that, The real-time operating data also includes: the power generation of traditional generating units and the total power consumption of all new entities in the power grid; the new entities include: electric vehicles and distributed energy storage charging. The determination of regional carbon emission factors based on real-time operational data and the winning bid electricity volume of the new entities includes: For each traditional unit, the direct carbon emissions of the traditional unit are determined based on the unit's power generation and the preset carbon emission intensity coefficient. The total direct emissions of all conventional units are obtained by summing up the direct carbon emissions of all conventional units. Obtain the regional carbon emission factor for the previous time period; The total indirect emissions of all new entities are determined based on the total electricity consumption of the grid of all new entities and the regional carbon emission factor of the previous period. The total indirect emissions of all new entities are weighted by the winning bid volume of the new entities to determine the indirect emissions of each new entity. Obtain the net input power of the sub-region; The total power supply of the sub-region is obtained by summing the power generation of all traditional units in the sub-region and adding it to the net input power. The total direct emissions of all traditional units are summed with the total indirect emissions of all new types of units to obtain the total carbon emissions of the sub-region. The ratio of total carbon emissions to total electricity generation in a sub-region is used as the regional carbon emission factor for that sub-region.

7. The carbon emission intensity assessment method based on dynamic regional division according to claim 1, characterized in that, After assessing the carbon emission intensity of the corresponding sub-region in the power grid based on the regional carbon emission factor of each sub-region, the following is also included: The interaction behavior of each power consumption of the interactive subject and the power consumption of the interaction are obtained; the interactive subject includes: traditional unit or new subject; Based on the various electricity interaction behaviors of the interactive subject and the amount of electricity interacted, calculate the carbon responsibility generated by the corresponding electricity interaction behavior of the interactive subject; The total net carbon footprint is obtained by algebraically summing the carbon responsibility generated by all electricity interaction behaviors of the interactive subject. Generate a corresponding carbon footprint report based on the total net carbon footprint of the interacting entity.

8. A carbon emission intensity assessment device based on dynamic regional division, characterized in that, include: The system includes modules for data acquisition, electrical distance calculation, power fluctuation calculation, similarity matrix construction, spectral feature decomposition, clustering and partitioning, regional carbon emission factor calculation, and carbon emission assessment. The data acquisition module is used to acquire real-time operating data of the power grid and the winning bid volume of the new entity; The real-time operating data includes: node impedance matrix and net power injection time series of each node; The electrical distance calculation module is used to calculate the electrical distance between nodes of the power grid based on the node impedance matrix. The power fluctuation calculation module is used to calculate the power fluctuation similarity between nodes of the power grid based on the net power injection time series of each node. The similarity matrix construction module is used to construct a comprehensive similarity matrix based on the electrical distance and power fluctuation similarity between all nodes; The spectral feature decomposition module is used to construct a Laplacian matrix based on the comprehensive similarity matrix and to calculate the eigenvectors of the Laplacian matrix. The clustering and partitioning module is used to cluster the feature vectors to determine the sub-region to which each node of the power grid belongs; The regional carbon emission factor calculation module is used to determine the regional carbon emission factor for each sub-region based on real-time operating data and the winning bid electricity of the new entity. The carbon emission assessment module is used to assess the carbon emission intensity of the corresponding sub-region in the power grid based on the regional carbon emission factor of each sub-region.

9. A terminal device, characterized in that, The system includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the carbon emission intensity assessment method based on dynamic zoning as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device on which the computer-readable storage medium is located to perform the carbon emission intensity assessment method based on dynamic zoning as described in any one of claims 1-7.

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