A method and device for constructing a high spatial resolution distribution map of electrical carbon factors

Abstract

The application provides a high spatial resolution distribution map construction method and device of an electric carbon factor, relates to the field of new energy power generation dynamic monitoring, and comprises the following steps: analyzing the influence of wind and light injection power on the total carbon flow rate of a system according to a system topology structure and system operation parameters, obtaining a branch power flow injection matrix and a unit injection distribution matrix; determining system node carbon potential according to the branch power flow injection matrix, the unit injection distribution matrix and pre-calculated thermal power generation carbon emission intensity; and inserting the system node carbon potential into a layer after substation position fuzzy processing to obtain a high spatial resolution distribution map of the electric carbon factor. The application can calculate the hourly resolution carbon emission intensity of a source side thermal power unit by using the material balance method, obtain the power carbon emission factor of substations at different voltage levels in combination with power flow tracking based on the target regional network structure, further combine the coverage range of substations at different voltage levels, and draw a distribution map of the electric carbon factor by using a spatial allocation technology.

Description

Technical Field

[0001] This application relates to the field of dynamic monitoring of new energy power generation, specifically a method and apparatus for constructing a high spatial resolution distribution map of the carbon factor. Background Technology

[0002] Carbon emissions from purchased electricity during the production process constitute a significant portion of corporate carbon emissions. The carbon factor for electricity is the fundamental basis for calculating carbon emissions from purchased electricity, and its coverage and time-varying characteristics are crucial to the accuracy of the calculation results. Currently, based on real-time grid topology and power generation and transmission data, the temporal resolution of the carbon factor can be improved to the hourly level, while the spatial resolution remains at the provincial level. Therefore, effectively improving the spatial resolution of the carbon factor is of great significance.

[0003] Existing technologies include electric carbon factor generation techniques based on power flow. Specifically, these techniques are based on the regional power grid architecture, power output, substations, and power flow generation. The power distribution matrix is ​​obtained by considering the diversion coefficients of all paths from the starting node to the end node of the power flow generated by the power flow at the starting node, combined with the power injected into the starting node by the corresponding generating units. Then, the carbon potential (i.e., electric carbon factor) of the grid nodes is calculated based on the carbon emission intensity of each power source.

[0004] Existing technologies also include technologies for generating carbon emission factors based on electricity input and output. Specifically, this involves using the sum of the total electricity generation in a region during a certain period and the net electricity input to that region as the denominator, and the sum of the carbon emissions from local thermal power generation and the carbon emissions from electricity generated from exporting regions during that period as the numerator, to calculate the carbon dioxide emission factor of electricity in that region during that period.

[0005] However, among the aforementioned existing technologies, the electric carbon factor generation technology based on power flow is highly dependent on the network architecture. When refined to the distribution network level, the number of nodes increases dramatically, putting pressure on power flow calculations. Furthermore, the disclosure of the network structure can pose risks to grid security. Electric carbon factors based on power input and output are calculated from statistical data, which is often released on an annual scale and has a 1-2 year lag. Therefore, the temporal precision does not meet the needs of refined carbon emission accounting. Spatially, it remains at the provincial level, and the spatial precision also does not meet the needs of refined carbon emission accounting.

[0006] This section is intended to provide background or context for the embodiments of the invention set forth in the claims. The description herein is not an admission that it is prior art simply because it is included in this section. Summary of the Invention

[0007] To address the problems in the existing technology, this application provides a method and apparatus for constructing a high spatial resolution distribution map of the electric carbon factor. It can obtain the electric carbon emission factor of substations at different voltage levels by calculating the hourly resolution carbon emission intensity of thermal power units on the source side through the material balance method, combined with power flow tracing based on the grid structure of the target area, and further combine the coverage of substations at different voltage levels to draw a distribution map of the electric carbon factor using spatial allocation technology.

[0008] To solve the above-mentioned technical problems, this application provides the following technical solution: In a first aspect, this application provides a method for constructing a high spatial resolution distribution map of the electric carbon factor, including: Based on the system topology and power grid system operating parameters, the impact of wind and solar power injection on the total carbon flow rate of the power grid system is analyzed, and the branch power flow injection matrix and the unit injection distribution matrix are obtained. The system node carbon potential is determined based on the branch power flow injection matrix, the unit injection distribution matrix, and the pre-calculated carbon emission intensity of thermal power generation. By inserting the carbon potential of the system nodes into the layer after the substation location has been blurred, a high spatial resolution distribution map of the electric carbon factor is obtained.

[0009] Furthermore, the power grid system operating parameters include the load of each node, the output of generators, resistance parameters, and reactance parameters; the analysis of the impact of wind and solar power injection on the total carbon flow rate of the power grid system based on the system topology and power grid system operating parameters, yielding the branch power flow injection matrix and the unit injection distribution matrix, includes: The total carbon flow rate of the power grid system considering wind and solar power injection is calculated based on the load, generator output, resistance parameters, and reactance parameters of each node. The active power and reactive power of the line are determined based on the load, generator output, resistance parameters and reactance parameters of each node. The branch power flow injection matrix and the generator injection distribution matrix are constructed based on the active power and reactive power of the line. The total carbon flow rate of the power grid system is used to verify the branch power flow injection matrix and the unit injection distribution matrix.

[0010] Furthermore, the calculation of the total carbon flux of the power grid system considering wind and solar injection based on the load, generator output, resistance parameters, and reactance parameters of each node includes: A model relating wind and solar power injection to the total carbon flow rate of the power grid system is constructed based on the load, generator output, resistance parameters, and reactance parameters of each node. The total carbon flow rate of the power grid system is calculated based on the wind and solar power injection power and the relationship model.

[0011] Furthermore, the steps for pre-calculating the carbon emission intensity of thermal power generation include: Carbon emission performance during combustion is determined based on the carbon content of coal, carbon oxidation rate, carbon capture rate, molar mass of carbon and carbon dioxide, and fuel consumption per kilowatt-hour. Determine carbon oxidation carbon emission performance based on carbon oxidation rate; The carbon emission performance during the desulfurization process is determined based on the medium-level electricity fuel consumption, sulfur content of coal, and desulfurization efficiency. The carbon emission intensity of the thermal power generation is determined based on the carbon emission performance during the combustion process, the carbon emission performance during carbon oxidation, and the carbon emission performance during the desulfurization process.

[0012] Further, determining the system node carbon potential based on the branch power flow injection matrix, the unit injection distribution matrix, and the pre-calculated carbon emission intensity of thermal power generation includes: Calculate the branch power flow transpose matrix corresponding to the branch power flow distribution matrix and the unit injection transpose matrix corresponding to the unit injection distribution matrix, respectively. The system node carbon potential is determined based on the branch power flow transpose matrix, the unit injection transpose matrix, and the unit's thermal power generation carbon emission intensity.

[0013] Further, the step of inserting the carbon potential of the system nodes into the layer after substation location fuzzing to obtain a high spatial resolution distribution map of the electric carbon factor includes: Cluster analysis of the substations is performed based on the obtained geographical coordinates of the substations and the system topology. The location of the substation is blurred based on the cluster analysis results; The carbon potential of the system nodes is interpolated to the power supply range of the clustered substation after fuzzy processing to obtain a high spatial resolution distribution map of the electric carbon factor.

[0014] Secondly, this application provides an apparatus for constructing a high spatial resolution distribution map of the electrocarbon factor, comprising: The matrix generation unit is used to analyze the impact of wind and solar power injection on the total carbon flow rate of the power grid system based on the system topology and power grid system operating parameters, and to obtain the branch power flow injection matrix and the unit injection distribution matrix. The node carbon potential determination unit is used to determine the system node carbon potential based on the branch power flow injection matrix, the unit injection distribution matrix, and the pre-calculated carbon emission intensity of thermal power generation. The distribution map generation unit is used to insert the carbon potential of the system nodes into the layer after the substation location has been blurred to obtain a high spatial resolution distribution map of the electric carbon factor.

[0015] Furthermore, the power grid system operating parameters include the load of each node, the output of the generator, resistance parameters, and reactance parameters; the matrix generation unit includes: The total carbon flow rate determination module is used to calculate the total carbon flow rate of the power grid system considering wind and solar power injection based on the load of each node, the output of the generator, the resistance parameters and the reactance parameters. The line power determination module is used to determine the active power and reactive power of the line based on the load of each node, the output of the generator, the resistance parameters and the reactance parameters. The matrix generation module is used to construct the branch power flow injection matrix and the unit injection distribution matrix based on the active power and reactive power of the line. The matrix verification module is used to verify the branch power flow injection matrix and the unit injection distribution matrix using the total carbon flow rate of the power grid system.

[0016] Furthermore, the total carbon flow rate determination module includes: The relational model generation module is used to construct a relational model between wind and solar power injection and the total carbon flow rate of the power grid system based on the load, generator output, resistance parameters and reactance parameters of each node. The relationship calculation module is used to calculate the total carbon flow rate of the power grid system based on the wind and solar power injection and the relationship model.

[0017] Furthermore, the node carbon potential determination unit includes: The combustion carbon emission performance determination module is used to determine the carbon emission performance during the combustion process based on the carbon content of coal, carbon oxidation rate, carbon capture rate, molar mass of carbon and carbon dioxide, and fuel consumption per kilowatt-hour. The carbon oxidation performance determination module is used to determine carbon oxidation carbon emission performance based on the carbon oxidation rate. The desulfurization carbon emission performance determination module is used to determine the carbon emission performance of the desulfurization process based on the medium-level electric fuel consumption, coal sulfur content, and desulfurization efficiency. An emission intensity determination module is used to determine the carbon emission intensity of the thermal power generation based on the carbon emission performance during the combustion process, the carbon emission performance during carbon oxidation, and the carbon emission performance during the desulfurization process.

[0018] Furthermore, the node carbon potential determination unit includes: The transpose matrix calculation module is used to calculate the branch power flow transpose matrix corresponding to the branch power flow distribution matrix and the unit injection transpose matrix corresponding to the unit injection distribution matrix, respectively. The transpose matrix generation module is used to determine the system node carbon potential based on the branch power flow transpose matrix, the unit injection transpose matrix, and the unit's thermal power generation carbon emission intensity.

[0019] Furthermore, the distribution map generation unit includes: The clustering analysis module is used to perform clustering analysis on the substations based on the obtained geographical coordinates of the substations and the system topology. The fuzzy processing module is used to perform fuzzy processing on the location of the substation based on the clustering analysis results; The distribution map generation module is used to interpolate the carbon potential of the system nodes to the power supply range of the clustered substation after fuzzing, so as to obtain the high spatial resolution distribution map of the electric carbon factor.

[0020] Thirdly, this application provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method for constructing a high spatial resolution distribution map of the electrocarbon factor.

[0021] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for constructing a high spatial resolution distribution map of the electrocarbon factor.

[0022] Fifthly, this application provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the method for constructing a high spatial resolution distribution map of the electrocarbon factor.

[0023] To address the problems in existing technologies, this application provides a method and apparatus for constructing a high spatial resolution distribution map of electric carbon factors. Based on the determination of hourly electric carbon factors for substations using power flow tracing methods that take into account wind and solar uncertainties, it proposes a method for generating specific user-level, hourly resolution electric carbon factor distribution maps based on grid structures. This method uses a material balance method to calculate the hourly resolution carbon emission intensity of source-side thermal power units, and combines power flow tracing based on the target area's grid structure to obtain the electric carbon emission factors of substations at different voltage levels. Furthermore, it combines the coverage area of ​​substations at different voltage levels and uses spatial allocation techniques to draw the electric carbon factor distribution map. Specifically, it uses power flow analysis to determine the carbon potential of each node in the power grid, then uses cluster analysis to classify substations into various types, and finally uses spatial interpolation and a low-voltage-level priority principle based on coverage area to determine the grid electric carbon factors of the target area. This method can alleviate the computational pressure of simulating electric carbon factors in distribution network substations, generating high spatiotemporal accuracy (time to the hour, space to within 1 kilometer) electric carbon factors on the user side without exposing the regional grid structure, providing accurate factor data for user-side electric carbon emission accounting. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a flowchart of the method for constructing a high spatial resolution distribution map of the electrocarbon factor in the embodiments of this application; Figure 2 This is a flowchart illustrating the branch power flow injection matrix and unit injection distribution matrix obtained in the embodiments of this application; Figure 3 This is a flowchart illustrating the calculation of the total carbon flow rate of the system considering wind and solar energy input in an embodiment of this application. Figure 4 This is a flowchart illustrating the calculation of carbon emission intensity from thermal power generation in an embodiment of this application; Figure 5 This is a flowchart illustrating the determination of system node carbon potential in the embodiments of this application; Figure 6 This is a flowchart illustrating the high spatial resolution distribution map of the electrocarbon factor obtained in the embodiments of this application; Figure 7 This is a structural diagram of the device for constructing a high spatial resolution distribution map of the electrocarbon factor in the embodiments of this application; Figure 8 This is a structural diagram of the matrix generation unit in an embodiment of this application; Figure 9 This is a structural diagram of the total carbon flow rate determination module in an embodiment of this application; Figure 10 This is one of the structural diagrams of the node carbon potential determination unit in the embodiments of this application; Figure 11 This is the second structural diagram of the node carbon potential determination unit in the embodiments of this application; Figure 12 This is a structural diagram of the distribution map generation unit in an embodiment of this application; Figure 13 This is a schematic diagram of the structure of the electronic device in the embodiments of this application; Figure 14 This is a wiring diagram of an IEEE 14-node system in an embodiment of this application; Figure 15 This is a schematic diagram of the IEEE 14 system node electric carbon factor considering the uncertainty of wind and solar power in the embodiments of this application; Figure 16 This is an example diagram of the electrical carbon distribution map of IEEE 14 system nodes in an embodiment of this application. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0027] The information collected in the technical solution of this application is information and data authorized by the user or fully authorized by all parties. The collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data all comply with the relevant laws, regulations and standards of the relevant countries and regions, necessary confidentiality measures have been taken, and they do not violate public order and good morals. Corresponding operation portals are provided for users to choose to authorize or refuse.

[0028] Provide users with corresponding operation entry points, allowing them to choose to agree to or reject the automated decision results; if the user chooses to reject, the process will proceed to the expert decision-making process.

[0029] In one embodiment, see Figure 1 In order to calculate the hourly resolution carbon emission intensity of source-side thermal power units using the material balance method, and to obtain the power carbon emission factor of substations at different voltage levels by combining power flow tracing based on the target area grid structure, and further combine the coverage of substations at different voltage levels to draw a distribution map of the power carbon factor using spatial allocation technology, this application provides a method for constructing a high spatial resolution distribution map of the power carbon factor, including: S101: Based on the system topology and (power grid) system operating parameters, analyze the impact of wind and solar power injection on the total carbon flow rate of the (power grid) system, and obtain the branch power flow injection matrix and the unit injection distribution matrix; S102: Determine the system node carbon potential based on the branch power flow injection matrix, the unit injection distribution matrix, and the pre-calculated carbon emission intensity of thermal power generation; S103: Insert the carbon potential of the system nodes into the layer after the substation location has been blurred to obtain a high spatial resolution distribution map of the electric carbon factor.

[0030] Understandably, the method provided in this application, based on the calculation of hourly-resolution carbon emission intensity of source-side thermal power units using the material balance method, combines power flow tracing based on the grid structure of the target area to obtain the electricity carbon emission factor of substations at different voltage levels. Furthermore, by combining the coverage area of ​​substations at different voltage levels, a 1 km × 1 km electricity carbon factor distribution map is drawn using spatial allocation technology. The hourly-resolution electricity carbon emission factor of a user's location is then located using the user's latitude and longitude information. This provides fundamental data support for high-precision carbon emission accounting on the user side within the target area. See the following embodiments for details.

[0031] Step S1: This invention takes the IEEE 14-node system as an example, see... Figure 14 The system has a total of 5 generators and 20 transmission lines. Among them, G1 is a coal-fired unit with a carbon emission intensity of 0.88; G2 and G4 are gas-fired units with carbon emission intensities of 0.53 and 0.52, respectively; G3 and G5 are wind and hydropower units with a carbon emission intensity of 0.

[0032] The power flow distribution in the system was calculated using the Newton-Lager method, and the distribution of power flow in each branch of the entire system is shown in Table 1. After obtaining the power flow of each branch, the line loss of each branch was calculated. The line loss generated on the transmission line was regarded as a virtual load, and the virtual load was transferred to the end nodes of the line, so that the number of nodes and the topology of the system remained unchanged, reducing the amount of computation.

[0033] Table 1. Branch power flow distribution in the IEEE 14-node system

[0034] Based on the calculation results in Table 1, the injected power of each node is calculated using formula (2) below, and the results are shown in Table 2.

[0035] (2)

[0036] In the formula: m =1,2,···, N e ,in N e Φ represents the total number of nodes in the network. m For nodes m Direct connection to node m A set of nodes that inject active power flow; S m For nodes m The injected complex power; S mn line m - n The above is from the node m To the node n Flowing complex power; S Gm For nodes m The injected complex power of the connected generator.

[0037] Table 2 Calculation results of injected power at each node of the IEEE 14-node system

[0038] Step S2: Based on the power plant test and survey results, using the corresponding formulas below, the carbon emission intensity of coal-fired unit G1 is calculated to be 0.855 kg CO2 / kWh, the carbon emission intensities of gas-fired units G2 and G4 are 0.525 kg CO2 / kWh and 0.520 kg CO2 / kWh respectively, and the carbon emission intensity of clean energy units G3 and G5 is 0.

[0039] Step S3: The node's carbon factor is determined by the carbon flow from generators directly connected to that node and the carbon flow flowing into that node from other upstream nodes. The energy consumption per unit time of each node relative to the carbon emissions on the generation side can be measured by the node's carbon potential. n Carbon potential e n The calculation formula is as follows:

[0040] In the formula, For nodes n Direct connection and to the node n A set of nodes that inject active power flow. For the line mn Carbon flow on For nodes n Carbon flow rate of the generator set For nodes n The injected complex power.

[0041] The nodal carbon factor of each node in this system is calculated based on the carbon emission metering results of each node, branch, and generator set. (See [link to relevant documentation]). Figure 15 As shown.

[0042] Step S4: The coverage range of each grid node is 100 kilometers. Use a spatial interpolation tool to interpolate the node electrocarbon factor to the coverage range. See [link to relevant documentation]. Figure 16 As shown.

[0043] Therefore, this invention can provide users with high spatial resolution carbon factor for their substations while ensuring electricity safety. This invention is compatible with substations of different voltage levels, ensuring the accuracy of user-side electricity carbon emission accounting results.

[0044] As described above, the high spatial resolution distribution map construction method for electric carbon factors provided in this application can, based on the determination of the hourly electric carbon factor of substations using power flow tracing methods that take into account the uncertainties of wind and solar power, propose a method for generating a specific user-hourly resolution electric carbon factor distribution map based on the grid structure. It uses the material balance method to calculate the hourly resolution carbon emission intensity of thermal power units on the source side, and combines power flow tracing based on the grid structure of the target area to obtain the electric carbon emission factors of substations at different voltage levels. Furthermore, it combines the coverage area of ​​substations at different voltage levels and uses spatial allocation technology to draw the electric carbon factor distribution map. Specifically, it uses power flow analysis to determine the carbon potential of each node in the power grid, then uses cluster analysis to classify substations into multiple types, and then uses spatial interpolation and the principle of prioritizing low voltage levels based on coverage area to determine the grid electric carbon factor of the target area. This method can alleviate the computational pressure of electric carbon factor simulation in distribution network substations, and generate high spatiotemporal accuracy (time to the hour, space to within 1 kilometer) electric carbon factors on the user side without exposing the regional grid structure, providing accurate factor data for user-side electric carbon emission accounting.

[0045] In one embodiment, see Figure 2 The system operating parameters include the load of each node, the output of the generator, resistance parameters, and reactance parameters; the analysis of the impact of wind and solar power injection on the total carbon flow rate of the system based on the system topology and operating parameters yields the branch power flow injection matrix and the unit injection distribution matrix, including: S201: Calculate the total carbon flow rate of the system considering wind and solar injection based on the load, generator output, resistance parameters, and reactance parameters of each node; specifically, see... Figure 3 The step of calculating the total carbon flow rate of the system considering wind and solar power injection based on the load of each node, the output of the generator, the resistance parameters and the reactance parameters includes: S301: constructing a relationship model between wind and solar power injection power and the total carbon flow rate of the system based on the load of each node, the output of the generator, the resistance parameters and the reactance parameters; S302: calculating the total carbon flow rate of the system based on the wind and solar power injection power and the relationship model. S202: Determine the active power and reactive power of the line based on the load, generator output, resistance parameters and reactance parameters of each node; S203: Construct the branch power flow injection matrix and the generator injection distribution matrix based on the active power and reactive power of the line; S204: Verify the branch power flow injection matrix and the unit injection distribution matrix using the total carbon flow rate of the system. The total carbon flow rate of the system can be used in conjunction with equations (15) to (17) in subsequent steps to verify the branch power flow injection matrix and the unit injection distribution matrix. That is, if the E and P calculated in equation (16) are... bThe product (approximately) is equal to the product calculated in equation (10). R a If the result is , it means that the branch power flow injection matrix and the unit injection distribution matrix are correct and usable.

[0046] It is understood that the above steps S201 (including steps S301 to S302) to S204 are implemented through the following technical means.

[0047] (1) Construct a power flow distribution matrix that takes into account the uncertainty of wind and solar power.

[0048] 1) Obtain the load (including active load and reactive load), generator active and reactive output, network topology and related technical parameters such as resistance and reactance of each node in the system at the current moment.

[0049] 2) Analyze the impact of wind and solar power input on total carbon flow rate.

[0050] The injected power from wind power is affected by the wind speed at the wind farm. First, we establish the correlation function between wind speed and the total carbon flow rate of the system:

[0051] In the formula, R a The total carbon flow rate of the system. f ( x ) is a function of the total carbon flow rate of the system and the wind speed.

[0052] The relationship between the mechanical power output of a wind turbine and wind speed can be represented by a power curve. Generally, the power curve of a wind turbine can be expressed as an equation of the following form:

[0053] In the formula, P v The electric field wind speed is v The mechanical power of a single wind turbine a It refers to the number of wind turbines in the wind farm. ρ air density, A For rotor area, C p The power coefficient of the wind turbine. To cut into wind speed, Rated wind speed, Rated power, To cut off the wind speed.

[0054] When performing power flow calculations on the system, the system's conventional turbines are known quantities. Assume the wind farm connection node is... j and the balance node is s Considering only active power losses, the active power output of the balancing unit...P Gs It is the sum of the injected power of all other nodes plus the inverse of the total active power loss:

[0055] In the formula: Indicates the system's first i The injected active power of each node; For the first i Active power is injected into the generating units at each node; if no generating unit is connected, then... =0; , For nodes i and j The active load power is 0 if no load is connected. This represents the sum of the total system losses.

[0056] The total carbon emissions generated by all units per unit time are equal to the sum of the total carbon flow rates of the system, that is:

[0057] In the formula, To represent the first k Active power is injected into the units at each node. P Gs To balance the active power output of the generator unit, and These represent the carbon emission intensity of balanced units and conventional units, respectively.

[0058] By simultaneously solving the above equations, a model considering the injection power and total carbon flow rate of the system under wind speed uncertainty can be established as follows:

[0059] Considering that the injected power of the photovoltaic electric field is affected by the photovoltaic intensity, we first establish a correlation function between the irradiance and the total injected carbon flux of the system:

[0060] In the formula, ( r ) represents the relationship between the total carbon flux injected into the system and the light intensity. r Indicates light intensity.

[0061] The relationship between the mechanical power of a single photovoltaic array and the light intensity of the photovoltaic electric field can be represented by a power curve. Generally, the output power of a photovoltaic electric field... P w It can be expressed as an equation of the following form:

[0062] In the formula, b Indicates the number of photovoltaic arrays. r Indicates light intensity. B Indicates the area of ​​the photovoltaic array. For photoelectric conversion efficiency, t 0 represents atmospheric temperature.

[0063] Similar to the analysis of the impact of wind power uncertainties on carbon flow rate, only active power grid losses are considered, assuming that the photovoltaic power plant is connected to the node... m The active power output of the balancing unit is the sum of the power injected by all other nodes plus the inverse of the total active power loss:

[0064] In the formula, For the first i Active power is injected into the generating units at each node; if no generating unit is connected, then... =0; For nodes i The active load power is 0 if no load is connected. For nodes m The active load power is 0 if no load is connected. This represents the sum of the total system losses.

[0065] The total carbon emissions generated by all units per unit time are equal to the sum of the total carbon flow rates of the system, that is:

[0066] By simultaneously applying the above equations, a model considering the uncertainty of light intensity and the relationship between injection power and total carbon flow rate of the system can be established as shown below:

[0067] The specific meanings of the symbols in the formula are explained above.

[0068] Similar to uncertainty analysis of wind power, the total carbon flow rate of the system is related to the injected power of the photovoltaic power plant and the carbon emission intensity of the generator units. Due to the uncertain characteristics of the photovoltaic power plant grid connection system, such as the randomness and intermittency of sunlight intensity, carbon flow calculations can be extended to uncertainty analysis environments.

[0069] 3) The power flow equations are calculated by forward and backward substitution to obtain the power flow distribution at each node.

[0070] In order to track the specific situation of carbon flow transfer in power flow and the production sources of carbon emissions generated by electricity consumption by load-side users, it is necessary to establish a carbon flow distribution correlation analysis and calculation model of branches, nodes, loads and active power losses. Before this, it is necessary to carry out power flow calculations on the system based on a large amount of power grid topology and power flow section data.

[0071] The power flow calculation adopts the forward-backward substitution method. Based on the known power of the tail node, the current of the head branch is obtained by forward substitution. Then, the voltage value at each node is calculated by backward substitution. Finally, the forward-backward substitution is repeated multiple times to meet the voltage constraint conditions.

[0072] The calculation formula is as follows:

[0073] In the formula, Indicates the line ij Active power flowing upstream; Represents a node j The injected active power; Represents nodes j A set of connected nodes; Indicates the line ij Active power loss; Indicates the line ij The reactive power flowing upstream; Represents a node j Injected reactive power; Indicates the line ij Reactive power loss; Represents a node j The voltage; Indicates the line ij The resistance; Indicates the line ij The reactance.

[0074] 4) Construct the branch power flow distribution matrix based on the power flow distribution, and then construct the unit injection distribution matrix to obtain the branch power and the generator power components corresponding to the load of each node.

[0075] The process for constructing the branch power flow distribution matrix is ​​as follows: The number of power grid nodes is N Then the branch power flow distribution matrix is N A square matrix of order, if the nodes i With nodes j There are branches connecting them, assuming from node i To the node j The flowing tide is a meritorious force P ,and Pb ij = P Then it is called a node i To the node j The current that flows through a region is considered a positive current, and vice versa. Pb ji =0, considering there is no power flow within the same node, then Pbii =0.

[0076] The process for constructing the unit injection distribution matrix is ​​as follows: The unit injection distribution matrix is K × N An order matrix, where k This refers to the number of generator sets connected. N This represents the number of nodes in the power grid. If generator sets are connected to nodes... j Let the active power flow of the node connected to the generator unit be... P If a node is not connected to a generator set, the active power flow at that node is 0, denoted as . Pg kj =0.

[0077] As can be seen from the above description, the method for constructing a high spatial resolution distribution map of the electric carbon factor provided in this application can analyze the impact of wind and solar power injection on the total carbon flow rate of the system based on the system topology and system operating parameters, and obtain the branch power flow injection matrix and the unit injection distribution matrix.

[0078] In one embodiment, see Figure 4 The steps for pre-calculating the carbon emission intensity of thermal power generation include: S401: Determine the carbon emission performance during combustion based on the carbon content of coal, carbon oxidation rate, carbon capture rate, molar mass of carbon and carbon dioxide, and fuel consumption per kilowatt-hour. S402: Determine carbon oxidation carbon emission performance based on carbon oxidation rate; S403: Determine the carbon emission performance of the desulfurization process based on the medium-level electricity fuel consumption, coal sulfur content, and desulfurization efficiency. S404: Determine the carbon emission intensity of the thermal power generation based on the carbon emission performance during the combustion process, the carbon emission performance during carbon oxidation, and the carbon emission performance during the desulfurization process.

[0079] Understandably, the process of calculating the real-time carbon emission intensity of thermal power units is as follows: Provided data is available, the carbon emission intensity of thermal power generation with different installed capacities under different production loads is obtained through actual measurements. If actual measurement data is unavailable, the relevant parameters required for calculating the carbon emission intensity of thermal power generation can be obtained by simulating typical process flows of coal-fired and gas-fired power generation. The formulas for calculating the carbon emission intensity of coal-fired and gas-fired power generation are as follows:

[0080] In the formula, E i Expresses carbon emission performance during fuel combustion, in g / kWh; η i , ξi , μ i The units i The carbon content, carbon oxidation rate, and carbon capture rate of the coal; M C and M CO2 Let be the molar masses of carbon and carbon dioxide, respectively, taken as 12 g / mol and 44 g / mol. W i For the unit i Fuel consumption per kilowatt-hour, g / kWh. E For carbon emission performance, g / kWh; n This refers to the number of different types of alkanes contained in natural gas. θ i Alkane i In terms of the proportion of natural gas, % M CO2 The molecular weight of CO2 is taken as 44 g / mol; OF i The carbon oxidation rate is % M i Alkane i Molecular mass, g / mol; E ts Indicates carbon emission performance during the desulfurization process, in g / kWh; T coal This indicates the fuel consumption per unit of electricity generated during the desulfurization process, expressed in g / kWh. W S This indicates the sulfur content of coal, in %; M CO2 Expresses the molar mass of carbon dioxide, in g / mol; M s Represents the molar mass of sulfur, in g / mol; θ TS Indicates desulfurization efficiency, %.

[0081] As can be seen from the above description, the method for constructing a high spatial resolution distribution map of the carbon factor provided in this application can pre-calculate the carbon emission intensity of thermal power generation.

[0082] In one embodiment, see Figure 5 The step of determining the system node carbon potential based on the branch power flow injection matrix, the unit injection distribution matrix, and the pre-calculated carbon emission intensity of thermal power generation includes: S501: Calculate the branch power flow transpose matrix corresponding to the branch power flow distribution matrix and the unit injection transpose matrix corresponding to the unit injection distribution matrix, respectively; S502: Determine the system node carbon potential based on the branch power flow transpose matrix, the unit injection transpose matrix, and the unit's thermal power generation carbon emission intensity.

[0083] Understandably, the process of calculating the active flux and nodal carbon factor for each branch node is as follows: The inflow and outflow of active power at nodes are determined by the power flow operation results of the power grid. Based on the given known conditions, the unit injection matrix and generator carbon emission vector are written. Finally, the data are calculated according to the definition of the node active power flux matrix, and the relationship between system nodes is analyzed to calculate the node carbon potential.

[0084] Based on the active flux matrix of the nodes P n We can obtain:

[0085] In the formula, E n This represents the carbon potential at each node of the power grid; P b Represents the branch power flow distribution matrix; P G Represents the unit injection distribution matrix; E G This indicates the carbon emission intensity of the unit.

[0086] The carbon potential of each node in the power system after processing can be obtained as follows:

[0087] As can be seen from the above description, the method for constructing a high spatial resolution distribution map of the electric carbon factor provided in this application can determine the carbon potential of system nodes based on the branch power flow injection matrix, the unit injection distribution matrix, and the pre-calculated carbon emission intensity of thermal power generation.

[0088] In one embodiment, see Figure 6 The step of inserting the carbon potential of the system nodes into the layer after substation location fuzzing to obtain a high spatial resolution distribution map of the electric carbon factor includes: S601: Perform cluster analysis on the substations based on the obtained geographical coordinates of the substations and the system topology; S602: The location of the substation is fuzzy based on the cluster analysis results; S603: Interpolate the carbon potential of the system nodes to the power supply range of the clustered substation after fuzzy processing to obtain the high spatial resolution distribution map of the electric carbon factor.

[0089] Understandably, the process of drawing the hourly electrocarbon factor distribution map based on the grid structure is as follows: 1) Obtain the coverage area of ​​the grid nodes (substations).

[0090] A. Prioritize obtaining information on the actual coverage area of ​​substations; B. When the above information is unavailable, a dual-weighted function can be constructed by combining the substation's geographical coordinates and power grid topology data to determine the substation's coverage area. The formula is as follows:

[0091] In the formula, d is the geographical distance; r is the electrical distance; α, β, γ, and δ are default parameters, where α=0.65, β=2, γ=0.35, and δ=1.5. By generating a modified Thiessen polygon, the coverage accuracy of the substation power supply range is made >95%.

[0092] 2) Based on hierarchical clustering analysis or the improved K-means algorithm (iteration threshold ε=0.01), and combined with transformer capacity, voltage level and power supply radius, substations are divided into Class I substations (voltage level ≥500 kV), Class II substations (220 kV≤voltage level<500 kV), and Class III substations (voltage level=110 kV).

[0093] 3) The carbon factor of different types of substations is interpolated to the corresponding power supply range using the adaptive weighted spatial interpolation method, and then the layers of the interpolation results of different types of substations are merged.

[0094] A. Using the power supply radius of different types of substations as parameters, interpolate the substation's carbon factor to the covered power supply area. Spatial interpolation methods include, but are not limited to, inverse distance weighted interpolation, Kriging interpolation, triangular network interpolation, nearest neighbor interpolation, spline interpolation, and topographic interpolation. Taking Kriging interpolation as an example: For different types of substations, the Kriging interpolation parameters are automatically optimized and dynamically adjusted by the variogram function γ(h)=C0+C1(1-e^(-h / a)) to make the interpolation accuracy RMSE<0.08.

[0095] In the above variogram, C0 is the nugget effect, representing the spatial variability when the distance is 0; C1 is the sill value, representing the maximum variability when spatial correlation disappears; and a is the range, representing the distance at which spatial correlation reaches the sill value.

[0096] B. Draw the target area as a 1 km × 1 km grid (the grid size can be adjusted according to the area of ​​the target area); C. Layer merging is performed using a low-voltage-level priority principle based on coverage area. Specifically: For overlapping areas of Class I and Class II substations, if the coverage area of ​​Class II substations is ≥50%, the carbon factor of Class II substations is used; otherwise, the carbon factor of the overlapping area is the weighted sum of the carbon factors of the two types of substations. For overlapping areas of Class II and Class III substations, if the coverage area of ​​Class III substations is ≥50%, the carbon factor of Class III substations is used; otherwise, the carbon factor of the overlapping area is the weighted sum of the carbon factors of the two types of substations. For overlapping areas of Class I, Class II, and Class III substations, if the coverage area of ​​Class III substations is ≥30%, the carbon factor of Class III substations is used; otherwise, the carbon factor of the overlapping area is the weighted sum of the carbon factors of the three types of substations.

[0097] 4) Insert geographic information and legend.

[0098] Insert latitude and longitude lines and a legend for the carbon factor distribution map after the contract layer.

[0099] As can be seen from the above description, the method for constructing a high spatial resolution distribution map of the electric carbon factor provided in this application is capable of [the following].

[0100] Based on the same inventive concept, this application also provides an apparatus for constructing a high spatial resolution distribution map of the electrocarbon factor, which can be used to implement the method described in the above embodiments, as described in the following embodiments. Since the principle of the apparatus for constructing a high spatial resolution distribution map of the electrocarbon factor is similar to that of the method for constructing a high spatial resolution distribution map of the electrocarbon factor, the implementation of the apparatus can refer to the implementation of the method based on software performance benchmarks, and will not be repeated. As used below, the terms "unit" or "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the system described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0101] In one embodiment, see Figure 7 In order to calculate the hourly resolution carbon emission intensity of source-side thermal power units using the material balance method, and to obtain the power carbon emission factor of substations at different voltage levels by combining power flow tracing based on the target area grid structure, and further combine the coverage of substations at different voltage levels to draw a distribution map of power carbon factor using spatial allocation technology, this application provides a device for constructing a high spatial resolution distribution map of power carbon factor, including: The matrix generation unit 701 is used to analyze the impact of wind and solar power injection on the total carbon flow rate of the system based on the system topology and system operating parameters, and to obtain the branch power flow injection matrix and the unit injection distribution matrix. The node carbon potential determination unit 702 is used to determine the system node carbon potential based on the branch power flow injection matrix, the unit injection distribution matrix and the pre-calculated carbon emission intensity of thermal power generation. The distribution map generation unit 703 is used to insert the carbon potential of the system nodes into the layer after the substation location has been blurred to obtain a high spatial resolution distribution map of the electric carbon factor.

[0102] In one embodiment, see Figure 8 The system operating parameters include the load of each node, the output of the generator, resistance parameters, and reactance parameters; the matrix generation unit 701 includes: The total carbon flow rate determination module 801 is used to calculate the total carbon flow rate of the system considering wind and solar injection based on the load of each node, the output of the generator, the resistance parameters and the reactance parameters. The line power determination module 802 is used to determine the active power and reactive power of the line based on the load of each node, the output of the generator, the resistance parameters and the reactance parameters. The matrix generation module 803 is used to construct the branch power flow injection matrix and the unit injection distribution matrix based on the active power and reactive power of the line. The matrix verification module 804 is used to verify the branch power flow injection matrix and the unit injection distribution matrix using the total carbon flow rate of the system.

[0103] In one embodiment, see Figure 9 The total carbon flow rate determination module 801 includes: The relational model generation module 901 is used to construct a relational model between wind and solar power injection and the total carbon flow rate of the system based on the load of each node, the output of the generator, the resistance parameters and the reactance parameters. The relationship calculation module 902 is used to calculate the total carbon flow rate of the system based on the wind and solar injection power and the relationship model.

[0104] In one embodiment, see Figure 10 The node carbon potential determination unit 702 includes: The combustion carbon emission performance determination module 1001 is used to determine the carbon emission performance during the combustion process based on the carbon content of coal, carbon oxidation rate, carbon capture rate, molar mass of carbon and carbon dioxide, and fuel consumption per kilowatt-hour. Carbon oxidation performance determination module 1002 is used to determine carbon oxidation carbon emission performance based on carbon oxidation rate; The desulfurization carbon emission performance determination module 1003 is used to determine the carbon emission performance of the desulfurization process based on the medium-level electric fuel consumption, sulfur content of coal and desulfurization efficiency. The emission intensity determination module 1004 is used to determine the carbon emission intensity of the thermal power generation based on the carbon emission performance during the combustion process, the carbon emission performance during carbon oxidation, and the carbon emission performance during the desulfurization process.

[0105] In one embodiment, see Figure 11 The node carbon potential determination unit 702 includes: Transpose matrix calculation module 1101 is used to calculate the branch power flow transpose matrix corresponding to the branch power flow distribution matrix and the unit injection transpose matrix corresponding to the unit injection distribution matrix, respectively. The transpose matrix generation module 1102 is used to determine the system node carbon potential based on the branch power flow transpose matrix, the unit injection transpose matrix, and the thermal power generation carbon emission intensity of the unit.

[0106] In one embodiment, see Figure 12 The distribution map generation unit 703 includes: Clustering analysis module 1201 is used to perform clustering analysis on the substation based on the acquired geographical coordinates of the substation and the system topology. The fuzzy processing module 1202 is used to perform fuzzy processing on the location of the substation based on the clustering analysis results; The distribution map generation module 1203 is used to interpolate the carbon potential of the system nodes to the power supply range of the clustered substation after fuzzing, so as to obtain the high spatial resolution distribution map of the electric carbon factor.

[0107] From a hardware perspective, in order to calculate the hourly resolution carbon emission intensity of source-side thermal power units using the material balance method, and to obtain the electricity carbon emission factor of substations at different voltage levels by combining power flow tracing based on the target area grid structure, and further combine the coverage of substations at different voltage levels to draw an electricity carbon factor distribution map using spatial allocation technology, this application provides an embodiment of an electronic device for implementing all or part of the above-mentioned method for constructing a high spatial resolution distribution map of electricity carbon factors. The electronic device specifically includes the following components: The system comprises a processor, a memory, a communications interface, and a bus; wherein the processor, memory, and communications interface communicate with each other via the bus; the communications interface is used to realize information transmission between the high spatial resolution distribution map construction device for electric carbon factors and core business systems, user terminals, and related databases and other related devices; the logic controller can be a desktop computer, tablet computer, or mobile terminal, etc., and this embodiment is not limited to these. In this embodiment, the logic controller can be implemented with reference to the embodiments of the high spatial resolution distribution map construction method for electric carbon factors and the embodiments of the high spatial resolution distribution map construction device for electric carbon factors in the embodiments, the contents of which are incorporated herein, and repeated details will not be described again.

[0108] It is understood that the user terminal may include smartphones, tablet computers, network set-top boxes, portable computers, desktop computers, personal digital assistants (PDAs), in-vehicle devices, smart wearable devices, etc. Among these, the smart wearable devices may include smart glasses, smartwatches, smart bracelets, etc.

[0109] In practical applications, parts of the method for constructing a high spatial resolution distribution map of the electrocarbon factor can be executed on the electronic device side as described above, or all operations can be completed in the client device. The choice can be made based on the processing power of the client device and the limitations of the user's usage scenario. This application does not impose any limitations on this. If all operations are completed in the client device, the client device may further include a processor.

[0110] The aforementioned client device may have a communication module (i.e., a communication unit) that can communicate with a remote server to achieve data transmission. The server may include a server on the task scheduling center side; in other implementation scenarios, it may also include a server on an intermediate platform, such as a server on a third-party server platform that has a communication link with the task scheduling center server. The server may include a single computer device, a server cluster consisting of multiple servers, or a distributed server structure.

[0111] Figure 13 This is a schematic block diagram illustrating the system configuration of the electronic device 9600 according to an embodiment of this application. Figure 13 As shown, the electronic device 9600 may include a central processing unit 9100 and a memory 9140; the memory 9140 is coupled to the central processing unit 9100. It is worth noting that... Figure 13 This is an example; other types of structures can also be used to supplement or replace this structure to achieve telecommunications functions or other functions.

[0112] In one embodiment, the function of constructing a high spatial resolution distribution map of the electrocarbon factor can be integrated into a central processing unit 9100. The central processing unit 9100 can be configured to perform the following controls: S101: Based on the system topology and system operating parameters, analyze the impact of wind and solar power injection on the total carbon flow rate of the system, and obtain the branch power flow injection matrix and the unit injection distribution matrix; S102: Determine the system node carbon potential based on the branch power flow injection matrix, the unit injection distribution matrix, and the pre-calculated carbon emission intensity of thermal power generation; S103: Insert the carbon potential of the system nodes into the layer after the substation location has been blurred to obtain a high spatial resolution distribution map of the electric carbon factor.

[0113] As described above, the high spatial resolution distribution map construction method for electric carbon factors provided in this application can, based on the determination of the hourly electric carbon factor of substations using power flow tracing methods that take into account the uncertainties of wind and solar power, propose a method for generating a specific user-hourly resolution electric carbon factor distribution map based on the grid structure. It uses the material balance method to calculate the hourly resolution carbon emission intensity of thermal power units on the source side, and combines power flow tracing based on the grid structure of the target area to obtain the electric carbon emission factors of substations at different voltage levels. Furthermore, it combines the coverage area of ​​substations at different voltage levels and uses spatial allocation technology to draw the electric carbon factor distribution map. Specifically, it uses power flow analysis to determine the carbon potential of each node in the power grid, then uses cluster analysis to classify substations into multiple types, and then uses spatial interpolation and the principle of prioritizing low voltage levels based on coverage area to determine the grid electric carbon factor of the target area. This method can alleviate the computational pressure of electric carbon factor simulation in distribution network substations, and generate high spatiotemporal accuracy (time to the hour, space to within 1 kilometer) electric carbon factors on the user side without exposing the regional grid structure, providing accurate factor data for user-side electric carbon emission accounting.

[0114] In another embodiment, the high spatial resolution distribution map construction device for the electric carbon factor can be configured separately from the central processing unit 9100. For example, the data composite transmission device for the high spatial resolution distribution map construction device for the electric carbon factor can be configured as a chip connected to the central processing unit 9100, and the function of the high spatial resolution distribution map construction method for the electric carbon factor can be realized through the control of the central processing unit.

[0115] like Figure 13 As shown, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is worth noting that the electronic device 9600 does not necessarily need to include these components. Figure 13All components shown; in addition, the electronic device 9600 may also include Figure 13 For components not shown, please refer to existing technologies.

[0116] like Figure 13 As shown, the central processing unit 9100, sometimes also referred to as a controller or operating control, may include a microprocessor or other processor device and / or logic device, which receives inputs and controls the operation of various components of the electronic device 9600.

[0117] The memory 9140 may be, for example, one or more of a cache, flash memory, hard drive, removable media, volatile memory, non-volatile memory, or other suitable devices. It may store the aforementioned failure-related information, and also store a program for executing that information. The central processing unit 9100 may execute the program stored in the memory 9140 to perform information storage or processing, etc.

[0118] Input unit 9120 provides input to central processing unit 9100. Input unit 9120 may be, for example, a keypad or touch input device. Power supply 9170 provides power to electronic device 9600. Display 9160 displays images and text. Display may be, for example, an LCD display, but is not limited thereto.

[0119] The memory 9140 can be a solid-state memory, such as a read-only memory (ROM), random access memory (RAM), a SIM card, etc. It can also be a memory that retains information even when power is off, can be selectively erased, and contains more data; examples of this type of memory are sometimes referred to as EPROMs. The memory 9140 can also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application / function storage unit 9142 for storing application programs and function programs or processes for executing the operation of the electronic device 9600 via the central processing unit 9100.

[0120] The memory 9140 may also include a data storage unit 9143 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit 9144 of the memory 9140 may include various drivers for the electronic device's communication functions and / or for performing other functions of the electronic device (such as messaging applications, address book applications, etc.).

[0121] The communication module 9110 is a transmitter / receiver that sends and receives signals via the antenna 9111. The communication module (transmitter / receiver) 9110 is coupled to the central processing unit 9100 to provide input signals and receive output signals, which is the same as in a conventional mobile communication terminal.

[0122] Based on different communication technologies, multiple communication modules 9110 can be configured in the same electronic device, such as cellular network modules, Bluetooth modules, and / or wireless LAN modules. The communication module (transmitter / receiver) 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and receive audio input from the microphone 9132, thereby realizing typical telecommunications functions. The audio processor 9130 may include any suitable buffer, decoder, amplifier, etc. Additionally, the audio processor 9130 is also coupled to a central processing unit 9100, enabling on-device recording via the microphone 9132 and on-device playback of stored sound via the speaker 9131.

[0123] Embodiments of this application also provide a computer-readable storage medium capable of implementing all steps in the method for constructing a high spatial resolution distribution map of the electric carbon factor with the execution subject being a server or client as described in the above embodiments. The computer-readable storage medium stores a computer program that, when executed by a processor, implements all steps of the method for constructing a high spatial resolution distribution map of the electric carbon factor with the execution subject being a server or client as described in the above embodiments. For example, when the processor executes the computer program, it implements the following steps: S101: Based on the system topology and system operating parameters, analyze the impact of wind and solar power injection on the total carbon flow rate of the system, and obtain the branch power flow injection matrix and the unit injection distribution matrix; S102: Determine the system node carbon potential based on the branch power flow injection matrix, the unit injection distribution matrix, and the pre-calculated carbon emission intensity of thermal power generation; S103: Insert the carbon potential of the system nodes into the layer after the substation location has been blurred to obtain a high spatial resolution distribution map of the electric carbon factor.

[0124] As described above, the high spatial resolution distribution map construction method for electric carbon factors provided in this application can, based on the determination of the hourly electric carbon factor of substations using power flow tracing methods that take into account the uncertainties of wind and solar power, propose a method for generating a specific user-hourly resolution electric carbon factor distribution map based on the grid structure. It uses the material balance method to calculate the hourly resolution carbon emission intensity of thermal power units on the source side, and combines power flow tracing based on the grid structure of the target area to obtain the electric carbon emission factors of substations at different voltage levels. Furthermore, it combines the coverage area of ​​substations at different voltage levels and uses spatial allocation technology to draw the electric carbon factor distribution map. Specifically, it uses power flow analysis to determine the carbon potential of each node in the power grid, then uses cluster analysis to classify substations into multiple types, and then uses spatial interpolation and the principle of prioritizing low voltage levels based on coverage area to determine the grid electric carbon factor of the target area. This method can alleviate the computational pressure of electric carbon factor simulation in distribution network substations, and generate high spatiotemporal accuracy (time to the hour, space to within 1 kilometer) electric carbon factors on the user side without exposing the regional grid structure, providing accurate factor data for user-side electric carbon emission accounting.

[0125] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0126] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0127] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0128] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0129] Specific embodiments have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

Claims

1. A method for constructing a high spatial resolution distribution map of the electric carbon factor, characterized in that, include: Based on the system topology and power grid system operating parameters, the impact of wind and solar power injection on the total carbon flow rate of the power grid system is analyzed, and the branch power flow injection matrix and the unit injection distribution matrix are obtained. The system node carbon potential is determined based on the branch power flow injection matrix, the unit injection distribution matrix, and the pre-calculated carbon emission intensity of thermal power generation. By inserting the carbon potential of the system nodes into the layer after the substation location has been blurred, a high spatial resolution distribution map of the electric carbon factor is obtained.

2. The method for constructing a high spatial resolution distribution map of the electrocarbon factor according to claim 1, characterized in that, The power grid system operating parameters include the load of each node, generator output, resistance parameters, and reactance parameters; the analysis of the impact of wind and solar power injection on the total carbon flow rate of the power grid system based on the system topology and power grid system operating parameters yields the branch power flow injection matrix and the unit injection distribution matrix, including: The total carbon flow rate, active power, and reactive power of the power grid system considering wind and solar power injection are determined based on the load of each node, the output of the generator, the resistance parameters, and the reactance parameters. The branch power flow injection matrix and the generator injection distribution matrix are constructed based on the active power and reactive power of the line. The total carbon flow rate of the power grid system is used to verify the branch power flow injection matrix and the unit injection distribution matrix.

3. The method for constructing a high spatial resolution distribution map of the electrocarbon factor according to claim 2, characterized in that, The calculation of the total carbon flux of the power grid system considering wind and solar injection, based on the load, generator output, resistance parameters, and reactance parameters of each node, includes: A model relating wind and solar power injection to the total carbon flow rate of the power grid system is constructed based on the load, generator output, resistance parameters, and reactance parameters of each node. The total carbon flow rate of the power grid system is calculated based on the wind and solar power injection power and the relationship model.

4. The method for constructing a high spatial resolution distribution map of the electrocarbon factor according to claim 1, characterized in that, The steps for pre-calculating the carbon emission intensity of thermal power generation include: Carbon emission performance during combustion is determined based on the carbon content of coal, carbon oxidation rate, carbon capture rate, molar mass of carbon and carbon dioxide, and fuel consumption per kilowatt-hour. Determine carbon oxidation carbon emission performance based on carbon oxidation rate; The carbon emission performance during the desulfurization process is determined based on the medium-level electricity fuel consumption, sulfur content of coal, and desulfurization efficiency. The carbon emission intensity of the thermal power generation is determined based on the carbon emission performance during the combustion process, the carbon emission performance during carbon oxidation, and the carbon emission performance during the desulfurization process.

5. The method for constructing a high spatial resolution distribution map of the electrocarbon factor according to claim 1, characterized in that, The determination of system node carbon potential based on the branch power flow injection matrix, the unit injection distribution matrix, and the pre-calculated carbon emission intensity of thermal power generation includes: Calculate the branch power flow transpose matrix corresponding to the branch power flow distribution matrix and the unit injection transpose matrix corresponding to the unit injection distribution matrix, respectively. The system node carbon potential is determined based on the branch power flow transpose matrix, the unit injection transpose matrix, and the unit's thermal power generation carbon emission intensity.

6. The method for constructing a high spatial resolution distribution map of the electrocarbon factor according to claim 1, characterized in that, The step of inserting the carbon potential of the system nodes into the layer after substation location fuzzing to obtain a high spatial resolution distribution map of the electric carbon factor includes: Cluster analysis of the substations is performed based on the obtained geographical coordinates of the substations and the system topology. The location of the substation is blurred based on the cluster analysis results; The carbon potential of the system nodes is interpolated to the power supply range of the clustered substation after fuzzy processing to obtain a high spatial resolution distribution map of the electric carbon factor.

7. A device for constructing a high spatial resolution distribution map of the electrocarbon factor, characterized in that, include: The matrix generation unit is used to analyze the impact of wind and solar power injection on the total carbon flow rate of the power grid system based on the system topology and power grid system operating parameters, and to obtain the branch power flow injection matrix and the unit injection distribution matrix. The node carbon potential determination unit is used to determine the system node carbon potential based on the branch power flow injection matrix, the unit injection distribution matrix, and the pre-calculated carbon emission intensity of thermal power generation. The distribution map generation unit is used to insert the carbon potential of the system nodes into the layer after the substation location has been blurred to obtain a high spatial resolution distribution map of the electric carbon factor.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method for constructing a high spatial resolution distribution map of the electrocarbon factor as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the method for constructing a high spatial resolution distribution map of the electrocarbon factor as described in any one of claims 1 to 6.

10. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method for constructing a high spatial resolution distribution map of the electrocarbon factor as described in any one of claims 1 to 6.

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