A dynamic carbon metering method and device in a multi-energy complementary scenario

Abstract

This specification discloses a dynamic carbon metering method and apparatus for a multi-energy complementary scenario. The method includes synchronously or asynchronously collecting grid data, photovoltaic data, energy storage data, and load data from the corresponding grid side, photovoltaic side, energy storage side, and load side based on preset grid acquisition frequencies, photovoltaic acquisition frequencies, energy storage acquisition frequencies, and load acquisition frequencies. It collects power structure data of the grid to determine the power generation ratio of each power source, and calculates the average carbon emission factor by combining it with pre-stored carbon emission factors for each power source. It identifies marginal generator units and extracts their carbon emission factors, then weights and combines the average carbon emission factor with the carbon emission factor corresponding to the marginal generator unit to obtain the actual carbon emission factor. This method reflects the overall characteristics of the power structure while considering the influence of marginal generator units, improving the dynamics and accuracy of the carbon emission factor, thereby reducing the calculated carbon metering error.

Description

Technical Field

[0001] This invention relates to the field of carbon metering technology, and in particular to a dynamic carbon metering method, device, electronic device, and storage medium for multi-energy complementary scenarios. Background Technology

[0002] With the large-scale integration of new energy sources, the power system is exhibiting diversified characteristics across "source-grid-load-storage." The widespread application of distributed energy resources (DERs), such as distributed photovoltaics, wind power, energy storage systems, and controllable loads, has fundamentally changed the carbon emission characteristics of the power system. Most existing technologies employ calculation methods based on fixed carbon emission factors. This approach uses annual or monthly average grid carbon emission factors to calculate carbon emissions based on electricity consumption. The calculation formula is: Carbon Emissions = Electricity Consumption × Fixed Carbon Emission Factor. However, in the "source-grid-load-storage" scenario, the fixed carbon emission factor cannot reflect real-time changes in the power supply structure, leading to significant carbon measurement errors. Especially during periods of large fluctuations in new energy output, the deviation between the fixed factor and the actual carbon emission level can reach over 30%. In summary, the fixed carbon emission factor lacks dynamism and accuracy, resulting in substantial errors in the calculated carbon emissions. Summary of the Invention

[0003] To address the problems existing in the prior art, this specification describes a dynamic carbon metering method, apparatus, electronic device, and storage medium in a multi-energy complementary scenario through one or more embodiments.

[0004] According to the first aspect, a dynamic carbon metering method for a multi-energy complementary scenario is provided, the method comprising:

[0005] Based on preset grid acquisition frequency, photovoltaic acquisition frequency, energy storage acquisition frequency and load acquisition frequency, grid data, photovoltaic data, energy storage data and load data are collected synchronously or asynchronously from the corresponding grid side, photovoltaic side, energy storage side and load side;

[0006] Data on the power structure of the power grid is collected to determine the power generation ratio of each power source. Combined with the pre-stored carbon emission factors of each power source, the average carbon emission factor is calculated. Marginal generator sets are identified and their carbon emission factors are extracted. The average carbon emission factor and the carbon emission factor corresponding to the marginal generator set are weighted and combined to obtain the actual carbon emission factor.

[0007] Based on the actual carbon emission factors and the power grid data, the carbon emissions from power grid supply are calculated. Based on the pre-stored carbon emission factors of each power source, the photovoltaic data, and the load data, the carbon emissions from photovoltaic self-consumption and the carbon reduction from surplus electricity fed into the grid are obtained. Based on the pre-stored carbon emission factors of each power source and the energy storage data, the carbon emissions from energy storage losses are calculated.

[0008] Based on the carbon emissions from the power grid supply, the carbon emissions from photovoltaic self-consumption, the carbon reduction from surplus electricity fed into the grid, and the carbon emissions from energy storage losses, a carbon flow network diagram representing the carbon emission path from the power source to the load, a carbon flow topology diagram representing the carbon emission flow logic, and a carbon emission distribution heat map representing the spatial density of carbon emissions are drawn, and the total carbon emissions are calculated.

[0009] Based on the carbon flow network diagram, the carbon flow topology diagram, the carbon emission distribution heat map, and the total carbon emissions, the carbon emission distribution characteristics and transmission paths are analyzed, and carbon metering application services are provided to users based on the analysis results.

[0010] Preferably, the method further includes:

[0011] The power structure of the power grid is collected based on a set power acquisition frequency to update the power generation ratio of each power source. If the difference between the updated power generation ratio and the original power generation ratio exceeds a preset ratio threshold, the actual carbon emission factor is updated based on the updated power generation ratio.

[0012] Preferably, providing carbon metering application services to users includes generating green electricity certificates for users, and the process of tracing the carbon emissions from photovoltaic self-consumption and the carbon reduction from surplus electricity fed into the grid based on the pre-stored carbon emission factors of each power source, photovoltaic data, and load data includes:

[0013] The surplus power fed into the grid is calculated based on the photovoltaic power generation capacity and the photovoltaic self-consumption capacity.

[0014] The carbon emissions from photovoltaic self-consumption are calculated based on the photovoltaic self-consumption power.

[0015] The carbon reduction of the surplus electricity fed into the grid is calculated based on the surplus electricity power, the grid carbon emission factor, and the photovoltaic carbon emission factor, and a green electricity certificate is generated based on the surplus electricity carbon reduction.

[0016] Preferably, the energy storage data includes charging time, charging power, charging capacity at charging time, charging carbon emission factor at charging time, discharging time, discharging power, and charging / discharging efficiency. The calculation of energy storage loss carbon emissions based on the pre-stored carbon emission factors of each power source and the energy storage data includes:

[0017] During energy storage charging, the total charging carbon emissions are calculated based on the charging amount at the charging time and the charging carbon emission factor at the charging time, the charging amount is calculated based on the charging time and the charging power, and the average charging carbon emission factor is calculated based on the total charging carbon emissions and the charging amount.

[0018] During energy storage discharge, the discharge power is calculated based on the discharge time, the discharge power, and the charge / discharge efficiency, and the carbon emissions from energy storage loss are calculated based on the charging capacity and the average carbon emission factor during charging.

[0019] Preferably, the carbon flow network diagram includes power source nodes, load nodes, confluence nodes, and connecting lines, and drawing the carbon flow network diagram characterizing the power source-load carbon emission path includes:

[0020] The carbon intensity configuration of the power nodes is determined based on the power type corresponding to each power node. The carbon emissions of the combiner node are equal to the sum of the carbon emissions of each power node. The carbon intensity of the combiner node is equal to the ratio of the carbon emissions of the combiner node to the sum of the power supply power of each power type corresponding to each power node. The carbon emissions of the load node are equal to the product of the load power of each load type corresponding to each load node and the carbon intensity of the combiner node.

[0021] Preferably, the calculation of total carbon emissions based on the carbon emissions from grid power supply, the carbon emissions from photovoltaic self-consumption, and the carbon reduction from surplus electricity fed into the grid includes: the total carbon emissions are the carbon emissions from grid power supply plus the carbon emissions from photovoltaic self-consumption minus the carbon reduction from surplus electricity fed into the grid.

[0022] Preferably, the carbon metering application services provided to users also include carbon emission detection, low-carbon scheduling suggestions, and carbon emission reduction benefit assessment.

[0023] According to a second aspect, a dynamic carbon metering device for a multi-energy complementary scenario is provided, the device implementing the steps of the method provided as in the first aspect or any possible implementation thereof, the device comprising:

[0024] The multi-data acquisition module is used to synchronously or asynchronously acquire grid data, photovoltaic data, energy storage data, and load data from the corresponding grid side, photovoltaic side, energy storage side, and load side based on preset grid acquisition frequency, photovoltaic acquisition frequency, energy storage acquisition frequency, and load acquisition frequency.

[0025] The actual carbon emission factor calculation module is used to collect power grid structure data in real time to determine the power generation ratio of each power source, and calculate the average carbon emission factor by combining it with the pre-stored carbon emission factors of each power source. It also identifies marginal generator sets and extracts their carbon emission factors, and performs a weighted combination of the average carbon emission factor and the carbon emission factor corresponding to the marginal generator set to obtain the actual carbon emission factor.

[0026] The carbon emission accounting module is used to calculate the carbon emissions of grid power supply based on the actual carbon emission factors and the grid data, trace the source of carbon emissions of photovoltaic self-consumption and carbon reduction of surplus electricity fed into the grid based on the pre-stored carbon emission factors of each power source, the photovoltaic data and the load data, and calculate the carbon emissions of energy storage loss based on the pre-stored carbon emission factors of each power source and the energy storage data.

[0027] The carbon flow tracking and visualization analysis module is used to draw a carbon flow network diagram representing the carbon emission path of the power supply to the load, a carbon flow topology diagram representing the carbon emission flow logic, and a carbon emission distribution heat map representing the carbon emission spatial density based on the carbon emission of the power grid supply, the carbon emission of the photovoltaic self-consumption, the carbon reduction of the surplus electricity fed into the grid, and the carbon emission of the energy storage loss, and to calculate the total carbon emission.

[0028] The carbon metering application module is used to analyze the carbon emission distribution characteristics and transmission paths based on the carbon flow network diagram, the carbon flow topology diagram, the carbon emission distribution heat map, and the total carbon emissions, and to provide carbon metering application services to users based on the analysis results.

[0029] According to a third aspect, an electronic device is provided, including a processor and a memory;

[0030] The processor is connected to the memory;

[0031] The memory is used to store executable program code;

[0032] The processor runs a program corresponding to the executable program code stored in the memory to perform the steps of the method provided as in the first aspect or any possible implementation thereof.

[0033] According to a fourth aspect, a computer-readable storage medium is provided having a computer program stored thereon, the computer-readable storage medium storing instructions that, when executed on a computer or processor, cause the computer or processor to perform the method provided as in the first aspect or any possible implementation thereof.

[0034] The beneficial effects of this invention are as follows:

[0035] 1. The method and apparatus provided in the embodiments of this specification reflect the overall characteristics of the power supply structure and take into account the influence of marginal generator sets in the calculation of the actual carbon emission factor, making the carbon emission factor more scientific and reasonable, improving the dynamics and accuracy of the carbon emission factor, and thus reducing the calculated carbon measurement error.

[0036] 2. The method and apparatus provided in the embodiments of this specification accurately distinguish between self-consumed electricity and surplus electricity fed into the grid based on the real-time comparison between photovoltaic power generation and local load power. This method is simple to calculate and has strong real-time performance. It equates surplus electricity fed into the grid with a reduction in the amount of thermal power generation in the grid, reflecting the contribution of photovoltaic power generation to the carbon emission reduction of the grid and providing a scientific basis for carbon trading and the issuance of green electricity certificates.

[0037] 3. The method and apparatus provided in the embodiments of this specification record the charging amount and corresponding carbon emission factor for each time period during the charging process, and use the carbon emission factor during the charging process to calculate the carbon emission during the discharging process. This fully considers the time mismatch characteristics of energy storage, avoids the double calculation or omission of carbon emissions, and includes the carbon emission corresponding to the lost electricity in the total carbon emission of the energy storage system, reflecting the true carbon cost of energy storage use.

[0038] 4. The method and apparatus provided in the embodiments of this specification are set with a periodic update frequency of 5-15 minutes. When the power supply structure changes significantly, the update is triggered immediately, achieving the best balance between computational efficiency and accuracy. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is a flowchart illustrating a dynamic carbon metering method in a multi-energy complementary scenario, as specifically implemented in this manual.

[0041] Figure 2 This is a schematic diagram of the structure of a dynamic carbon metering device in a multi-energy complementary scenario in the specific implementation of this specification;

[0042] Figure 3 This is a schematic diagram of the structure of an electronic device in a specific implementation of this specification;

[0043] Figure 4 This is a schematic diagram of the carbon flow network in the specific implementation of this specification;

[0044] Figure 5 This is a schematic diagram of the carbon flow topology in the specific implementation of this specification;

[0045] Figure 6 This is a schematic diagram of the carbon emission distribution heat map in the specific implementation of this instruction manual. Detailed Implementation

[0046] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0047] In the following description, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance. The following description provides multiple embodiments of this application, which can be substituted or combined with each other. Therefore, this application can also be considered to include all possible combinations of the same and / or different embodiments described. Thus, if one embodiment includes features A, B, and C, and another embodiment includes features B and D, then this application should also be considered to include embodiments containing one or more other possible combinations of A, B, C, and D, even if such embodiments are not explicitly described in the following text.

[0048] The following description provides examples and does not limit the scope, applicability, or examples set forth in the claims. Changes may be made to the function and arrangement of the described elements without departing from the scope of this application. Various processes or components may be appropriately omitted, substituted, or added to the examples. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Furthermore, features described with respect to some examples may be combined into other examples.

[0049] See Figure 1 , Figure 1 This is a flowchart illustrating the dynamic carbon metering method in a multi-energy complementary scenario provided in this application embodiment. In this application embodiment, the method includes:

[0050] S101. Based on the preset grid acquisition frequency, photovoltaic acquisition frequency, energy storage acquisition frequency and load acquisition frequency, synchronously or asynchronously acquire grid data, photovoltaic data, energy storage data and load data from the corresponding grid side, photovoltaic side, energy storage side and load side.

[0051] S102. Collect power structure data of the power grid to determine the power generation ratio of each power source, and calculate the average carbon emission factor by combining the pre-stored carbon emission factors of each power source. Identify marginal generator sets and extract their carbon emission factors. Weight the average carbon emission factor and the carbon emission factor corresponding to the marginal generator set to obtain the actual carbon emission factor.

[0052] S103. Calculate the carbon emissions of grid power supply based on the actual carbon emission factors and the grid data. Based on the pre-stored carbon emission factors of each power source, the photovoltaic data, and the load data, trace the source to obtain the carbon emissions of photovoltaic self-consumption and the carbon reduction of surplus electricity fed into the grid. Calculate the carbon emissions of energy storage loss based on the pre-stored carbon emission factors of each power source and the energy storage data.

[0053] S104. Based on the carbon emissions from the power grid supply, the carbon emissions from photovoltaic self-consumption, the carbon reduction from surplus electricity fed into the grid, and the carbon emissions from energy storage losses, draw a carbon flow network diagram representing the carbon emission path from the power source to the load, a carbon flow topology diagram representing the carbon emission flow logic, and a carbon emission distribution heat map representing the spatial density of carbon emissions, and calculate the total carbon emissions.

[0054] S105. Based on the carbon flow network diagram, the carbon flow topology diagram, the carbon emission distribution heat map, and the total carbon emissions, analyze the carbon emission distribution characteristics and transmission paths, and provide carbon metering application services to users based on the analysis results.

[0055] The entity executing this application may be a server of a power system.

[0056] In the embodiments of this specification, grid data, photovoltaic data, energy storage data, and load data are collected synchronously or asynchronously from the corresponding grid side, photovoltaic side, energy storage side, and load side based on the set grid acquisition frequency, photovoltaic acquisition frequency, energy storage acquisition frequency, and load acquisition frequency. Specifically, the grid acquisition frequency can be set to 5-15 minutes / time to collect real-time electricity consumption, electricity price, etc. from the grid side to form grid data; the photovoltaic acquisition frequency can be set to 1-5 minutes / time to collect power generation, irradiance, temperature, etc. from the photovoltaic side to form photovoltaic data; the energy storage acquisition frequency can be set to 1 minute / time to collect SOC, charging and discharging power, charging time, charging power, charging amount at charging time, discharging time, discharging power, etc. from the energy storage side to form energy storage data; and the load acquisition frequency can be set to 5-15 minutes / time to collect power consumption, electricity consumption type, etc. from the load side to form load data. All collected data undergoes data preprocessing operations, including format conversion, timestamp alignment, outlier detection, and data quality verification. The preprocessed data is then sent to the data acquisition bus to provide a data foundation for subsequent carbon metering calculations.

[0057] Data on the power grid's power structure is collected, including various power sources (coal, gas, hydro, wind, solar, nuclear, etc.) and their corresponding power generation capacity. The power generation capacity of each power source is compared to the sum of their respective capacities to obtain their respective power generation percentages. Carbon emission factors for each power source are then pre-stored on a server. The carbon emission factors for different power types are set according to life cycle assessment principles. For thermal power, the carbon emission factor considers fuel combustion and power generation efficiency, with a range of 0.8-1.0. For gas and electricity, use 0.4-0.5. For renewable energy sources such as hydropower, wind power, and photovoltaics, emissions are considered throughout the entire life cycle, including equipment manufacturing, transportation, and installation, with a carbon emission factor of 0.01-0.05. The carbon emission factor for nuclear power is set at 0.01-0.02. The average carbon emission factor is calculated based on the carbon emission factors of various power sources. The formula for calculating the average carbon emission factor is as follows:

[0058] in, for Average carbon emission factor at time, For the first Type of power supply Power generation at any given time For the first Carbon emission factors of similar power sources.

[0059] Marginal generating units (MRGs) are identified and their carbon emission factors are extracted. These MRGs are typically peak-shaving thermal power units. The average carbon emission factor is then weighted and combined with the MRG's carbon emission factor to obtain the actual carbon emission factor. The formula for calculating the actual carbon emission factor is as follows:

[0060] in, Actual carbon emission factor for The carbon emission factor of the marginal generator unit at any given time. As a weighting factor, based on the nature of electricity consumption, under base load, During peak load, In this application, the calculation of the actual carbon emission factor reflects both the overall characteristics of the power supply structure and the influence of marginal generator units, making the carbon emission factor more scientific and reasonable, improving its dynamics and accuracy, and thus reducing the calculated carbon measurement error.

[0061] As an example, suppose the power structure at a certain moment is: coal power 40%, gas power 10%, hydropower 20%, wind power 15%, solar power 10%, and nuclear power 5%. Based on the carbon emission factors of each power source (coal power 0.9...),... Gas-electric 0.45 Hydropower 0.02 Wind power 0.01 Photovoltaics 0.03 Nuclear power 0.01 The average carbon emission factor is calculated based on the above formula.

[0062] .

[0063] Assuming the marginal generating unit is coal-fired, the marginal carbon emission factor is... ,

[0064] For base load, weighting factor Take 0.8, actual carbon emission factor For peak load, the weighting factor... Take 0.2, actual carbon emission factor .

[0065] The carbon emissions from grid power supply are calculated based on the actual carbon emission factors and grid data (specifically, grid power supply). Then, the carbon emissions from photovoltaic self-consumption and the carbon reduction from surplus electricity fed into the grid are calculated based on the carbon emission factors of each power source, photovoltaic data, and data traceability. Finally, the carbon emissions from energy storage losses are calculated based on the carbon emission factors of each power source and energy storage data.

[0066] Based on the calculated carbon emissions from grid power supply, photovoltaic self-consumption, surplus electricity fed into the grid, and energy storage losses, carbon flow network diagrams, carbon flow topology diagrams, and carbon emission distribution heatmaps are created. The total carbon emissions are then calculated. The carbon flow network diagram is created as follows: multiple power supply nodes, multiple load nodes, confluence nodes, and multiple connecting lines are drawn. Nodes have carbon intensity attributes, and connecting lines have power flow attributes. The carbon flow topology diagram is created using a Sankey diagram visualization method, where the width of the flow band represents the magnitude of carbon emissions, and different colors represent different types of energy, visually displaying the flow path of carbon emissions. The carbon emission distribution heatmap is created by mapping the carbon intensity of each node to different colors and overlaying them onto the carbon flow network diagram, forming a spatial visualization of carbon emission distribution.

[0067] Carbon metering application services are provided to users based on the carbon emissions from grid power supply, carbon emissions from photovoltaic self-consumption, carbon reduction from surplus electricity fed into the grid, carbon emissions from energy storage losses, total carbon emissions, carbon flow network diagrams, carbon flow topology diagrams, and carbon emission distribution heat maps. Carbon metering application services include: (1) Real-time carbon emission monitoring: Displaying the current carbon emission status of the system in the form of a dashboard, including indicators such as carbon emission rate, cumulative carbon emissions, carbon emission factors, and green electricity consumption ratio, with data refreshed every minute; (2) Historical carbon emission statistical analysis: Providing multi-dimensional data query and comparison functions, allowing users to perform statistical analysis by time period, energy type, and load type to identify carbon emission patterns and anomalies; (3) Carbon emission reduction benefit assessment: Quantifying the carbon emission reduction effects of various carbon reduction measures (such as photovoltaic power generation, energy storage peak shaving, load optimization, etc.) and providing users with intuitive carbon emission reduction reports; 4) Automatic generation of green electricity certificates: Based on green electricity consumption data, green electricity certificates are automatically generated, including information such as power generation time, power generation, carbon emission reduction, and certificate number, and are stored through blockchain technology; (5) Low-carbon scheduling suggestions: Based on carbon emission prediction and carbon flow analysis results, low-carbon optimization scheduling suggestions are provided to users, such as the best usage time of high energy-consuming equipment and energy storage charging and discharging strategies; (6) Customized scenario services: Customized carbon metering services are provided for different application scenarios such as microgrids, virtual power plants, and green parks, such as carbon metering in island operation mode, aggregated level carbon emission statistics, and carbon emission comparison between enterprises.

[0068] In one possible implementation, the method further includes:

[0069] The power structure of the power grid is collected based on a set power acquisition frequency to update the power generation ratio of each power source. If the difference between the updated power generation ratio and the original power generation ratio exceeds a preset ratio threshold, the actual carbon emission factor is updated based on the updated power generation ratio.

[0070] In the embodiments of this specification, the power structure of the power grid is periodically collected according to a set power collection frequency. The power collection frequency can be set to 5-15 minutes / time to obtain the current power structure data of the power grid. The power structure data includes various types of power sources (coal power, gas power, hydropower, wind power, photovoltaic, nuclear power, etc.) and the corresponding power generation capacity of each type of power source. The power generation capacity of each type of power source is compared with the sum of the power generation capacity of each type of power source to obtain the power generation capacity ratio of each type of power source. If the deviation between the current power structure of the power grid and the power structure at the time of the last update exceeds a preset ratio threshold (e.g., the thermal power ratio was 20% in the last collection and 30% in the current collection, the deviation of the thermal power ratio between the two collections is 10%, exceeding the ratio threshold (5%)), that is, the difference between the updated power generation capacity ratio and the power generation capacity ratio before the update exceeds the preset ratio threshold, the actual carbon emission factor is recalculated based on the current power structure data. In this application, by setting a power collection frequency to periodically update the power structure of the power grid, the carbon emission factor can be calculated based on the real-time power structure to ensure the accuracy of the carbon emission factor.

[0071] In one possible implementation, providing carbon metering application services to users includes generating green electricity certificates for users, and the process of tracing the carbon emissions from photovoltaic self-consumption and the carbon reduction from surplus electricity fed into the grid based on the pre-stored carbon emission factors of each power source, photovoltaic data, and load data includes:

[0072] The surplus power fed into the grid is calculated based on the photovoltaic power generation capacity and the photovoltaic self-consumption capacity.

[0073] The carbon emissions from photovoltaic self-consumption are calculated based on the photovoltaic self-consumption power.

[0074] The carbon reduction of the surplus electricity fed into the grid is calculated based on the surplus electricity power, the grid carbon emission factor, and the photovoltaic carbon emission factor, and a green electricity certificate is generated based on the surplus electricity carbon reduction.

[0075] In the embodiments of this specification, a network topology model of a multi-energy system is first established, including power supply nodes (grid, photovoltaic, energy storage), load nodes, and connecting lines. Then, a proportional allocation principle is used for photovoltaic power consumption tracking: for any node, the proportion of photovoltaic self-consumption flowing into that node is equal to the weighted average photovoltaic self-consumption proportion of all upstream nodes, with the weight being the power supply share of each upstream node. The calculation formula is:

[0076]

[0077] in This represents the proportion of photovoltaic power consumed by the node itself. For the first The power supply of each upstream node, For the first The proportion of photovoltaic self-consumption at each upstream node.

[0078] At any given moment Photovoltaic power generation is The local load power is The power exchange between the power grids is .when At that time, all photovoltaic power was used for self-consumption, with a self-consumption ratio of 100% and a self-consumption power of [missing information]. The power of the grid is .when At that time, the photovoltaic portion was used for self-consumption and the portion was fed into the grid, with a self-consumption ratio of 1%. Self-consumption power is The surplus power connected to the grid is The formula for calculating carbon emissions from photovoltaic self-consumption is:

[0079]

[0080] in, Carbon emissions from photovoltaic self-consumption For photovoltaic self-consumption power, This represents the carbon emission factor throughout the entire life cycle of photovoltaic power generation. The carbon emission reduction calculation for surplus electricity fed into the grid considers the substitution effect. Grid-connected surplus electricity is equivalent to reducing the amount of thermal power generated by the grid; the carbon emission reduction calculation formula is as follows:

[0081]

[0082] in, To reduce carbon emissions by feeding surplus electricity into the grid. This is the surplus power supplied to the grid. for The system tracks the grid carbon emission factor at all times, reflecting the contribution of photovoltaic power generation to grid carbon reduction. It cumulatively records the carbon emission reduction from surplus electricity fed into the grid and the surplus electricity power at each moment, generating green electricity certificates monthly.

[0083] As an example, assuming the installed photovoltaic capacity is 100kW, the photovoltaic power generation at a certain moment... Local load power Then the photovoltaic self-consumption power Photovoltaic grid-connected power Power grid supply The photovoltaic self-consumption rate is 100%. Assume that at another moment, the photovoltaic power generation capacity... Local load power Then the photovoltaic self-consumption power Surplus power grid connection The self-consumption rate of photovoltaic power is 77.8%.

[0084] Using the second time point as an example, let's assume the grid carbon emission factor at the time of grid connection. 0.6 Photovoltaic carbon emission factor Regarding the carbon emission reduction from grid connection of surplus electricity, the carbon emission reduction is calculated according to the above formula as follows: The carbon emissions from photovoltaic self-consumption are .

[0085] Assuming the self-consumption of photovoltaic power in this month is 9000 kWh, the system automatically generates a green electricity certificate with the certificate number xx-xxxx-xx-xxx, containing the following information: power generation period from xxxx year x month x day to x month xx day, power generation is 9000 kWh, and carbon emission reduction is 4230. The certificate holder is a certain user, and the certificate was generated on [date]. The certificate is stored on the blockchain, and users can verify it on a blockchain explorer using the certificate number, ensuring the certificate's authenticity and immutability.

[0086] In one possible implementation, the energy storage data includes charging time, charging power, charging capacity at charging time, charging carbon emission factor at charging time, discharging time, discharging power, and charging / discharging efficiency. The calculation of energy storage loss carbon emissions based on the pre-stored carbon emission factors of each power source and the energy storage data includes:

[0087] During energy storage charging, the total charging carbon emissions are calculated based on the charging amount at the charging time and the charging carbon emission factor at the charging time, the charging amount is calculated based on the charging time and the charging power, and the average charging carbon emission factor is calculated based on the total charging carbon emissions and the charging amount.

[0088] During energy storage discharge, the discharge power is calculated based on the discharge time, the discharge power, and the charge / discharge efficiency, and the carbon emissions from energy storage loss are calculated based on the charging capacity and the average carbon emission factor during charging.

[0089] In the embodiments of this specification, energy storage data includes charging time, charging power, charging capacity at the charging moment, charging carbon emission factor at the charging moment, discharging time, discharging power, and charging / discharging efficiency. When the energy storage discharges, the carbon emissions corresponding to the discharging capacity are calculated based on the carbon emission factor recorded during charging. The calculation formula is as follows:

[0090]

[0091] in, The carbon emissions corresponding to the amount of electricity discharged. For the first Batch discharge capacity, The above formula ensures that the carbon emission factor during the charging of this batch of electricity corresponds to the carbon emission level of the power grid during charging, avoiding double counting or omissions.

[0092] Carbon emission corrections for energy storage losses consider charge / discharge efficiency and self-discharge losses. The charge / discharge efficiency of energy storage systems is typically 85%-95%, and the self-discharge rate is 1%-5% per month. The formula for calculating carbon emissions corresponding to lost electricity is as follows:

[0093]

[0094] in To consume electricity, The average carbon emission factor during the loss period corresponds to the average carbon emission factor during the charging period. Carbon emissions from energy storage losses are included in the total carbon emissions of the energy storage system, reflecting the true carbon cost of using energy storage.

[0095] Furthermore, the assessment of the carbon emission reduction benefits of energy storage participating in peak shaving is based on substitution effect analysis. Energy storage charges during low-carbon periods and discharges during high-carbon periods, which is equivalent to reducing the amount of coal-fired power generation during high-carbon periods. The carbon emission reduction calculation formula is:

[0096]

[0097] in This is the amount of discharge capacity. The carbon emission factor of the power grid during the discharge period. The carbon emission factor during charging. When When energy storage is used properly, it generates carbon emission reduction benefits; conversely, it increases carbon emissions.

[0098] As an example, an energy storage carbon metering module records the energy storage charging and discharging process. Assume the energy storage capacity is 50 kWh, and charging occurs between 10:00 AM and 12:00 PM on a certain day. The charging power is 20 kW, the charging time is 2 hours, and the charging amount is 40 kWh. The grid carbon emission factor at 10:00 AM... The value at 11:00 is 0.35. Therefore, according to the timestamp method, the carbon emissions from charging are recorded as follows: [10:00-11:00, 20kWh, 0.4kWh]. 8 ], [11:00-12:00, 20kWh, 0.35 7 Total carbon emissions from charging

[0099]

[0100] Assuming discharge occurs between 18:00 and 20:00 on that day, with a discharge power of 20kW, a discharge duration of 2 hours, and a discharge capacity of 38kWh (considering a charge / discharge efficiency of 95%), what is the average carbon emission factor during the charging period? Then the carbon emissions from the discharge are Power loss carbon emissions from losses .

[0101] Assuming the power grid carbon emission factor during the discharge period Average carbon emission factor during charging period According to the formula

[0102]

[0103] The carbon emission reduction from energy storage participating in peak shaving is This demonstrates the low-carbon value of energy storage.

[0104] In one possible implementation, the calculation of total carbon emissions based on the carbon emissions from grid power supply, the carbon emissions from photovoltaic self-consumption, and the carbon reduction from surplus electricity fed into the grid includes: the total carbon emissions are the carbon emissions from grid power supply plus the carbon emissions from photovoltaic self-consumption minus the carbon reduction from surplus electricity fed into the grid.

[0105] In one possible implementation, assume that the photovoltaic power generation on a certain day is... Of which, for personal use Surplus electricity connected to the internet Energy storage and charging (10:00-12:00), Discharge (18:00-20:00); Total power consumption The energy balance relationship is as follows: That is, 650kWh = 150kWh + 38kWh + 462kWh.

[0106] Carbon emissions from each stage are calculated as follows: 1. Carbon emissions from grid power supply: Carbon emissions from grid power supply (Including 40kWh during energy storage charging), assuming an average carbon emission factor throughout the day. Carbon emissions from power grid supply 2. Carbon emissions from photovoltaic self-consumption: Photovoltaic self-consumption carbon emission factors carbon emissions 3. Carbon emission reduction through surplus electricity fed into the grid: (This refers to the process of feeding surplus electricity into the grid.) Based on an average grid carbon emission factor of 0.5 Calculations show that carbon emission reduction is... 4. Energy storage losses and carbon emissions: losses The average carbon emission factor during the loss period is 0.375. Calculations show that carbon emissions are 5. Total carbon emissions of the system .

[0107] It should be noted that the 40 kWh absorbed from the grid during energy storage charging and its carbon emissions... The carbon emissions from the energy storage system, which are already included in the grid power supply carbon emissions, are traced back to the carbon emission factor during charging, thus avoiding double counting.

[0108] In one possible implementation, the carbon flow network diagram includes power generation nodes, load nodes, confluence nodes, and connecting lines. Drawing the carbon flow network diagram characterizing the power generation-load carbon emission path includes:

[0109] The carbon intensity configuration of the power nodes is determined based on the power type corresponding to each power node. The carbon emissions of the combiner node are equal to the sum of the carbon emissions of each power node. The carbon intensity of the combiner node is equal to the ratio of the carbon emissions of the combiner node to the sum of the power supply power of each power type corresponding to each power node. The carbon emissions of the load node are equal to the product of the load power of each load type corresponding to each load node and the carbon intensity of the combiner node.

[0110] As described above in the embodiments of this specification, the carbon flow network diagram includes multiple power supply nodes, multiple load nodes, combiner nodes, and multiple connecting lines. The carbon flow network diagram is drawn based on the carbon emissions from grid power supply, carbon emissions from photovoltaic self-consumption, carbon reduction from surplus electricity fed into the grid, and carbon emissions from energy storage losses calculated above. ,in It is a set of nodes, including power generation nodes (grid, photovoltaic, energy storage), multiple load nodes, and intermediate nodes. Let be a set of edges representing energy flow paths. Each node has a carbon intensity attribute. (Carbon emissions / Electricity), each edge has power flow attributes. .

[0111] For any node j, the formula for calculating its carbon emissions is:

[0112]

[0113] in, For nodes carbon emissions, For the node Flow to Node power, For nodes carbon strength,

[0114] The formula for calculating the carbon intensity of node j is:

[0115]

[0116] in, For nodes carbon strength, For the nodes that flow through Total power.

[0117] As an example, such as Figure 4 As shown, the network is assumed to contain 4 power generation nodes (grid, photovoltaic, energy storage discharge, energy storage charging), 3 load nodes (lighting, air conditioning, power), and 1 aggregation node (bus). The carbon intensity of the power generation nodes is set as follows: Grid node Photovoltaic nodes Energy storage discharge node (Based on the carbon factor weighted average during charging), the energy storage charging node is considered as a negative carbon intensity node. .

[0118] Assume the power flow at a certain moment is: power supplied by the grid Photovoltaic power generation Energy storage and discharge Energy storage and charging The lighting load is 30kW, the air conditioning load is 80kW, and the power load is 70kW. All power sources are aggregated at the busbar node, with a total power of 180kW. Carbon emissions are...

[0119] ,

[0120] carbon strength is .

[0121] Carbon emissions at each load node are calculated based on busbar carbon intensity: Lighting carbon emissions , , .

[0122] In the embodiments described in this specification, such as Figure 5 As shown, the carbon flow topology diagram, from left to right, consists of the power supply layer, bus layer, and load layer. The flow band width from the grid node to the bus is 50. (100kW×0.5), red in color, with a flow band width of 1.8 from the photovoltaic node to the busbar. (60kW×0.03), green in color, with a flow band width of 7.5 from the energy storage node to the busbar. (20kW × 0.375), color blue. The flow band width from the busbar to each load is 9.87. 26.32 23.03 The color is gray. The carbon flow topology diagram clearly shows that the power grid is the main source of carbon emissions, while air conditioning is the largest carbon emission load.

[0123] In the embodiments described in this specification, such as Figure 6 As shown in the carbon emission distribution heatmap, the carbon intensity of the bus node is 0.329. It is displayed in yellow, indicating a carbon intensity of 0.5 at the grid node. It is displayed in orange-red, and the carbon intensity of the photovoltaic node is 0.03. It is displayed in dark green, with a carbon intensity of 0.375 for the energy storage node. The heatmap, displayed in light yellow, clearly shows the high-carbon areas (grid side) and low-carbon areas (photovoltaic side) in the system, providing direction for carbon reduction measures.

[0124] In the embodiments of this specification, 24-hour carbon flow data is recorded to generate a dynamic evolution animation of the carbon flow. The animation shows that during the photovoltaic power generation period from 8:00 AM to 4:00 PM, the busbar carbon intensity decreases from 0.5 to 0.3. The system as a whole displays a green color. During the peak load period from 18:00 to 22:00 at night, the increased power supply from the grid causes the carbon intensity of the busbar to rise to 0.6. The system is displayed in red, indicating that the energy storage absorbs carbon emissions during charging from 10:00 to 12:00 and releases carbon emissions during discharging from 18:00 to 20:00. This visually demonstrates the time-varying characteristics of carbon emissions and the coupling relationship between multiple energy sources.

[0125] In one possible implementation, the provision of carbon metering application services to users also includes carbon emission detection, low-carbon scheduling suggestions, and carbon emission reduction benefit assessment.

[0126] In the embodiments described in this specification, the carbon metering service includes: 1. Real-time carbon emission monitoring function, which displays the current carbon emission status of the system in the form of a dashboard: assuming the current carbon emission rate is 10. The cumulative carbon emissions for the day were 150. The cumulative carbon emissions this month are 3500 The carbon emission factor is 0.45. The green electricity consumption rate is 35%. The dashboard uses a combination of charts and numbers, and the data is refreshed every minute, allowing users to monitor the system's carbon emissions in real time. 2. The historical carbon emission statistical analysis function provides multi-dimensional data query and comparison: For example, if a user queries the carbon emission trend over the past 30 days, the system generates a line chart showing daily carbon emissions between 200-300... Carbon emissions fluctuate between days, with lower emissions on weekends and higher emissions on weekdays. Users can perform statistical analysis by time period (day, week, month, year), by energy type (grid, photovoltaic, energy storage), and by load type (lighting, air conditioning, power) to identify carbon emission patterns and anomalies; 3. Green electricity certificate generation: Assuming the self-consumption of photovoltaic power in this month is 9000kWh, the system automatically generates a green electricity certificate with the certificate number xx-xxxx-xx-xxx, containing the following information: power generation time from xxxx year x month x day to x month xx day, power generation is 9000kWh, and carbon emission reduction is 4230. The certificate holder is a user, and the certificate was generated on [date]. The certificate is stored on the blockchain, and users can verify it using the certificate number on a blockchain explorer, ensuring the certificate's authenticity and immutability. 4. The low-carbon dispatch suggestion function provides optimization suggestions based on carbon emission predictions: Assuming the system predicts sufficient photovoltaic power generation from 14:00 to 16:00 tomorrow, and a low grid carbon emission factor (0.3)... Users are advised to schedule the use of high-energy-consuming equipment (such as washing machines and charging stations) during this period. The system predicts that the load peak will occur from 18:00 to 20:00 tomorrow, resulting in a higher grid carbon emission factor (0.7). It is recommended that the energy storage system discharge during this period to reduce grid power supply and lower carbon emissions. This information will be sent to users via push notification; users can choose to execute it manually or set it to execute automatically. 5. Customized services for application scenarios: For microgrid scenarios, carbon metering functionality is provided in islanded operation mode to calculate the microgrid's carbon self-sufficiency rate. For virtual power plant scenarios, aggregated-level carbon emission statistics and distributed energy resource carbon contribution analysis are provided. For green industrial park scenarios, inter-enterprise carbon emission comparison and carbon quota management functions are provided.

[0127] The following will be combined with the appendix Figure 2 This paper provides a detailed description of the dynamic carbon metering device in a multi-energy complementary scenario provided in the embodiments of this application. It should be noted that the appendix... Figure 2 The dynamic carbon metering device shown in the multi-energy complementary scenario is used to perform the functions described in this application. Figure 1 The methods shown in the embodiments are for illustrative purposes only, illustrating the parts relevant to the embodiments of this application. For specific technical details not disclosed, please refer to this application. Figure 1 The example shown.

[0128] Please see Figure 2 , Figure 2 This is a schematic diagram of the structure of the dynamic carbon metering device in a multi-energy complementary scenario provided in the embodiments of this application. Figure 2 As shown, the device includes:

[0129] The multi-data acquisition module 201 is used to synchronously or asynchronously acquire grid data, photovoltaic data, energy storage data and load data from the corresponding grid side, photovoltaic side, energy storage side and load side based on the preset grid acquisition frequency, photovoltaic acquisition frequency, energy storage acquisition frequency and load acquisition frequency.

[0130] The actual carbon emission factor calculation module 202 is used to collect power structure data of the power grid in real time to determine the power generation ratio of each power source, and calculate the average carbon emission factor by combining the pre-stored carbon emission factors of each power source, identify marginal generator sets and extract their carbon emission factors, and perform weighted combination of the average carbon emission factor and the carbon emission factor corresponding to the marginal generator set to obtain the actual carbon emission factor.

[0131] The carbon emission accounting module 203 is used to calculate the carbon emission of grid power supply based on the actual carbon emission factor and the grid data, to trace the source of the carbon emission of photovoltaic self-consumption and the carbon reduction of surplus electricity fed into the grid based on the pre-stored carbon emission factors of each power source, the photovoltaic data and the load data, and to calculate the carbon emission of energy storage loss based on the pre-stored carbon emission factors of each power source and the energy storage data.

[0132] The carbon flow tracking and visualization analysis module 204 is used to draw a carbon flow network diagram representing the carbon emission path of the power supply to the load, a carbon flow topology diagram representing the carbon emission flow logic, and a carbon emission distribution heat map representing the carbon emission spatial density based on the carbon emission of the power grid supply, the carbon emission of the photovoltaic self-consumption, the carbon reduction of the surplus electricity fed into the grid, and the carbon emission of the energy storage loss, and to calculate the total carbon emission.

[0133] The carbon metering application module 205 is used to analyze the carbon emission distribution characteristics and transmission paths based on the carbon flow network diagram, the carbon flow topology diagram, the carbon emission distribution heat map, and the total carbon emissions, and to provide carbon metering application services to users based on the analysis results.

[0134] In one possible implementation, the actual carbon emission factor calculation module 202 is specifically used for:

[0135] The power structure of the power grid is collected based on a set power acquisition frequency to update the power generation ratio of each power source. If the difference between the updated power generation ratio and the original power generation ratio exceeds a preset ratio threshold, the actual carbon emission factor is updated based on the updated power generation ratio.

[0136] In one possible implementation, the carbon emission accounting module 203 is specifically used for:

[0137] The provision of carbon metering application services to users includes generating green electricity certificates for users. The process of tracing the carbon emission factors of each power source, photovoltaic data, and load data to obtain the carbon emission reduction of photovoltaic self-consumption and surplus electricity fed into the grid includes:

[0138] The surplus power fed into the grid is calculated based on the photovoltaic power generation capacity and the photovoltaic self-consumption capacity.

[0139] The carbon emissions from photovoltaic self-consumption are calculated based on the photovoltaic self-consumption power.

[0140] The carbon reduction of the surplus electricity fed into the grid is calculated based on the surplus electricity power, the grid carbon emission factor, and the photovoltaic carbon emission factor, and a green electricity certificate is generated based on the surplus electricity carbon reduction.

[0141] In one possible implementation, the carbon emission accounting module 203 is specifically used for:

[0142] The energy storage data includes charging time, charging power, charging capacity at charging time, charging carbon emission factor at charging time, discharging time, discharging power, and charging / discharging efficiency. The carbon emissions from energy storage losses calculated based on the pre-stored carbon emission factors of each power source and the energy storage data include:

[0143] During energy storage charging, the total charging carbon emissions are calculated based on the charging amount at the charging time and the charging carbon emission factor at the charging time, the charging amount is calculated based on the charging time and the charging power, and the average charging carbon emission factor is calculated based on the total charging carbon emissions and the charging amount.

[0144] During energy storage discharge, the discharge power is calculated based on the discharge time, the discharge power, and the charge / discharge efficiency, and the carbon emissions from energy storage loss are calculated based on the charging capacity and the average carbon emission factor during charging.

[0145] In one possible implementation, the carbon flow tracing and visualization analysis module 204 is specifically used for:

[0146] The carbon flow network diagram includes power generation nodes, load nodes, confluence nodes, and connecting lines. The process of drawing the carbon flow network diagram representing the carbon emission path from power source to load includes:

[0147] The carbon intensity of each power node is configured based on the power type corresponding to that power node. The carbon emissions of each combiner node are equal to the sum of the carbon emissions of all power nodes. The carbon intensity of each combiner node is equal to the ratio of its carbon emissions to the sum of the power supply power of each power type. The carbon emissions of each load node are equal to the product of the load power of each load type and the carbon intensity of the combiner node.

[0148] In one possible implementation, the carbon flow tracing and visualization analysis module 204 is specifically used for:

[0149] The calculation of total carbon emissions based on the carbon emissions from grid power supply, the carbon emissions from photovoltaic self-consumption, and the carbon reduction from surplus electricity fed into the grid includes: the total carbon emissions are the carbon emissions from grid power supply plus the carbon emissions from photovoltaic self-consumption minus the carbon reduction from surplus electricity fed into the grid.

[0150] In one possible implementation, the carbon metering application module 205 is specifically used for:

[0151] The carbon metering application services provided to users also include carbon emission detection, low-carbon scheduling suggestions, and carbon emission reduction benefit assessment.

[0152] Those skilled in the art will clearly understand that the technical solutions of the embodiments of this application can be implemented by means of software and / or hardware. In this specification, "unit" and "module" refer to software and / or hardware that can independently complete or cooperate with other components to complete a specific function, wherein the hardware may be, for example, a field-programmable gate array (FPGA), an integrated circuit (IC), etc.

[0153] Each processing unit and / or module in the embodiments of this application can be implemented by an analog circuit that implements the functions described in the embodiments of this application, or by software that executes the functions described in the embodiments of this application.

[0154] See Figure 3 It shows a schematic diagram of the structure of an electronic device according to an embodiment of this application, which can be used to implement... Figure 1 The method in the illustrated embodiment. (As shown) Figure 3 As shown, the electronic device 300 may include: at least one central processing unit 301, at least one network interface 304, user interface 303, memory 305, and at least one communication bus 302.

[0155] The communication bus 302 is used to enable communication between these components.

[0156] The user interface 303 may include a display screen and a camera. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0157] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0158] The central processing unit 301 may include one or more processing cores. The central processing unit 301 connects to various parts within the electronic device 300 using various interfaces and lines. It executes various functions of the terminal 300 and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and by calling data stored in the memory 305. Optionally, the central processing unit 301 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The central processing unit 301 may integrate one or more of the following: a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also be implemented as a separate chip without being integrated into the central processing unit 301.

[0159] The memory 305 may include random access memory (RAM) or read-only memory. Optionally, the memory 305 may include a non-transitory computer-readable storage medium. The memory 305 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 305 may also be at least one storage device located remotely from the aforementioned central processing unit 301. Figure 3 As shown, the memory 305, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and program instructions.

[0160] exist Figure 3 In the illustrated electronic device 300, the user interface 303 is mainly used to provide an input interface for the user and to acquire user input data; while the central processing unit 301 can be used to call the application program stored in the memory 305 and specifically perform the following operations:

[0161] S101. Based on the preset grid acquisition frequency, photovoltaic acquisition frequency, energy storage acquisition frequency and load acquisition frequency, synchronously or asynchronously acquire grid data, photovoltaic data, energy storage data and load data from the corresponding grid side, photovoltaic side, energy storage side and load side.

[0162] S102. Collect power structure data of the power grid to determine the power generation ratio of each power source, and calculate the average carbon emission factor by combining the pre-stored carbon emission factors of each power source. Identify marginal generator sets and extract their carbon emission factors. Weight the average carbon emission factor and the carbon emission factor corresponding to the marginal generator set to obtain the actual carbon emission factor.

[0163] S103. Calculate the carbon emissions of grid power supply based on the actual carbon emission factors and the grid data. Based on the pre-stored carbon emission factors of each power source, the photovoltaic data, and the load data, trace the source to obtain the carbon emissions of photovoltaic self-consumption and the carbon reduction of surplus electricity fed into the grid. Calculate the carbon emissions of energy storage loss based on the pre-stored carbon emission factors of each power source and the energy storage data.

[0164] S104. Based on the carbon emissions from the power grid supply, the carbon emissions from photovoltaic self-consumption, the carbon reduction from surplus electricity fed into the grid, and the carbon emissions from energy storage losses, draw a carbon flow network diagram representing the carbon emission path from the power source to the load, a carbon flow topology diagram representing the carbon emission flow logic, and a carbon emission distribution heat map representing the spatial density of carbon emissions, and calculate the total carbon emissions.

[0165] S105. Based on the carbon flow network diagram, the carbon flow topology diagram, the carbon emission distribution heat map, and the total carbon emissions, analyze the carbon emission distribution characteristics and transmission paths, and provide carbon metering application services to users based on the analysis results.

[0166] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method. The computer-readable storage medium may include, but is not limited to, any type of disk, including floppy disks, optical disks, DVDs, CD-ROMs, microdrives, as well as magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic cards or optical cards, nanosystems (including molecular memory ICs), or any type of medium or device suitable for storing instructions and / or data.

[0167] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0168] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0169] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between devices or units may be electrical or other forms.

[0170] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0171] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0172] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0173] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.

[0174] The foregoing description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Those skilled in the art will readily conceive of embodiments of this disclosure upon considering the specification and practicing the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described herein. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A dynamic carbon metering method for multi-energy complementary scenarios, characterized in that, The method includes: Based on preset grid acquisition frequency, photovoltaic acquisition frequency, energy storage acquisition frequency and load acquisition frequency, grid data, photovoltaic data, energy storage data and load data are collected synchronously or asynchronously from the corresponding grid side, photovoltaic side, energy storage side and load side; Data on the power structure of the power grid is collected to determine the power generation ratio of each power source. Combined with the pre-stored carbon emission factors of each power source, the average carbon emission factor is calculated. Marginal generator sets are identified and their carbon emission factors are extracted. The average carbon emission factor and the carbon emission factor corresponding to the marginal generator set are weighted and combined to obtain the actual carbon emission factor. Based on the actual carbon emission factors and the power grid data, the carbon emissions from power grid supply are calculated. Based on the pre-stored carbon emission factors of each power source, the photovoltaic data, and the load data, the carbon emissions from photovoltaic self-consumption and the carbon reduction from surplus electricity fed into the grid are obtained. Based on the pre-stored carbon emission factors of each power source and the energy storage data, the carbon emissions from energy storage losses are calculated. Based on the carbon emissions from the power grid supply, the carbon emissions from photovoltaic self-consumption, the carbon reduction from surplus electricity fed into the grid, and the carbon emissions from energy storage losses, a carbon flow network diagram representing the carbon emission path from the power source to the load, a carbon flow topology diagram representing the carbon emission flow logic, and a carbon emission distribution heat map representing the spatial density of carbon emissions are drawn, and the total carbon emissions are calculated. Based on the carbon flow network diagram, the carbon flow topology diagram, the carbon emission distribution heat map, and the total carbon emissions, the carbon emission distribution characteristics and transmission paths are analyzed, and carbon metering application services are provided to users based on the analysis results.

2. The dynamic carbon metering method in a multi-energy complementary scenario according to claim 1, characterized in that, The method further includes: The power structure of the power grid is collected based on a set power acquisition frequency to update the power generation ratio of each power source. If the difference between the updated power generation ratio and the original power generation ratio exceeds a preset ratio threshold, the actual carbon emission factor is updated based on the updated power generation ratio.

3. The dynamic carbon metering method in a multi-energy complementary scenario according to claim 1, characterized in that, The provision of carbon metering application services to users includes generating green electricity certificates for users. The process of tracing the carbon emission factors of each power source, photovoltaic data, and load data to obtain the carbon emission reduction of photovoltaic self-consumption and surplus electricity fed into the grid includes: The surplus power fed into the grid is calculated based on the photovoltaic power generation capacity and the photovoltaic self-consumption capacity. The carbon emissions from photovoltaic self-consumption are calculated based on the photovoltaic self-consumption power. The carbon reduction of the surplus electricity fed into the grid is calculated based on the surplus electricity power, the grid carbon emission factor, and the photovoltaic carbon emission factor, and a green electricity certificate is generated based on the surplus electricity carbon reduction.

4. The dynamic carbon metering method in a multi-energy complementary scenario according to claim 1, characterized in that, The energy storage data includes charging time, charging power, charging capacity at charging time, charging carbon emission factor at charging time, discharging time, discharging power, and charging / discharging efficiency. The carbon emissions from energy storage losses calculated based on the pre-stored carbon emission factors of each power source and the energy storage data include: During energy storage charging, the total charging carbon emissions are calculated based on the charging amount at the charging time and the charging carbon emission factor at the charging time, the charging amount is calculated based on the charging time and the charging power, and the average charging carbon emission factor is calculated based on the total charging carbon emissions and the charging amount. During energy storage discharge, the discharge power is calculated based on the discharge time, the discharge power, and the charge / discharge efficiency, and the carbon emissions from energy storage loss are calculated based on the charging capacity and the average carbon emission factor during charging.

5. The dynamic carbon metering method in a multi-energy complementary scenario according to claim 1, characterized in that, The carbon flow network diagram includes power generation nodes, load nodes, confluence nodes, and connecting lines. The process of drawing the carbon flow network diagram representing the carbon emission path from power source to load includes: The carbon intensity configuration of the power nodes is determined based on the power type corresponding to each power node. The carbon emissions of the combiner node are equal to the sum of the carbon emissions of each power node. The carbon intensity of the combiner node is equal to the ratio of the carbon emissions of the combiner node to the sum of the power supply power of each power type corresponding to each power node. The carbon emissions of the load node are equal to the product of the load power of each load type corresponding to each load node and the carbon intensity of the combiner node.

6. The dynamic carbon metering method in a multi-energy complementary scenario according to claim 1, characterized in that, The calculation of total carbon emissions based on the carbon emissions from grid power supply, the carbon emissions from photovoltaic self-consumption, and the carbon reduction from surplus electricity fed into the grid includes: the total carbon emissions are the carbon emissions from grid power supply plus the carbon emissions from photovoltaic self-consumption minus the carbon reduction from surplus electricity fed into the grid.

7. The dynamic carbon metering method in a multi-energy complementary scenario according to claim 3, characterized in that, The carbon metering application services provided to users also include carbon emission detection, low-carbon scheduling suggestions, and carbon emission reduction benefit assessment.

8. A dynamic carbon metering device for multi-energy complementary scenarios, characterized in that, The apparatus implements the steps of the method as described in any one of claims 1-7, the apparatus comprising: The multi-data acquisition module is used to synchronously or asynchronously acquire grid data, photovoltaic data, energy storage data, and load data from the corresponding grid side, photovoltaic side, energy storage side, and load side based on preset grid acquisition frequency, photovoltaic acquisition frequency, energy storage acquisition frequency, and load acquisition frequency. The actual carbon emission factor calculation module is used to collect power grid structure data in real time to determine the power generation ratio of each power source, and calculate the average carbon emission factor by combining it with the pre-stored carbon emission factors of each power source. It also identifies marginal generator sets and extracts their carbon emission factors, and performs a weighted combination of the average carbon emission factor and the carbon emission factor corresponding to the marginal generator set to obtain the actual carbon emission factor. The carbon emission accounting module is used to calculate the carbon emissions of grid power supply based on the actual carbon emission factors and the grid data, trace the source of carbon emissions of photovoltaic self-consumption and carbon reduction of surplus electricity fed into the grid based on the pre-stored carbon emission factors of each power source, the photovoltaic data and the load data, and calculate the carbon emissions of energy storage loss based on the pre-stored carbon emission factors of each power source and the energy storage data. The carbon flow tracking and visualization analysis module is used to draw a carbon flow network diagram representing the carbon emission path of the power supply to the load, a carbon flow topology diagram representing the carbon emission flow logic, and a carbon emission distribution heat map representing the carbon emission spatial density based on the carbon emission of the power grid supply, the carbon emission of the photovoltaic self-consumption, the carbon reduction of the surplus electricity fed into the grid, and the carbon emission of the energy storage loss, and to calculate the total carbon emission. The carbon metering application module is used to analyze the carbon emission distribution characteristics and transmission paths based on the carbon flow network diagram, the carbon flow topology diagram, the carbon emission distribution heat map, and the total carbon emissions, and to provide carbon metering application services to users based on the analysis results.

9. 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 computer program, it implements the steps of the method as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, the computer-readable storage medium storing instructions that, when executed on a computer or processor, cause the computer or processor to perform the steps of the method as claimed in any one of claims 1-7.

Images