Method, device, equipment and storage medium for measuring marginal carbon emissions

Abstract

The present application discloses a method, apparatus, device and storage medium for measuring marginal carbon emissions. The method includes: establishing a seemingly unrelated regression model, where the seemingly unrelated regression model includes a coal-fired power equation, a gas-fired power equation, a biomass energy equation and an externally received power equation; obtaining marginal coefficients according to the seemingly unrelated regression model; and calculating the marginal carbon emissions of power generation based on the carbon emission intensities of coal-fired power, gas-fired power, biomass energy and externally received power and the marginal coefficients. Through the above method, the present application uses a seemingly unrelated regression model, combines the conversion efficiencies of different types of generator sets and the carbon emission intensities of different types of fossil energy, can more accurately identify marginal units, fully considers the time heterogeneity of demand, and considers the coordinated power generation of different technical types in actual situations. Therefore, it can more accurately estimate the carbon emissions of power generation in different time periods and more precisely depict the carbon footprint of industrial enterprises.

Description

Technical Field

[0001] This application relates to the technical field of carbon emissions, and particularly to a method, device, equipment and storage medium for measuring marginal carbon emissions. Background Art

[0002] Existing methods for characterizing carbon footprints mainly focus on the measurement of average carbon emissions (AEF) (Defra, 2013; White, 2004). This method assumes that the annual average emissions of clean technologies equally replace all other non-clean technologies. The weighted average of the annual carbon emissions of all non-clean units is the average carbon emission coefficient. The problems of this method are as follows:

[0003] 1. This method inaccurately characterizes the carbon footprint. This method is only an approximate measurement method for calculating how much carbon emissions are reduced by clean technologies, because the substitution effect of clean technologies on non-clean technologies occurs more in marginal units rather than all units. For example, nuclear power units generally serve as base loads, and increasing clean technologies will not replace nuclear power units for power generation. The contribution of clean technologies to reducing carbon emissions calculated directly using AEF is not accurate enough.

[0004] 2. This method does not consider time heterogeneity. The demand of the power system has time heterogeneity. The marginal units during peak power demand periods and non-peak periods are different, and the corresponding carbon emission intensities are different, which further makes the carbon footprints during peak demand and non-peak periods different. However, AEF uses the weighted average of the annual emission levels of all units and does not consider the time heterogeneity of carbon emissions.

[0005] Therefore, using AEF cannot accurately characterize the real-time carbon emissions of industrial enterprises, and thus cannot describe the carbon footprints of enterprises, which is not conducive to the evaluation of the effects of existing emission reduction policies and the further upgrading and adjustment of the industrial structure. Summary of the Invention

[0006] This application provides a method, device, equipment and storage medium for measuring marginal carbon emissions to solve the problem that AEF cannot accurately characterize the real-time carbon emissions of industrial enterprises.

[0007] To solve the above technical problems, this application proposes a method for measuring marginal carbon emissions, including: establishing a seemingly unrelated regression model, where the seemingly unrelated regression model includes a coal-fired power equation, a gas-fired power equation, a biomass energy equation, and an externally received power equation; obtaining marginal coefficients according to the seemingly unrelated regression model; where the marginal coefficients are the model parameters of the seemingly unrelated regression model; calculating the marginal carbon emissions of power generation according to the carbon emission intensities of coal-fired power, gas-fired power, biomass energy, and externally received power and the marginal coefficients.

[0008] Optionally, calculate the marginal carbon emissions of power generation based on the carbon emission intensities and marginal coefficients of coal-fired power, gas-fired power, biomass energy, and externally received electricity, including: calculating the marginal carbon emissions of power generation MEF as:

[0009] MEF = e coal α2 + e gas β2 + e biomass γ2 + e import δ2; where e coal is the carbon emission intensity of coal-fired power; e gas is the carbon emission intensity of gas-fired power; e biomass is the carbon emission intensity of biomass energy; e import is the carbon emission intensity of externally received electricity; α2, β2, γ2, and δ2 are marginal coefficients.

[0010] Optionally, establish a seemingly unrelated regression model, including: separately calculating the coal-fired power equation, gas-fired power equation, biomass energy equation, and externally received electricity equation:

[0011]

[0012]

[0013]

[0014]

[0015] where t represents hours; Δ is the first-order difference, Coal t , Gas, Biomass t and Import t are the hourly power generation of provincial coal-fired power, gas-fired power, biomass energy, and externally received electricity respectively; Wind t and Demand t are the hourly wind power generation and the market's electricity demand respectively, X t is other relevant control variable, and are the disturbance terms of coal-fired power, gas-fired power, biomass energy, and externally received electricity respectively; θ coal , θ gas , θ biomass and θ import are the other control variables of coal-fired power, gas-fired power, biomass energy, and externally received electricity respectively; α0, β0, γ0, and δ0 are intercept terms; α1, β1, γ1, and δ1 are used to reflect the substitution effects of wind power technology on coal-fired power, gas-fired power, biomass energy, and externally received electricity.

[0016] Optionally, obtaining marginal coefficients according to the similarity independent regression model includes: combining historical power generation granularity data and the similarity independent regression model to obtain marginal coefficients.

[0017] To solve the above technical problems, the present application proposes a measurement device for marginal carbon emissions, including: a model establishment module for establishing a similarity independent regression model, where the similarity independent regression model includes a coal power equation, a gas power equation, a biomass energy equation, and an externally received power equation; a marginal coefficient module for obtaining marginal coefficients according to the similarity independent regression model; where the marginal coefficient is a model parameter of the similarity independent regression model; a calculation module for calculating the marginal carbon emissions of power generation according to the carbon emission intensities of coal power, gas power, biomass energy, and externally received power and the marginal coefficients.

[0018] Optionally, the calculation module is further configured to: calculate the marginal carbon emissions MEF of power generation as: MEF = e coal α2 + e gas β2 + e biomass γ2 + e import δ2; where, e coal is the carbon emission intensity of coal power; e gas is the carbon emission intensity of gas power; e biomass is the carbon emission intensity of biomass energy; e import is the carbon emission intensity of externally received power; α2, β2, γ2, and δ2 are marginal coefficients.

[0019] Optionally, the model establishment module is further configured to: respectively calculate the coal power equation, the gas power equation, the biomass energy equation, and the externally received power equation:

[0020]

[0021]

[0022]

[0023]

[0024] where, t represents hours; Δ is the first-order difference, Coal t 、Gas、Biomass t and Import t are respectively the hourly power generation of provincial coal power, gas power, biomass energy, and externally received power; Wind t and Demand t are respectively the hourly wind power generation and the market power demand, X t is other relevant control variables, and are the interference terms of coal-fired power, gas-fired power, biomass energy, and externally received power respectively; θ coal , θ gas , θ biomass and θ import are the other control variables of coal-fired power, gas-fired power, biomass energy, and externally received power respectively; α0, β0, γ0, and δ0 are intercept terms; α1, β1, γ1, and δ1 are used to reflect the substitution effects of wind power technology on coal-fired power, gas-fired power, biomass energy, and externally received power.

[0025] Optionally, the marginal coefficient module is further configured to: combine historical power generation granularity data and a seemingly unrelated regression model to obtain marginal coefficients.

[0026] To solve the above technical problems, the present application proposes a measurement device for marginal carbon emissions, including a memory and a processor. The memory is connected to the processor, and the memory stores a computer program. When the computer program is executed by the processor, the above measurement method for marginal carbon emissions is implemented.

[0027] To solve the above technical problems, the present application proposes a computer-readable storage medium storing a computer program. When the computer program is executed, the above measurement method for marginal carbon emissions is implemented.

[0028] The present application proposes a measurement method, device, equipment, and storage medium for marginal carbon emissions. The method includes: establishing a seemingly unrelated regression model, where the seemingly unrelated regression model includes a coal-fired power equation, a gas-fired power equation, a biomass energy equation, and an externally received power equation; obtaining marginal coefficients according to the seemingly unrelated regression model; calculating the marginal carbon emissions of power generation based on the carbon emission intensities of coal-fired power, gas-fired power, biomass energy, and externally received power and the marginal coefficients. Through the above method, the present application uses an approximately unrelated regression model, combines the conversion efficiencies of different types of generator sets and the carbon emission intensities of different types of fossil energy, can more accurately identify marginal units, fully considers the time heterogeneity of demand, and considers the coordinated power generation of different technical types in actual situations. Therefore, it can more accurately estimate the carbon emissions of power generation in different time periods and more precisely depict the carbon footprint of industrial enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] To more clearly illustrate the technical solutions of the present application, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0030] Figure 1 is a schematic flowchart of an embodiment of the measurement method for marginal carbon emissions of the present application;

[0031] Figure 2It is a schematic structural diagram of an embodiment of the measurement device for the marginal carbon emissions of the present application;

[0032] Figure 3 It is a schematic structural diagram of an embodiment of the measurement equipment for the marginal carbon emissions of the present application;

[0033] Figure 4 It is a schematic structural diagram of an embodiment of the computer-readable storage medium of the present application. Detailed implementation manners

[0034] To enable those skilled in the art to better understand the technical solutions of the present application, the measurement methods, devices, equipment and storage media for the marginal carbon emissions provided by the present application will be further described in detail below in conjunction with the accompanying drawings and specific implementation manners.

[0035] First, the terms appearing in the present application are explained:

[0036] Carbon footprint: The aggregation of greenhouse gas emissions caused by enterprises, institutions, activities, products or individuals through transportation, food production and consumption, and various production processes, etc.

[0037] Weighted average: An average method that takes into account weights.

[0038] Seemingly unrelated (approximate uncorrelated) regression model: A type of econometric model. The characteristic of this method is that there is no internal connection between the variables of each equation, but there is a correlation between the disturbance terms of each equation.

[0039] Carbon emission intensity: The amount of carbon dioxide emissions brought by unit power generation. Different power generation technologies have different carbon emission intensities.

[0040] AEF: Average carbon emission factor.

[0041] MEF: Marginal carbon emission factor.

[0042] Coal: Coal, which refers to coal-fired power generation technology in the formula.

[0043] Wind: Wind, which refers to wind power generation technology in the formula.

[0044] Biomass: Biomass, which refers to biomass power generation technology in the formula.

[0045] Gas: Natural gas, which refers to gas-fired power generation technology in the formula.

[0046] Import: Import, which refers to imported electricity in the formula.

[0047] Demand: Demand, which refers to the total demand of the power system in the formula. In practical applications, it is interchangeable with the concept of the total load of the power system.

[0048] This application proposes a method for measuring marginal carbon emissions. Please refer to Figure 1 , Figure 1 which is a schematic flowchart of an embodiment of the method for measuring marginal carbon emissions in this application. In this embodiment, the method for measuring marginal carbon emissions may include steps S110 to S130, and the specific steps are as follows:

[0049] S110: Establish a seemingly unrelated regression model, where the seemingly unrelated regression model includes a coal-fired power equation, a gas-fired power equation, a biomass energy equation, and an imported electricity equation.

[0050] Calculate the coal-fired power equation, the gas-fired power equation, the biomass energy equation, and the imported electricity equation respectively, so as to obtain a seemingly unrelated (SURE) regression model. Specifically, the expressions of the coal-fired power equation, the gas-fired power equation, the biomass energy equation, and the imported electricity equation are as follows:

[0051]

[0052]

[0053]

[0054]

[0055] where t represents hours; Δ is the first-order difference, Coal t , Gas, Biomass t and Import t are the hourly power generations of in-province coal-fired power, gas-fired power, biomass energy, and imported electricity respectively; since in actual application, nuclear power units act as base loads and do not act as marginal units, the situation of nuclear power as a marginal unit is not considered. Other power types that will not have a significant impact on the results, such as tidal energy, geothermal energy, etc., are ignored.

[0056] Wind t and Demand t are the hourly wind power generation and the market's electricity demand respectively. X t is other relevant control variables, which can be dummy variables of units or hours to capture individual fixed effects or time fixed effects.

[0057] and are the disturbance terms of coal-fired power, gas-fired power, biomass energy, and imported electricity respectively. Each equation of the regression equation reflects what impact a change in clean technology (taking wind power technology as an example) or a change in demand will have on the power generations of other corresponding technologies. For example, in the first equation, α1 represents the impact of coal-fired power on the change in wind power, and α2 represents the impact of coal-fired power on the change in electricity demand.

[0058] The seemingly unrelated regression model restricts the correlation between disturbance terms within the same equation to zero, allowing for correlation between disturbance terms across equations, which is more in line with the actual situation of coordinated power generation of different types of generating units.

[0059] α0, β0, γ0, and δ0 are the intercept terms of the model parameters, reflecting the initial height of the influencing trend.

[0060] α1, β1, γ1, and δ1 are the model parameters used to reflect the substitution effects of wind power technology on coal power, gas power, biomass energy, and externally received power.

[0061] α2, β2, γ2, and δ2 are the model parameters used to reflect the marginal emission rates of the loads of coal power, gas power, biomass energy, and externally received power, and they are also the marginal coefficients of the seemingly unrelated regression model to be obtained in the next step. They can be used to calculate the marginal carbon emissions of power generation.

[0062] θ coal 、θ gas 、θ biomass and θ import are the other control variables of coal power, gas power, biomass energy, and externally received power, respectively.

[0063] S120: Obtain the marginal coefficients according to the seemingly unrelated regression model.

[0064] Combining the historical power generation granularity data and the seemingly unrelated regression model, the marginal coefficients are obtained. For example, using the historical power generation granularity data at the hourly level of different power sources in Guangdong, together with relevant data processing software (R, stata, spss, etc.), input the code to obtain the marginal coefficients.

[0065] Example 1:

[0066] In R, input the following content using the organized data:

[0067] rl = d_coal ~ d_wind + d_demand + Hours

[0068] r2 = d_ccgt ~ d_wind + d_demand + Hours

[0069] r3 = d_biomass ~ d_wind + d_demand + Hours

[0070] r4 = d_import ~ d_wind + d_demand + Hours

[0071] The above is used to define the regression, where r1 to r4 are the regression names, and the rest are data variables. Then input:

[0072] fitsur <- systemfit(list(coalreg = r1, ccgtreg = r2, biomassreg = r3, importreg = r4), data = agg_grid)

[0073] summary(fitsur)

[0074] The regression results can be obtained, where agg_grid is the data name and systemfit() is the seemingly unrelated regression model.

[0075] S130: Calculate the marginal carbon emissions of power generation based on the carbon emission intensities and marginal coefficients of coal-fired power, gas-fired power, biomass energy, and externally received electricity.

[0076] Based on the carbon emission intensities and marginal coefficients of coal-fired power, gas-fired power, biomass energy, and externally received electricity, and combined with the following formula, calculate the marginal carbon emissions of power generation:

[0077] MEF = e coal α2 + e gas β2 + e biomass γ2 + e import δ2;

[0078] Among them, e coal is the carbon emission intensity of coal-fired power; e gas is the carbon emission intensity of gas-fired power; e biomass is the carbon emission intensity of biomass energy; e import is the carbon emission intensity of externally received electricity; α2, β2, γ2, and δ2 are the marginal coefficients estimated in step S120.

[0079] Among them, MEF (Marginal Emission Factor) reflects the marginal carbon emissions of the power system under different power structures and different load conditions, and can be used to characterize the carbon footprint of industrial enterprises.

[0080] This embodiment proposes a method for measuring marginal carbon emissions, including: establishing a seemingly unrelated regression model, where the seemingly unrelated regression model includes a coal-fired power equation, a gas-fired power equation, a biomass energy equation, and an externally received power equation; obtaining marginal coefficients according to the seemingly unrelated regression model; calculating the marginal carbon emissions of power generation based on the carbon emission intensities of coal-fired power, gas-fired power, biomass energy, and externally received power and the marginal coefficients. Through the above method, this embodiment uses an approximately unrelated regression model, combines the conversion efficiencies of different types of generator sets and the carbon emission intensities of different types of fossil energy, can identify marginal units more accurately, fully considers the time heterogeneity of demand, and considers the coordinated power generation of different technical types in actual situations. Therefore, it can more accurately estimate the carbon emissions of power generation in different time periods and more precisely depict the carbon footprint of industrial enterprises.

[0081] Based on the above method for measuring marginal carbon emissions, the present application also proposes a device for measuring marginal carbon emissions. Please refer to Figure 2 , Figure 2 which is a schematic structural diagram of an embodiment of the device for measuring marginal carbon emissions of the present application.

[0082] In this embodiment, the device for measuring marginal carbon emissions may include:

[0083] A model establishment module 110, configured to establish a seemingly unrelated regression model, where the seemingly unrelated regression model includes a coal-fired power equation, a gas-fired power equation, a biomass energy equation, and an externally received power equation.

[0084] A marginal coefficient module 120, configured to obtain marginal coefficients according to the seemingly unrelated regression model, where the marginal coefficients are model parameters of the seemingly unrelated regression model.

[0085] A calculation module 130, configured to calculate the marginal carbon emissions of power generation based on the carbon emission intensities of coal-fired power, gas-fired power, biomass energy, and externally received power and the marginal coefficients.

[0086] Optionally, the calculation module 130 is further configured to: calculate the marginal carbon emissions MEF of power generation as: MEF = e coal α2 + e gas β2 + e biomass γ2 + e import δ2; where e coal is the carbon emission intensity of coal-fired power; e gas is the carbon emission intensity of gas-fired power; e biomass is the carbon emission intensity of biomass energy; e import is the carbon emission intensity of externally received power; α2, β2, γ2, and δ2 are marginal coefficients.

[0087] Optionally, the model establishment module 110 is further configured to: respectively calculate the coal-fired power equation, the gas-fired power equation, the biomass energy equation, and the externally received power equation:

[0088]

[0089]

[0090]

[0091]

[0092] where t represents hours; Δ is the first-order difference, Coal t , Gas, Biomass t and Import t are the hourly power generation of in-province coal power, gas power, biomass energy, and externally received power respectively; Wind t and Demand t are the hourly wind power generation and the electricity demand of the market respectively, X t is other relevant control variables, and are the disturbance terms of coal power, gas power, biomass energy, and externally received power respectively; θ coal , θ gas , θ biomass and θ import are other control variables of coal power, gas power, biomass energy, and externally received power respectively; α0, β0, γ0, and δ0 are intercept terms; α1, β1, γ1, and δ1 are used to reflect the substitution effects of wind power technology on coal power, gas power, biomass energy, and externally received power.

[0093] Optionally, the marginal coefficient module 120 is further configured to: combine historical power generation granularity data and a seemingly unrelated regression model to obtain a marginal coefficient.

[0094] Based on the above measurement method of marginal carbon emissions, the present application also proposes a measurement device for marginal carbon emissions, as Figure 3 shown, Figure 3 is a schematic structural diagram of an embodiment of the measurement device for marginal carbon emissions of the present application. The measurement device 300 for marginal carbon emissions may include a memory 31 and a processor 32. The memory 31 is connected to the processor 32. A computer program is stored in the memory 31. When the computer program is executed by the processor 32, the method of any of the above embodiments is implemented. Its steps and principles have been introduced in detail in the above method and will not be elaborated here.

[0095] In this embodiment, the processor 32 can also be referred to as a CPU (central processing unit). The processor 32 can be an integrated circuit chip with signal processing capabilities. The processor 32 can also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0096] Based on the above method for measuring marginal carbon emissions, the present application also proposes a computer-readable storage medium. Please refer to Figure 4 , Figure 4 which is a schematic structural diagram of an embodiment of the computer-readable storage medium of the present application. A computer program 41 is stored on the computer-readable storage medium 400. When the computer program 41 is executed by a processor, the method of any of the above embodiments is implemented. The steps and principles have been introduced in detail in the above method and will not be repeated here.

[0097] Furthermore, the computer-readable storage medium 400 can also be various media such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic tape, or an optical disc that can store program codes.

[0098] In summary, the present application has the following technical effects:

[0099] 1. Compared with AEF, the calculation of marginal carbon emissions in the present application can accurately identify marginal units, and the description of the carbon footprint is more accurate;

[0100] 2. Compared with AEF, the marginal carbon emissions in the present application consider time heterogeneity. During the day and night, spring and autumn, and winter and summer, due to different total electricity demands, the power generation structure and marginal response units are different; similarly, the improvement of generator efficiency and the increasing cleanliness of energy will also affect the marginal emission factor. This further increases the accuracy of the carbon footprint description;

[0101] 3. Compared with AEF, the setting of the seemingly unrelated regression model in the present application considers the coordinated power generation of different technology types in actual situations, making the description of the carbon footprint more accurate;

[0102] 4. The use of seemingly unrelated in the present application refines the sources of carbon emissions and can identify the impact path of marginal carbon emissions.

[0103] It is understood that the specific embodiments described herein are only for explaining the present application and not for limiting the present application. Additionally, for ease of description, only the parts related to the present application rather than all structures are shown in the drawings. The step numbers used in the text are only for convenience of description and do not limit the execution order of the steps. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0104] The terms "first", "second", etc. in the present application are used to distinguish different objects rather than to describe a specific order. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products or devices.

[0105] Referring to "embodiments" herein means that the specific features, structures or characteristics described in connection with the embodiments can be included in at least one embodiment of the present application. The phrase appearing at various positions in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0106] The above description is only the implementation manner of the present application and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the content of the specification and drawings of the present application, or directly or indirectly applied in other related technical fields, is similarly included in the patent protection scope of the present application.

Claims

1. A method for measuring marginal carbon emissions, characterized in that, Including: Establish a similar uncorrelated regression model, wherein the similar uncorrelated regression model includes a coal-fired power equation, a gas-fired power equation, a biomass energy equation, and an externally received power equation; the establishment of the similar uncorrelated regression model includes: respectively calculating the coal-fired power equation, the gas-fired power equation, the biomass energy equation, and the externally received power equation: where t represents hours; Δ is the first difference; Coal t , Gas, Biomass t and Import t are the hourly power generation of in - province coal - fired power, gas - fired power, biomass energy, and externally received power respectively; Wind t and Demand t are the hourly wind power generation and the electricity demand of the market respectively, X t is other relevant control variable, and are the disturbance terms of coal - fired power, gas - fired power, biomass energy, and externally received power respectively; θ coal , θ gas , θ biomass and θ import are the other control variables of coal - fired power, gas - fired power, biomass energy, and externally received power respectively; α0, β0, γ0, and δ0 are intercept terms; α1, β1, γ1, and δ1 are used to reflect the substitution effects of wind power technology on coal - fired power, gas - fired power, biomass energy, and externally received power; Combining historical power generation granularity data and the similar uncorrelated regression model to obtain a marginal coefficient; wherein the marginal coefficient is a model parameter of the similar uncorrelated regression model; Calculating the marginal carbon emission of power generation according to the carbon emission intensities of coal-fired power, gas-fired power, biomass energy, and externally received power and the marginal coefficient; wherein, the marginal carbon emission MEF of power generation is calculated by the following formula: MEF = e coal α2 + e gas β2 + e biomass γ2 + e import δ2; Among them, e coal is the carbon emission intensity of coal-fired power; e gas is the carbon emission intensity of gas-fired power; e biomass is the carbon emission intensity of biomass energy; e import is the carbon emission intensity of externally received electricity; α2, β2, γ2 and δ2 are the marginal coefficients.

2. A measurement device for marginal carbon emissions, characterized in that Including: A model establishment module for establishing a similar uncorrelated regression model, wherein the similar uncorrelated regression model includes a coal-fired power equation, a gas-fired power equation, a biomass energy equation, and an externally received power equation; The model establishment module is further configured to respectively calculate the coal-fired power equation, the gas-fired power equation, the biomass energy equation, and the externally received power equation: where t represents hours; Δ is the first difference, Coal t , Gas, Biomass t and Import t are the hourly power generation of in - province coal - fired power, gas - fired power, biomass energy, and externally received power respectively; Wind t and Demand t are the hourly wind power generation and the electricity demand of the market respectively, X t is other relevant control variable, and are the disturbance terms of coal - fired power, gas - fired power, biomass energy, and externally received power respectively; θ coal , θ gas , θ biomass and θ import are the other control variables of coal - fired power, gas - fired power, biomass energy, and externally received power respectively; α0, β0, γ0, and δ0 are intercept terms; α1, β1, γ1, and δ1 are used to reflect the substitution effects of wind power technology on coal - fired power, gas - fired power, biomass energy, and externally received power; A marginal coefficient module for combining historical power generation granularity data and the similar uncorrelated regression model to obtain a marginal coefficient; wherein the marginal coefficient is a model parameter of the similar uncorrelated regression model; A calculation module for calculating the marginal carbon emission of power generation according to the carbon emission intensities of coal-fired power, gas-fired power, biomass energy, and externally received power and the marginal coefficient; wherein, the marginal carbon emission MEF of power generation is calculated by the following formula: MEF = e coal α2 + e gas β2 + β biomass γ2 + e import δ2; Among them, β coal is the carbon emission intensity of coal-fired power; β gas is the carbon emission intensity of gas-fired power; β biomass is the carbon emission intensity of biomass energy; β import is the carbon emission intensity of externally received electricity; α2, β2, γ2, and δ2 are the marginal coefficients.

3. A measurement device for marginal carbon emissions, characterized in that, Including a memory and a processor, the memory is connected to the processor, the memory stores a computer program, and when the computer program is executed by the processor, the measurement method of the marginal carbon emission described in claim 1 is implemented.

4. A computer-readable storage medium, characterized in that, Stored with a computer program, when the computer program is executed, the measurement method of the marginal carbon emission described in claim 1 is implemented.

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