A marginal carbon emission heterogeneity analysis method and system

Abstract

The present invention provides a method and system for analyzing the heterogeneity of marginal carbon emissions. The method includes: establishing an uncorrelated regression model for coal-fired power generation, an uncorrelated regression model for gas-fired power generation, an uncorrelated regression model for biomass energy, and an uncorrelated regression model for external power generation; calculating parameters of the uncorrelated regression model for coal-fired power generation, gas-fired power generation, biomass energy, and external power generation based on historical data; and calculating marginal carbon emissions under heterogeneous power generation scenarios. The present invention analyzes the impact of different factors on marginal carbon emissions, improving the accuracy of the analysis results.

Description

Technical Field

[0001] The present invention relates to the technical field of electricity markets, and in particular to a marginal carbon emission heterogeneity analysis method and system. Background Art

[0002] As the power system transitions towards marketization and clean energy, the factors influencing marginal carbon emissions in the power system and the corresponding influencing directions are receiving increasing attention. Currently, there is no general research tool for the heterogeneity of marginal carbon emissions in the power system. Adding dummy variables to econometric models to control the impact of the presence or absence of such factors on the dependent variable cannot be directly applied to the analysis of marginal carbon emissions heterogeneity in the power market. This is because:

[0003] 1. The electricity market has certain unique characteristics. It has numerous market players: power generation companies, grid companies, retailers, and users; a rich variety of market types: wholesale markets, retail markets, ancillary services markets, capacity markets, carbon markets, and financial markets, all of which are being piloted; complex market trading rules: bilateral negotiations, medium- and long-term transactions, spot trading, options and futures; and a unique product: electricity is both a commodity and a vital foundation for macroeconomic operations and the quality of life of residents. This unique nature of the product also creates a unique dual-track system in the electricity market. As such a complex product, the heterogeneity of its marginal carbon emissions cannot be simply measured using dummy variables; rather, a systematic and identifiable econometric model is required.

[0004] 2. There are gaps in the calculation and analysis of carbon emissions from power systems. Existing research on marginal carbon emissions mainly focuses on numerical accounting, but no one has conducted heterogeneity analysis of marginal carbon emissions. In fact, compared with accounting, analysis of factors affecting marginal carbon emissions will have more important reference significance for policy formulation and power system investment planning. For example, when a company saves energy and reduces emissions by reducing demand, for the whole society, the reduced carbon emissions should be marginal carbon emissions rather than average carbon emissions or the corresponding carbon emissions of the generators connected to the transmission lines. In addition, the calculation of reduced carbon emissions should take into account temporal heterogeneity - the effect of emission reduction at different times of the day and year varies depending on the power generation structure. Summary of the Invention

[0005] In order to solve the above problems in the prior art, the present invention provides a marginal carbon emission heterogeneity analysis method and system to analyze the impact of different factors on marginal carbon emissions.

[0006] A first aspect of the present invention provides a method for analyzing marginal carbon emission heterogeneity, comprising:

[0007] Obtain hourly coal-fired power generation, hourly gas-fired power generation, hourly biomass power generation, hourly external power generation, and hourly market power demand;

[0008] Establishing an uncorrelated regression model for coal-fired power, an uncorrelated regression model for gas-fired power, an uncorrelated regression model for biomass energy, and an uncorrelated regression model for external power supply, respectively, based on the hourly coal-fired power generation, hourly wind power generation, hourly biomass power generation, hourly external power generation, and hourly market power demand;

[0009] Based on historical data, calculate the parameters of the unrelated regression model for coal-fired power, the parameters of the unrelated regression model for gas-fired power, the parameters of the unrelated regression model for biomass energy, and the parameters of the unrelated regression model for external power supply;

[0010] The marginal carbon emissions under the heterogeneous scenarios of power generation are calculated based on the parameters of the uncorrelated regression model of coal power, the parameters of the uncorrelated regression model of gas power, the parameters of the uncorrelated regression model of biomass energy, and the parameters of the uncorrelated regression model of external power supply.

[0011] Furthermore, the marginal carbon emissions under the heterogeneous scenario of power generation are calculated by the following formula:

[0012]

[0013] Among them, MEF i represents the marginal emissions under heterogeneous scenarios, e coal represents the interference value of coal power, α2 represents the parameter of the uncorrelated regression model of coal power, α 3,i Indicates the load, represents an exogenous variable, e gas represents the gas-electric interference value, β2 represents the parameter of the gas-electric uncorrelated regression model, β 3,i represents the seasonal parameter, e biomass represents the biomass energy interference value, γ2 represents the parameter of the uncorrelated regression model of biomass energy, γ 3,i Indicates the year parameter, e imp ort represents the external power interference value, δ2 represents the parameter of the irrelevant regression model of external power, δ 3,i Represents the extreme value shock parameter.

[0014] Furthermore, the uncorrelated regression model of coal power is expressed by the following formula:

[0015]

[0016] Among them, ΔCoal trepresents the uncorrelated first-order difference value of coal-fired power, α0 represents the first-order coefficient of coal-fired power, α1 represents the coefficient of coal-fired power generation, ΔWind t represents the hourly wind power generation, α2 represents the parameter of the uncorrelated regression model of coal power, ΔDemand t represents the hourly market electricity demand, θ coal The coefficient representing the coal power control parameter, X t represents the control parameter, α 3,i Indicates the load, represents an exogenous variable, Represents the parameters of coal-fired power interference.

[0017] Furthermore, the uncorrelated regression model of gas and electricity is expressed by the following formula:

[0018]

[0019] Among them, ΔGas t represents the uncorrelated first-order difference value of gas-fired power, β0 represents the first-order coefficient of gas-fired power, β1 represents the coefficient of gas-fired power generation, ΔWind t represents the hourly wind power generation, β2 represents the parameter of the uncorrelated regression model of gas power, ΔDemand t represents the hourly market electricity demand, θ gas The coefficient representing the gas-electric control parameter, X t represents the control parameter, β 3,i represents the seasonal parameter, represents an exogenous variable, Indicates the parameters of gas-electric interference items.

[0020] Furthermore, the uncorrelated regression model of biomass energy is expressed by the following formula:

[0021]

[0022] Among them, ΔBiomass t represents the uncorrelated first-order difference value of biomass power generation, γ0 represents the first-order coefficient of biomass power generation, γ1 represents the coefficient of biomass power generation, ΔWind t represents the hourly wind power generation, γ2 represents the parameter of the uncorrelated regression model of biomass power generation, ΔDemand t represents the hourly market electricity demand, θ biomass The coefficient representing the control parameter of biomass power generation, X t represents the control parameter, γ 3,i Represents the year parameter, represents an exogenous variable, represents the interference parameter of biomass power generation;

[0023] The uncorrelated regression model of external power supply is expressed by the following formula:

[0024]

[0025] Among them, ΔImport t represents the uncorrelated first-order difference value of external power, δ0 represents the first-order coefficient of external power, δ1 represents the coefficient of external power generation, ΔWind t represents the hourly wind power generation, δ2 represents the parameter of the uncorrelated regression model of external power supply, ΔDemand t represents the hourly market electricity demand, θ import The coefficient representing the external power control parameter, X t represents the control parameter, δ 3,i represents the extreme value shock parameter, represents an exogenous variable, Indicates the parameters of external electrical interference.

[0026] A second aspect of the present invention provides a marginal carbon emission heterogeneity analysis system, comprising:

[0027] A data acquisition module is used to obtain hourly coal-fired power generation, hourly gas-fired power generation, hourly biomass power generation, hourly external power generation, and hourly market power demand;

[0028] a model building module for respectively building an uncorrelated regression model of coal-fired power, an uncorrelated regression model of gas-fired power, an uncorrelated regression model of biomass energy, and an uncorrelated regression model of external power supply based on the hourly coal-fired power generation, hourly wind power generation, hourly biomass power generation, hourly external power supply generation, and hourly market power demand;

[0029] A parameter calculation module is used to calculate the parameters of the unrelated regression model of coal power, the parameters of the unrelated regression model of gas power, the parameters of the unrelated regression model of biomass energy, and the parameters of the unrelated regression model of external power based on historical data;

[0030] The marginal carbon emission calculation module is used to calculate the marginal carbon emissions under the heterogeneous scenario of power generation based on the parameters of the uncorrelated regression model of coal power, the parameters of the uncorrelated regression model of gas power, the parameters of the uncorrelated regression model of biomass energy and the parameters of the uncorrelated regression model of external power supply.

[0031] Furthermore, the marginal carbon emissions under the heterogeneous scenario of power generation are calculated by the following formula:

[0032]

[0033] Among them, MEF i represents the marginal emissions under heterogeneous scenarios, e coal represents the interference value of coal power, α2 represents the parameter of the uncorrelated regression model of coal power, α 3,i Indicates the load, represents an exogenous variable, e gas represents the gas-electric interference value, β2 represents the parameter of the gas-electric uncorrelated regression model, β 3,i represents the seasonal parameter, e biomass represents the biomass energy interference value, γ2 represents the parameter of the uncorrelated regression model of biomass energy, γ 3,i Indicates the year parameter, e import represents the external power interference value, δ2 represents the parameter of the irrelevant regression model of external power, δ 3,i Represents the extreme value shock parameter.

[0034] Furthermore, the uncorrelated regression model of coal power is expressed by the following formula:

[0035]

[0036] Among them, ΔCoal t represents the uncorrelated first-order difference value of coal-fired power, α0 represents the first-order coefficient of coal-fired power, α1 represents the coefficient of coal-fired power generation, ΔWind t represents the hourly wind power generation, α2 represents the parameter of the uncorrelated regression model of coal power, ΔDemand t represents the hourly market electricity demand, θ coal The coefficient representing the coal power control parameter, X t represents the control parameter, α 3,i Indicates the load, represents an exogenous variable, Represents the parameters of coal-fired power interference.

[0037] Furthermore, the uncorrelated regression model of gas and electricity is expressed by the following formula:

[0038]

[0039]

[0040] Among them, ΔGas t represents the uncorrelated first-order difference value of gas-fired power, β0 represents the first-order coefficient of gas-fired power, β1 represents the coefficient of gas-fired power generation, ΔWind t represents the hourly wind power generation, β2 represents the parameter of the uncorrelated regression model of gas power, ΔDemand t represents the hourly market electricity demand, θ gasThe coefficient representing the gas-electric control parameter, X t represents the control parameter, β 3,i represents the seasonal parameter, represents an exogenous variable, Indicates the parameters of gas-electric interference items.

[0041] Furthermore, the uncorrelated regression model of biomass energy is expressed by the following formula:

[0042]

[0043] Among them, ΔBiomass t represents the uncorrelated first-order difference value of biomass power generation, γ0 represents the first-order coefficient of biomass power generation, γ1 represents the coefficient of biomass power generation, ΔWind t represents the hourly wind power generation, γ2 represents the parameter of the uncorrelated regression model of biomass power generation, ΔDemand t represents the hourly market electricity demand, θ biomass The coefficient representing the control parameter of biomass power generation, X t represents the control parameter, γ 3,i Represents the year parameter, represents an exogenous variable, represents the interference parameter of biomass power generation;

[0044] The uncorrelated regression model of external power supply is expressed by the following formula:

[0045]

[0046] Among them, ΔImport t represents the uncorrelated first-order difference value of external power, δ0 represents the first-order coefficient of external power, δ1 represents the coefficient of external power generation, ΔWind t represents the hourly wind power generation, δ2 represents the parameter of the uncorrelated regression model of external power supply, ΔDemand t represents the hourly market electricity demand, θ import The coefficient representing the external power control parameter, X t represents the control parameter, δ 3,i represents the extreme value shock parameter, represents an exogenous variable, Indicates the parameters of external electrical interference.

[0047] Compared with the prior art, the embodiments of the present invention have the following advantages:

[0048] The present invention provides a marginal carbon emission heterogeneity analysis method and system, wherein the method comprises: obtaining hourly coal-fired power generation, hourly gas-fired power generation, hourly biomass power generation, hourly external power generation and hourly market power demand; establishing an independent regression model of coal-fired power generation, an independent regression model of gas-fired power generation, an independent regression model of biomass energy and an independent regression model of external power generation according to the hourly coal-fired power generation, hourly wind power generation, hourly biomass power generation, hourly external power generation and hourly market power demand; calculating parameters of the independent regression model of coal-fired power generation, parameters of the independent regression model of gas-fired power generation, parameters of the independent regression model of biomass energy and parameters of the independent regression model of external power generation according to historical data; and calculating marginal carbon emissions under heterogeneous power generation scenarios according to the parameters of the independent regression model of coal-fired power generation, parameters of the independent regression model of gas-fired power generation, parameters of the independent regression model of biomass energy and parameters of the independent regression model of external power generation. The present invention realizes analysis of the influence of different factors on marginal carbon emissions, thereby improving the accuracy of the analysis results. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solution of the present invention, the following is a brief introduction to the drawings required for use in the implementation. Obviously, the drawings described below are only some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0050] Figure 1 This is a flow chart of a marginal carbon emission heterogeneity analysis method provided by one embodiment of the present invention;

[0051] Figure 2 This is a diagram of a marginal carbon emission heterogeneity analysis system provided by one embodiment of the present invention;

[0052] Figure 3 This is a structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0054] It should be understood that the step numbers used herein are only for convenience of description and are not intended to limit the order in which the steps are to be executed.

[0055] It should be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, the singular forms "a", "an" and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0056] The terms “include” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0057] The term "and / or" refers to and includes any and all possible combinations of one or more of the associated listed items.

[0058] The first aspect.

[0059] See also Figure 1 An embodiment of the present invention provides a method for analyzing marginal carbon emission heterogeneity, comprising:

[0060] S10. Obtain hourly coal-fired power generation, hourly gas-fired power generation, hourly biomass power generation, hourly external power generation, and hourly market power demand.

[0061] S20. Establish an uncorrelated regression model for coal-fired power, an uncorrelated regression model for gas-fired power, an uncorrelated regression model for biomass energy, and an uncorrelated regression model for external power supply based on the hourly coal-fired power generation, hourly wind power generation, hourly biomass power generation, hourly external power generation, and hourly market power demand, respectively.

[0062] Preferably, the uncorrelated regression model of coal power is expressed by the following formula:

[0063]

[0064] Among them, ΔCoal t represents the uncorrelated first-order difference value of coal-fired power, α0 represents the first-order coefficient of coal-fired power, α1 represents the coefficient of coal-fired power generation, ΔWind t represents the hourly wind power generation, α2 represents the parameter of the uncorrelated regression model of coal power, ΔDemand t represents the hourly market electricity demand, θ coal The coefficient representing the coal power control parameter, X t represents the control parameter, α 3,i Indicates the load, represents an exogenous variable, Represents the parameters of coal-fired power interference.

[0065] The uncorrelated regression model of gas and electricity is expressed by the following formula:

[0066]

[0067] Among them, ΔGas t represents the uncorrelated first-order difference value of gas-fired power, β0 represents the first-order coefficient of gas-fired power, β1 represents the coefficient of gas-fired power generation, ΔWind t represents the hourly wind power generation, β2 represents the parameter of the uncorrelated regression model of gas power, ΔDemand t represents the hourly market electricity demand, θ gas The coefficient representing the gas-electric control parameter, X t represents the control parameter, β 3,i represents the seasonal parameter, represents an exogenous variable, Indicates the parameters of gas-electric interference items.

[0068] The uncorrelated regression model of biomass energy is expressed by the following formula:

[0069]

[0070] Among them, ΔBiomass t represents the uncorrelated first-order difference value of biomass power generation, γ0 represents the first-order coefficient of biomass power generation, γ1 represents the coefficient of biomass power generation, ΔWind t represents the hourly wind power generation, γ2 represents the parameter of the uncorrelated regression model of biomass power generation, ΔDemand t represents the hourly market electricity demand, θ biomass The coefficient representing the control parameter of biomass power generation, X t represents the control parameter, γ 3,i Represents the year parameter, represents an exogenous variable, Represents the interference parameter of biomass power generation.

[0071] The uncorrelated regression model of external power supply is expressed by the following formula:

[0072]

[0073] Among them, ΔImport t represents the uncorrelated first-order difference value of external power, δ0 represents the first-order coefficient of external power, δ1 represents the coefficient of external power generation, ΔWind t represents the hourly wind power generation, δ2 represents the parameter of the uncorrelated regression model of external power supply, ΔDemandt represents the hourly market electricity demand, γ import The coefficient representing the external power control parameter, X t represents the control parameter, δ 3,i represents the extreme value shock parameter, represents an exogenous variable, Indicates the parameters of external electrical interference.

[0074] S30. Calculate, based on historical data, parameters of an uncorrelated regression model for coal-fired power, parameters of an uncorrelated regression model for gas-fired power, parameters of an uncorrelated regression model for biomass energy, and parameters of an uncorrelated regression model for external power supply.

[0075] S40. Calculate marginal carbon emissions under heterogeneous power generation scenarios based on the parameters of the uncorrelated regression model for coal-fired power, the parameters of the uncorrelated regression model for gas-fired power, the parameters of the uncorrelated regression model for biomass energy, and the parameters of the uncorrelated regression model for external power supply.

[0076] Preferably, the marginal carbon emissions under the heterogeneous scenario of power generation are calculated by the following formula:

[0077]

[0078] Among them, MEF i represents the marginal emissions under heterogeneous scenarios, e coal represents the interference value of coal power, α2 represents the parameter of the uncorrelated regression model of coal power, α 3,i Indicates the load, represents an exogenous variable, e gas represents the gas-electric interference value, β2 represents the parameter of the gas-electric uncorrelated regression model, β 3,i represents the seasonal parameter, e biomass represents the biomass energy interference value, γ2 represents the parameter of the uncorrelated regression model of biomass energy, γ 3,i Indicates the year parameter, e import represents the external power interference value, δ2 represents the parameter of the irrelevant regression model of external power, δ 3,i Represents the extreme value shock parameter.

[0079] The method provided by the present invention realizes analysis of the impact of different factors on marginal carbon emissions, thereby improving the accuracy of the analysis results.

[0080] Another embodiment of the present invention provides a method for analyzing marginal carbon emission heterogeneity, comprising:

[0081] Step 1: For different power supply types, establish the following approximately uncorrelated (SURE) regression model:

[0082]

[0083]

[0084]

[0085]

[0086] Where t represents hours, Δ is the first-order difference, Coal t 、Gas、Biomass t and Import t The hourly power generation of coal-fired power, gas-fired power, biomass energy and external power respectively in the province, Wind t and Demand t are the hourly wind power generation and market electricity demand, X t Other relevant control variables can be dummy variables for units or hours to capture individual fixed effects and time fixed effects. It is a dummy variable. It takes the value of 1 when it is impacted by some exogenous variable and takes the value of 0 otherwise. is the interaction term between demand difference and exogenous shock, and the coefficient of this term is α 3,i , β 3,i , γ 3,i , δ 3,i It reflects the impact of factors such as load, season, year, extreme value shock and policy changes on generators of different power types. The larger the estimated value of these coefficients, the greater the impact of the corresponding load, season, year, extreme value shock and policy changes on ΔDemand t ΔCoal t For example, if the coefficient corresponding to winter in the first row of regression is larger, it means that the demand for coal-fired power will be more responsive to the increase in winter. In this case, since coal-fired power emits more carbon dioxide, the marginal carbon emissions will be greater.

[0087] at last, The SURE model restricts the correlation between interference terms within the same equation to zero and allows for correlation between interference terms across equations, which is more consistent with the actual situation of coordinated power generation of different types of generators.

[0088] Step 2: Substitute the data and estimate the parameters in the regression equation:

[0089] The regression parameters are obtained by using the 15-minute plant-level historical power generation granularity data of Guangdong Province, equipped with relevant data processing software (stata, spss, etc.), inputting the code.

[0090] Step 3: Analyze regression parameters:

[0091] The marginal coefficients (α2, γ2, γ2, δ2) estimated based on the SURE model and the carbon emission intensity of coal-fired power, gas-fired power, biomass energy and external power supply (denoted as e i , where i∈{coal,gas,biomass,impoet}).

[0092] The marginal carbon emissions under the heterogeneous scenario of power generation are calculated using the following formula:

[0093]

[0094] Among them, heterogeneity is reflected in the superscript i, MEF i Refers to the marginal emission factor (MarginalEmission Factor) under heterogeneous situations.

[0095] Furthermore, the coefficients α3, β3, γ3, and δ3 reflect the impact of factors such as load, season, year, extreme value shocks, and policy changes on marginal carbon emissions.

[0096] This paper designs a generalized research tool for analyzing the heterogeneity of marginal carbon emissions in power systems. By incorporating the influence of a certain factor into the model, the impact of that factor on marginal carbon emissions can be intuitively analyzed.

[0097] In the process of heterogeneity analysis, the present invention distinguishes the impact paths in more detail, and uses the SURE model to subdivide how different factors affect marginal emissions by affecting coal-fired units, gas-fired units, biomass units and net imports.

[0098] This invention takes into account the particularities of the power industry and is applicable to the heterogeneity analysis of all exogenous factors that affect marginal carbon emissions. Load shocks will change marginal carbon emissions by changing marginal units. Seasonal factors will affect the power generation of renewable energy. For example, the output of solar units is higher in summer. At this time, the matching between the output and load of the power system will change, which in turn affects marginal carbon emissions. The year factor can capture the time-varying factors that affect marginal carbon emissions. Extreme value shocks reflect the impact on marginal carbon emissions when at least one of the power supply and demand has extreme values. Due to the basic commodity properties of electricity, the power system basically needs to maintain a real-time balance between supply and demand. When extreme values ​​occur, the scheduling arrangements of units will be very different, which will also have an impact on marginal carbon emissions. Finally, some policy changes will also affect marginal carbon emissions, such as the proposal of the "carbon neutrality" goal, the establishment of the carbon market, the establishment of the spot market, etc.

[0099] The second aspect.

[0100] See also Figure 2 An embodiment of the present invention provides a marginal carbon emission heterogeneity analysis system, comprising:

[0101] The data acquisition module 10 is used to obtain the hourly coal-fired power generation, hourly gas-fired power generation, hourly biomass power generation, hourly external power generation and hourly market power demand.

[0102] The model building module 20 is used to establish an uncorrelated regression model for coal-fired power, an uncorrelated regression model for gas-fired power, an uncorrelated regression model for biomass energy, and an uncorrelated regression model for external power supply based on the hourly coal-fired power generation, hourly wind power generation, hourly biomass power generation, hourly external power generation, and hourly market power demand.

[0103] Preferably, the uncorrelated regression model of coal power is expressed by the following formula:

[0104]

[0105] Among them, ΔCoal t represents the uncorrelated first-order difference value of coal-fired power, α0 represents the first-order coefficient of coal-fired power, α1 represents the coefficient of coal-fired power generation, ΔWind t represents the hourly wind power generation, α2 represents the parameter of the uncorrelated regression model of coal power, ΔDemand t represents the hourly market electricity demand, θ coal The coefficient representing the coal power control parameter, X t represents the control parameter, α 3,i Indicates the load, represents an exogenous variable, Represents the parameters of coal-fired power interference.

[0106] The uncorrelated regression model of gas and electricity is expressed by the following formula:

[0107]

[0108] Among them, ΔGas t represents the uncorrelated first-order difference value of gas-fired power, β0 represents the first-order coefficient of gas-fired power, β1 represents the coefficient of gas-fired power generation, ΔWind t represents the hourly wind power generation, β2 represents the parameter of the uncorrelated regression model of gas power, ΔDemand t represents the hourly market electricity demand, θ gas The coefficient representing the gas-electric control parameter, X t represents the control parameter, β 3,i represents the seasonal parameter, represents an exogenous variable, Indicates the parameters of gas-electric interference items.

[0109] The uncorrelated regression model of biomass energy is expressed by the following formula:

[0110]

[0111] Among them, ΔBiomass t represents the uncorrelated first-order difference value of biomass power generation, γ0 represents the first-order coefficient of biomass power generation, γ1 represents the coefficient of biomass power generation, ΔWind t represents the hourly wind power generation, γ2 represents the parameter of the uncorrelated regression model of biomass power generation, ΔDemand t represents the hourly market electricity demand, θ biomass The coefficient representing the control parameter of biomass power generation, X t represents the control parameter, γ 3,i Represents the year parameter, represents an exogenous variable, Represents the interference parameter of biomass power generation.

[0112] The uncorrelated regression model of external power supply is expressed by the following formula:

[0113]

[0114] Among them, ΔImport t represents the uncorrelated first-order difference value of external power, δ0 represents the first-order coefficient of external power, δ1 represents the coefficient of external power generation, ΔWind t represents the hourly wind power generation, δ2 represents the parameter of the uncorrelated regression model of external power supply, ΔDemand t represents the hourly market electricity demand, θ import The coefficient representing the external power control parameter, X t represents the control parameter, δ 3,i represents the extreme value shock parameter, represents an exogenous variable, Indicates the parameters of external electrical interference.

[0115] The parameter calculation module 30 is used to calculate the parameters of the independent regression model of coal power, the parameters of the independent regression model of gas power, the parameters of the independent regression model of biomass energy and the parameters of the independent regression model of external power based on historical data.

[0116] The marginal carbon emission calculation module 40 is used to calculate the marginal carbon emissions under the heterogeneous scenario of power generation based on the parameters of the unrelated regression model of coal power, the parameters of the unrelated regression model of gas power, the parameters of the unrelated regression model of biomass energy and the parameters of the unrelated regression model of external power supply.

[0117] Preferably, the marginal carbon emissions under the heterogeneous scenario of power generation are calculated by the following formula:

[0118]

[0119] Among them, MEF i represents the marginal emissions under heterogeneous scenarios, e coal represents the interference value of coal power, α2 represents the parameter of the uncorrelated regression model of coal power, α 3,i Indicates the load, represents an exogenous variable, e gas represents the gas-electric interference value, β2 represents the parameter of the gas-electric uncorrelated regression model, β 3,i represents the seasonal parameter, e biomass represents the biomass energy interference value, γ2 represents the parameter of the uncorrelated regression model of biomass energy, γ 3,i Indicates the year parameter, e impirt represents the external power interference value, δ2 represents the parameter of the irrelevant regression model of external power, δ 3,i Represents the extreme value shock parameter.

[0120] The system provided by the present invention realizes analysis of the impact of different factors on marginal carbon emissions, thereby improving the accuracy of the analysis results.

[0121] The third aspect.

[0122] The present invention provides an electronic device, comprising:

[0123] processor, memory, and bus;

[0124] The bus is used to connect the processor and the memory;

[0125] The memory is used to store operation instructions;

[0126] The processor is used to call the operation instruction, and the executable instruction enables the processor to perform operations corresponding to a marginal carbon emission heterogeneity analysis method as shown in the first aspect of the present application.

[0127] In an alternative embodiment, an electronic device is provided, such as Figure 3 As shown, Figure 3 The electronic device 5000 shown includes: a processor 5001 and a memory 5003. The processor 5001 and the memory 5003 are connected, for example, via a bus 5002. Optionally, the electronic device 5000 may further include a transceiver 5004. It should be noted that in actual applications, the number of transceivers 5004 is not limited to one, and the structure of the electronic device 5000 does not constitute a limitation on the embodiments of the present application.

[0128] Processor 5001 may be a CPU, a general-purpose processor, a DSP, an ASIC, an FPGA, or other programmable logic device, a transistor logic device, a hardware component, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 5001 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like.

[0129] The bus 5002 may include a path for transmitting information between the above components. The bus 5002 may be a PCI bus or an EISA bus, etc. The bus 5002 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0130] The memory 5003 may be a ROM or other type of static storage device that can store static information and instructions, a RAM or other type of dynamic storage device that can store information and instructions, or an EEPROM, a CD-ROM or other optical disk storage, an optical disc storage (including a compact disc, a laser disc, an optical disc, a digital versatile disc, a Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to these.

[0131] The memory 5003 is used to store application code for executing the solution of the present application, and the execution is controlled by the processor 5001. The processor 5001 is used to execute the application code stored in the memory 5003 to implement the content shown in any of the above method embodiments.

[0132] Among them, electronic devices include but are not limited to: mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc.

[0133] The fourth aspect.

[0134] The present invention provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the method for analyzing the heterogeneity of marginal carbon emissions as shown in the first aspect of the present application is implemented.

[0135] Another embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer-readable storage medium is run on a computer, the computer can execute the corresponding contents of the aforementioned method embodiments.

Claims

1. A method for analyzing marginal carbon emission heterogeneity, characterized in that: include: Obtain hourly coal-fired power generation, hourly gas-fired power generation, hourly biomass power generation, hourly external power generation, and hourly market power demand; Establishing an uncorrelated regression model for coal-fired power, an uncorrelated regression model for gas-fired power, an uncorrelated regression model for biomass energy, and an uncorrelated regression model for external power supply, respectively, based on the hourly coal-fired power generation, hourly wind power generation, hourly biomass power generation, hourly external power generation, and hourly market power demand; Based on historical data, calculate the parameters of the unrelated regression model for coal-fired power, the parameters of the unrelated regression model for gas-fired power, the parameters of the unrelated regression model for biomass energy, and the parameters of the unrelated regression model for external power supply; The marginal carbon emissions under the heterogeneous scenarios of power generation are calculated based on the parameters of the uncorrelated regression model of coal power, the parameters of the uncorrelated regression model of gas power, the parameters of the uncorrelated regression model of biomass energy, and the parameters of the uncorrelated regression model of external power supply.

2. A marginal carbon emission heterogeneity analysis method according to claim 1, characterized in that: The marginal carbon emissions under the heterogeneous scenario of power generation are calculated by the following formula: in, represents the marginal emissions under heterogeneous scenarios, represents the coal-fired power interference value, represents the parameters of the uncorrelated regression model for coal power, Indicates the load, represents an exogenous variable, Indicates the gas-electric interference value, represents the parameters of the uncorrelated regression model of gas and electricity, represents the seasonal parameter, represents the biomass interference value, represents the parameters of the uncorrelated regression model for biomass energy, Represents the year parameter, Indicates the external power interference value. represents the parameters of the uncorrelated regression model for external power supply, Represents the extreme value shock parameter.

3. A marginal carbon emission heterogeneity analysis method according to claim 1, characterized in that: The uncorrelated regression model of coal power is expressed by the following formula: ； in, represents the uncorrelated first-order difference value of coal-fired power, represents the first-order coefficient of coal-fired power, The coefficient representing coal-fired power generation, represents the hourly wind power generation, represents the parameters of the uncorrelated regression model for coal power, represents the hourly market electricity demand, represents the coefficient of coal power control parameters, represents the control parameter, Indicates the load, represents an exogenous variable, Represents the parameters of coal-fired power interference.

4. A marginal carbon emission heterogeneity analysis method according to claim 1, characterized in that: The uncorrelated regression model of gas and electricity is expressed by the following formula: ； in, represents the uncorrelated first-order difference value of gas and electricity, represents the first-order coefficient of gas electricity, The coefficient representing the amount of gas-fired power generation, represents the hourly wind power generation, represents the parameters of the uncorrelated regression model of gas and electricity, represents the hourly market electricity demand, The coefficients representing the gas-electric control parameters, represents the control parameter, represents the seasonal parameter, represents an exogenous variable, Indicates the parameters of gas-electric interference items.

5. A marginal carbon emission heterogeneity analysis method according to claim 1, characterized in that: The uncorrelated regression model of biomass energy is expressed by the following formula: ； in, represents the uncorrelated first-order difference value of biomass power generation, represents the first-order coefficient of biomass power generation, The coefficient representing the power generation capacity of biomass power generation, represents the hourly wind power generation, represents the parameters of the uncorrelated regression model for biomass power generation, represents the hourly market electricity demand, represents the coefficient of the biomass power generation control parameter, represents the control parameter, Represents the year parameter, represents an exogenous variable, represents the interference parameter of biomass power generation; The uncorrelated regression model of external power supply is expressed by the following formula: ； in, represents the uncorrelated first-order difference value of external power, represents the first-order coefficient of external power, The coefficient representing the amount of external power generation, represents the hourly wind power generation, represents the parameters of the uncorrelated regression model for external power supply, represents the hourly market electricity demand, The coefficient representing the external power control parameter, represents the control parameter, represents the extreme value shock parameter, represents an exogenous variable, Indicates the parameters of external electrical interference.

6. A marginal carbon emission heterogeneity analysis system, characterized in that: include: A data acquisition module is used to obtain hourly coal-fired power generation, hourly gas-fired power generation, hourly biomass power generation, hourly external power generation, and hourly market power demand; a model building module for respectively building an uncorrelated regression model of coal-fired power, an uncorrelated regression model of gas-fired power, an uncorrelated regression model of biomass energy, and an uncorrelated regression model of external power supply based on the hourly coal-fired power generation, hourly wind power generation, hourly biomass power generation, hourly external power supply generation, and hourly market power demand; A parameter calculation module is used to calculate the parameters of the unrelated regression model of coal power, the parameters of the unrelated regression model of gas power, the parameters of the unrelated regression model of biomass energy, and the parameters of the unrelated regression model of external power based on historical data; The marginal carbon emission calculation module is used to calculate the marginal carbon emissions under the heterogeneous scenario of power generation based on the parameters of the uncorrelated regression model of coal power, the parameters of the uncorrelated regression model of gas power, the parameters of the uncorrelated regression model of biomass energy and the parameters of the uncorrelated regression model of external power supply.

7. A marginal carbon emission heterogeneity analysis system according to claim 6, characterized in that: The marginal carbon emissions under the heterogeneous scenario of power generation are calculated by the following formula: in, represents the marginal emissions under heterogeneous scenarios, represents the coal-fired power interference value, represents the parameters of the uncorrelated regression model for coal power, Indicates the load, represents an exogenous variable, Indicates the gas-electric interference value, represents the parameters of the uncorrelated regression model of gas and electricity, represents the seasonal parameter, represents the biomass interference value, Represents the year parameter, Indicates the external power interference value. represents the parameters of the uncorrelated regression model for external power supply, Represents the extreme value shock parameter.

8. A marginal carbon emission heterogeneity analysis system according to claim 6, characterized in that: The uncorrelated regression model of coal power is expressed by the following formula: ； in, represents the uncorrelated first-order difference value of coal-fired power, represents the first-order coefficient of coal-fired power, The coefficient representing the coal-fired power generation, represents the hourly wind power generation, represents the parameters of the uncorrelated regression model for coal power, represents the hourly market electricity demand, represents the coefficient of coal power control parameters, represents the control parameter, Indicates the load, represents an exogenous variable, Represents the parameters of coal-fired power interference.

9. The marginal carbon emission heterogeneity analysis system according to claim 6, characterized in that: The uncorrelated regression model of gas and electricity is expressed by the following formula: ； in, represents the uncorrelated first-order difference value of gas and electricity, represents the first-order coefficient of gas electricity, The coefficient representing the amount of gas-fired power generation, represents the hourly wind power generation, represents the parameters of the uncorrelated regression model of gas and electricity, represents the hourly market electricity demand, The coefficients representing the gas-electric control parameters, represents the control parameter, represents the seasonal parameter, represents an exogenous variable, Indicates the parameters of gas-electric interference items.

10. The marginal carbon emission heterogeneity analysis system according to claim 6, characterized in that: The uncorrelated regression model of biomass energy is expressed by the following formula: ； in, represents the uncorrelated first-order difference value of biomass power generation, represents the first-order coefficient of biomass power generation, The coefficient representing the power generation capacity of biomass power generation, represents the hourly wind power generation, represents the parameters of the uncorrelated regression model for biomass power generation, represents the hourly market electricity demand, represents the coefficient of the biomass power generation control parameter, represents the control parameter, Represents the year parameter, represents an exogenous variable, represents the interference parameter of biomass power generation; The uncorrelated regression model of external power supply is expressed by the following formula: ； in, represents the uncorrelated first-order difference value of external power, represents the first-order coefficient of external power, The coefficient representing the amount of external power generation, represents the hourly wind power generation, represents the parameters of the uncorrelated regression model for external power supply, represents the hourly market electricity demand, The coefficient representing the external power control parameter, represents the control parameter, represents the extreme value shock parameter, represents an exogenous variable, Indicates the parameters of external electrical interference.

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