Method and device for power industry carbon emission budgeting and dynamic optimization

Abstract

The application discloses a kind of power industry carbon emission budget formulation and dynamic optimization method and device.The method includes: obtaining the basic data corresponding to target area electric power industry in benchmark year and prediction year, based on LEAP energy environment scenario model, direct emission accounting model is established, and the thermal power generation, energy intensity and carbon emission budget amount of benchmark year and prediction year are obtained;According to project type, power generation project and energy saving and carbon reduction project are respectively mapped into thermal power generation adjustment amount and energy intensity adjustment amount, the annual emission after project implementation is calculated, and the carbon budget threshold is combined to dynamically adjust the parameters of emission reduction project.The technical scheme of the present application realizes the computable, assessable and dynamic optimization of the carbon emission budget of electric power industry by establishing a direct carbon emission accounting model and mapping the long-term carbon emission prediction index with the specific emission reduction project parameters, and improves the landing performance of emission reduction projects and the accuracy of annual budget adjustment.

Description

Technical Field

[0001] This invention relates to the technical field of carbon emission management, and in particular to a method and apparatus for formulating and dynamically optimizing carbon emission budgets in the power industry. Background Technology

[0002] Existing research has developed various methods for predicting peak carbon emissions at the national, provincial, municipal, or key industry levels. Among these, long-term energy substitution planning models (LEAPs) can incorporate factors such as population, economy, energy structure, end-user demand, and technological progress to simulate changing trends in energy consumption and carbon emissions, and are therefore widely used in medium- and long-term energy planning and carbon emission forecasting. The LEAP model, as an energy-environment analysis tool based on scenario simulation, can establish a model structure according to multiple levels of branches such as demand, sector, industry, type, and energy. It combines parameters such as activity level, energy intensity, and emission factors to calculate energy consumption, pollutant emissions, and carbon emissions under different scenarios. The LEAP model interface is shown in the attached image. Figure 9 As shown.

[0003] However, existing carbon emission budgeting methods mostly focus on predicting total carbon emissions, peak time, or macroeconomic scenario indicators. They typically use macroeconomic variables such as population, GDP growth rate, energy consumption structure, green electricity share, vehicle ownership, or freight volume as inputs. While these outputs can reflect the overall emission reduction trend of a region or industry, they are difficult to directly correlate with specific emission reduction entities and projects. For the power industry, carbon emission budgeting not only requires predicting future annual thermal power generation, green electricity share, and initial energy intensity, but also assessing whether new, expanded, or phased power generation projects, green electricity imports, thermal power exports, and energy-saving and carbon-reduction projects can meet annual carbon emission budget constraints. Existing solutions lack the technical means to establish a mapping relationship between long-term carbon emission forecast indicators and specific emission reduction project parameters and to dynamically adjust them. This leads to problems such as insufficient implementation of emission reduction measures, difficulty in timely evaluation of implementation effects, and difficulty in incorporating the uncertainties of long-term projects into budget management. How to properly address these issues has become an urgent problem for the industry. Summary of the Invention

[0004] This invention provides a method and apparatus for formulating and dynamically optimizing carbon emission budgets in the power industry, which can link carbon emission budgets with emission reduction projects, thereby improving the accuracy of budget formulation and dynamic optimization capabilities.

[0005] According to a first aspect of the present invention, a method for formulating and dynamically optimizing carbon emission budgets in the power industry is provided, the method comprising: Obtain basic data of the power industry in the target region for the base year n and the forecast year m. The basic data includes any one or more of the following: thermal power generation, renewable energy ratio, electricity consumption, electricity inflow, electricity outflow, thermal power generation energy intensity, fuel emission factor, and emission reduction project parameters. Based on the aforementioned fundamental data, a direct emission accounting model for the power industry in the target region is established within the LEAP-based energy and environmental scenario model. Using thermal power generation, energy intensity, and emission factors as model variables, the initial thermal power generation P for the predicted year m is obtained. m Initial energy intensity EI m and the initial carbon emissions budget E m ; Based on the project type of the emission reduction project parameters, new, renovated, expanded, or phased-out thermal power projects are mapped to thermal power generation adjustment amounts, and energy-saving and carbon-reduction projects are mapped to energy intensity adjustment amounts. Furthermore, based on the thermal power generation P... m and the energy consumption intensity EI m The thermal power generation adjustment and the energy intensity adjustment are respectively superimposed to obtain the thermal power generation P′ after the project is implemented. m Energy intensity EI′ after project implementation m ; The thermal power generation P′ after the project is implemented m and the energy intensity EI′ after the project is implemented m Substituting into the direct emission accounting model, the annual emission E′ after project implementation is obtained. m and E′ m With the initial carbon emission budget E m Compared with the carbon budget threshold determined by the elasticity coefficient, at E′ m When the carbon budget threshold is not met, according to P′ m With P m Comparison results and EI′ m With EI m The comparison results are used to adjust the parameters of the emission reduction projects and substitute them back into the direct emission accounting model until E′ m The carbon budget threshold is met.

[0006] According to a second aspect of the present invention, an apparatus for formulating and dynamically optimizing carbon emission budgets in the power industry is provided, comprising: The acquisition module is used to acquire basic data of the power industry in the target area in the base year n and the forecast year m. The basic data includes any one or more of the following: thermal power generation, renewable energy ratio, electricity consumption, power inflow, power outflow, thermal power generation energy intensity, fuel emission factor, and emission reduction project parameters. The module is used to establish a direct emission accounting model for the power industry in the target region within the LEAP-based energy and environmental scenario model, based on the aforementioned basic data. It uses thermal power generation, energy intensity, and emission factors as model variables to obtain the initial thermal power generation P for the predicted year m. m Initial energy intensity EI m and the initial carbon emissions budget E m ; The mapping module is used to map new, expanded, or phased-out thermal power projects to thermal power generation adjustment amounts, and energy-saving and carbon-reduction projects to energy intensity adjustment amounts, based on the project type of the emission reduction project parameters, and according to the thermal power generation P... m and the energy consumption intensity EI m The thermal power generation adjustment and the energy intensity adjustment are respectively superimposed to obtain the thermal power generation P′ after the project is implemented. m Energy intensity EI′ after project implementation m ; The adjustment module is used to adjust the thermal power generation P′ after the project is implemented. m and the energy intensity EI′ after the project is implemented m Substituting into the direct emission accounting model, the annual emission E′ after project implementation is obtained. m and E′ m With the initial carbon emission budget E m Compared with the carbon budget threshold determined by the elasticity coefficient, at E′ m When the carbon budget threshold is not met, according to P′ m With P m Comparison results and EI′ m With EI m The comparison results are used to adjust the parameters of the emission reduction projects and substitute them back into the direct emission accounting model until E′ m The carbon budget threshold is met.

[0007] According to a third aspect of the present invention, an electronic device is provided, comprising: a communication interface, a processor, and a memory; The memory is used to store program instructions, which, when executed by the processor that is connected to the memory via the communication interface, implement any of the above-described methods for formulating and dynamically optimizing carbon emission budgets in the power industry.

[0008] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, on which computer program instructions are stored, which, when executed by a computer (e.g., a processor in the computer), implement any of the above-described methods for formulating and dynamically optimizing carbon emission budgets in the power industry.

[0009] In summary, this invention provides a method and apparatus for formulating and dynamically optimizing carbon emission budgets in the power industry. The method includes: acquiring basic data of the power industry in a target region for the base year and the forecast year; establishing a direct emission accounting model based on the LEAP energy and environmental scenario model to obtain the thermal power generation, energy intensity, and carbon emission budget for the base year and the forecast year; mapping power generation projects and energy conservation and carbon reduction projects to thermal power generation adjustments and energy intensity adjustments, respectively, according to project type; calculating the annual emissions after project implementation; and dynamically adjusting emission reduction project parameters based on carbon budget thresholds. The technical solution of this invention, by establishing a direct carbon emission accounting model and mapping long-term carbon emission forecast indicators to specific emission reduction project parameters, enables the calculation, evaluation, and dynamic optimization of carbon emission budgets in the power industry, improving the feasibility of emission reduction projects and the accuracy of annual budget adjustments.

[0010] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and drawings.

[0011] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0012] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0013] Figure 1 A flowchart of a method for formulating and dynamically optimizing carbon emission budgets in the power industry, provided as an embodiment of the present invention; Figure 2 A flowchart of another method for formulating and dynamically optimizing carbon emission budgets in the power industry, provided as an embodiment of the present invention; Figure 3 A flowchart of yet another method for formulating and dynamically optimizing carbon emission budgets in the power industry, provided as an embodiment of the present invention; Figure 4 A flowchart of yet another method for formulating and dynamically optimizing carbon emission budgets in the power industry, provided as an embodiment of the present invention; Figure 5 A flowchart of yet another method for formulating and dynamically optimizing carbon emission budgets in the power industry, provided as an embodiment of the present invention; Figure 6A flowchart of yet another method for formulating and dynamically optimizing carbon emission budgets in the power industry, provided as an embodiment of the present invention; Figure 7 A structural diagram of a carbon emission budgeting and dynamic optimization device for the power industry is provided as an embodiment of the present invention; Figure 8 A structural diagram of an electronic device provided as an embodiment of the present invention; Figure 9 The LEAP model interface provided for embodiments of the present invention. Detailed Implementation

[0014] The features and exemplary embodiments of various aspects of the present invention will now be described in detail. To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely intended to explain the present invention and not to limit the present invention. For those skilled in the art, the present invention can be practiced without some of these specific details. The following description of the embodiments is merely to provide a better understanding of the present invention by illustrating examples of the invention.

[0015] like Figure 1 As shown, this invention provides a method for formulating and dynamically optimizing carbon emission budgets in the power industry. This method includes: In step S11, the basic data of the power industry in the target area in the base year n and the forecast year m are obtained. The basic data includes any one or more of the following: thermal power generation, renewable energy ratio, electricity consumption, electricity inflow, electricity outflow, thermal power generation energy intensity, fuel emission factor and emission reduction project parameters. In step S12, based on the aforementioned basic data, a direct emission accounting model for the power industry in the target region is established within the LEAP-based energy and environmental scenario model. Thermal power generation, energy intensity, and emission factors are used as model variables to obtain the initial thermal power generation P for the predicted year m. m Initial energy intensity EI m and the initial carbon emissions budget E m ; In step S13, based on the project type of the emission reduction project parameters, newly built, expanded, or phased-out thermal power projects are mapped to thermal power generation adjustment amounts, and energy-saving and carbon-reduction projects are mapped to energy intensity adjustment amounts, and based on the thermal power generation P... m and the energy consumption intensity EI m The thermal power generation adjustment and the energy intensity adjustment are respectively superimposed to obtain the thermal power generation P′ after the project is implemented. m Energy intensity EI′ after project implementationm ; In step S14, the thermal power generation P′ after the project is implemented is... m and the energy intensity EI′ after the project is implemented m Substituting into the direct emission accounting model, the annual emission E′ after project implementation is obtained. m and E′ m With the initial carbon emission budget E m Compared with the carbon budget threshold determined by the elasticity coefficient, at E′ m When the carbon budget threshold is not met, according to P′ m With P m Comparison results and EI′ m With EI m The comparison results are used to adjust the parameters of the emission reduction projects and substitute them back into the direct emission accounting model until E′ m The carbon budget threshold is met.

[0016] In one embodiment, the target area is a city or industrial park with both thermal power generation enterprises and green power projects. The processing equipment acquires basic data on the power industry in the target area for the base year n and the forecast year m. The basic data includes thermal power generation, the proportion of green power, electricity consumption, power inflow, power outflow, initial energy intensity, fuel emission factor, and emission reduction project parameters. Emission reduction project parameters include project unit, project type, planned completion time, installed capacity, average annual power generation duration, fuel savings, and the year the project takes effect. Project types include newly built, renovated, expanded, or phased-out power generation projects, as well as energy-saving and carbon-reducing projects. Green power includes wind power, photovoltaic power, hydropower, and other power types with zero direct carbon emissions.

[0017] The processing equipment constructs a direct carbon emission accounting model for the power industry in the target region based on basic data. The direct carbon emission accounting model uses thermal power generation, energy intensity, and fuel emission factors as model variables, and determines the annual carbon emissions by multiplying these factors. Since the direct carbon emissions from green electricity are zero, the annual carbon emissions for the predicted year *m* are determined by the thermal power generation and energy intensity for the predicted year *m*. The processing equipment inputs the thermal power generation, green electricity ratio, and initial energy intensity for the baseline year *n* into the long-term energy substitution planning model, and combines this with the electricity consumption growth trend, green electricity construction plan, electricity inflow trend, and electricity outflow trend for the predicted year *m* to obtain the initial thermal power generation *P* for the predicted year *m*. m Initial energy intensity EI m and initial carbon emission budget E m .

[0018] Predicted initial thermal power generation P in year m mThe amount of electricity generated is determined based on the supply and demand relationship between electricity consumption, total power generation, losses, and electricity inflows and outflows. When reliable data on losses is lacking, a preset loss rate is used, or the losses are ignored without affecting budget accuracy. When independent planning data for electricity inflows and outflows is lacking, predictions are made based on electricity consumption trends; when historical electricity inflows and outflows exist, predictions are made based on historical trends. The processing equipment determines the green electricity generation based on the predicted annual total power generation (m) and the proportion of green electricity, and the power generation after deducting green electricity generation from the total power generation is determined as the initial thermal power generation P. m .

[0019] Predicted annual initial energy intensity EI m The initial energy intensity trend is determined based on the annual fuel consumption and annual thermal power generation of the thermal power plant. For thermal power plants with continuous production data, the processing equipment generates an initial energy intensity trend based on the ratio of annual fuel consumption to annual thermal power generation. For thermal power plants lacking continuous production data, the processing equipment completes the initial energy intensity trend based on the changing trends of power generation energy consumption or power supply energy consumption corresponding to the installed capacity of the thermal power plant. The processing equipment then calculates the initial thermal power generation P. m Initial energy intensity EI m Substituting the fuel emission factor into the direct carbon emission accounting model, the initial carbon emission budget E is calculated. m The model variables are corrected based on the preset peak year, preset peak value, or preset carbon emission intensity reduction target.

[0020] The processing equipment establishes project mapping relationships based on the project types in the emission reduction project parameters. For newly built, expanded, or phased-out power generation projects that take effect in forecast year m, the processing equipment calculates the project's annual power generation based on the project's installed capacity and average annual power generation duration, and determines the adjustment amount for thermal power generation according to the project's energy type. When the project's energy type is green electricity, the project's annual power generation is used to reduce the thermal power generation demand corresponding to forecast year m; when the project's energy type is thermal power generation, the project's annual power generation is used to increase the thermal power generation corresponding to forecast year m. For energy-saving and carbon-reducing projects that take effect in forecast year m, the processing equipment calculates the fuel consumption after implementing the energy-saving and carbon-reducing projects based on the thermal power plants' fuel consumption before and after implementing the projects, and combines this with the thermal power generation P′ after project implementation. m Calculate the energy intensity EI′ after project implementation m .

[0021] The processing equipment will handle the thermal power generation P′ after the project is implemented. m Energy intensity EI′ after project implementation m Substitute the values ​​into the direct carbon emission accounting model to calculate the annual emissions E′ after the project is implemented.m The treatment equipment is based on the initial carbon emissions budget E. m The carbon budget threshold is determined by a flexibility coefficient, which is set based on the policy constraint intensity of the target region in that year. The annual emissions E′ after project implementation. m When the emissions are less than or equal to the carbon budget threshold, the processing equipment outputs the result that the emissions reduction project parameters meet the carbon emission budget constraints. The annual emissions E′ after project implementation. m When the carbon budget threshold is exceeded, the thermal power generation P′ after the treatment equipment is implemented is compared with that of the project. m Compare the initial thermal power generation Pm with the energy intensity EI′ after project implementation. m With initial energy intensity EI m The thermal power generation P′ after the project is implemented m If the energy consumption intensity (EI′) is too high, adjust the parameters for new green power projects, green power imports, or thermal power exports; adjust the parameters for energy consumption intensity (EI′) after project implementation. m If the levels are too high, adjust the fuel savings of energy-saving and carbon-reduction projects or add new energy-saving and carbon-reduction projects. Substitute the adjusted emission reduction project parameters back into the direct carbon emission accounting model until the annual emissions E′ after project implementation are calculated. m Meets the carbon budget threshold.

[0022] In one embodiment, such as Figure 2 As shown, obtaining the basic data of the power industry in the target area for the base year n and the forecast year m includes the following steps S21-S23: In step S21, the total power generation, thermal power generation, renewable energy power generation, electricity consumption, power inflow and power outflow for the base year n are obtained from the energy balance sheet, statistical yearbook and power development plan, and the renewable energy ratio for the base year is determined by the renewable energy power generation and the total power generation. In step S22, the electricity consumption growth trend, renewable energy construction plan, energy intensity change trend of thermal power enterprises and fuel emission factor are organized according to the time series of the predicted year m to obtain the model input sequence; In step S23, the project unit, project type, planned completion time, installed capacity, average annual power generation duration or fuel saving amount of the emission reduction project are collected, and the model input sequence is associated with the emission reduction project to the predicted year m.

[0023] In one embodiment, the processing device acquires basic data on the power industry in a target region for a base year n and a forecast year m. The basic data sources include energy balance sheets, statistical yearbooks, and power development plans. The processing device extracts total power generation, thermal power generation, green power generation, electricity consumption, power inflow, and power outflow for base year n from the energy balance sheets and statistical yearbooks, and determines the ratio of green power generation to total power generation as the green power share for base year n. Green power includes wind power, photovoltaic power, hydropower, and other power generation types with zero direct carbon emissions.

[0024] The processing equipment organizes the electricity consumption growth trend, green power construction plan, energy intensity change trend of thermal power plants, and fuel emission factor according to the time series of the predicted year m, forming the model input sequence. The electricity consumption growth trend is used to characterize the future electricity demand change in the target area, the green power construction plan is used to characterize the future green power supply change in the target area, the energy intensity change trend of thermal power plants is used to characterize the energy intensity change of thermal power plants, and the fuel emission factor is used to characterize the carbon emissions corresponding to a unit of fuel consumption. The fuel emission factor remains at the base year n value when there is no annual adjustment basis.

[0025] The processing equipment collects emission reduction project parameters within the target area and performs field matching on these parameters according to project type. For newly built, expanded, or phased-out power generation projects, emission reduction project parameters include project unit, project type, planned completion time, installed capacity, and average annual power generation duration. For energy-saving and carbon-reducing projects, emission reduction project parameters include project unit, project type, planned completion time, and fuel savings. The processing equipment determines whether the emission reduction project is effective in prediction year m based on the planned completion time and associates the effective emission reduction project parameters with the model input sequence to the same prediction year m, enabling the basic data for prediction year m to support subsequent thermal power generation prediction, energy intensity prediction, and carbon emission budget calculation.

[0026] In one embodiment, such as Figure 3 As shown, the initial thermal power generation P for the predicted year m is obtained. m This includes the following steps S31-S33: In step S31, based on the predicted annual electricity consumption U... m Total power generation Q m Loss amount L m Electricity transfer in volume I m and power outflow O m Relationship U m =Q m -L m +I m -O m Determine the total power generation Q for the predicted year m. m ; In step S32, if the power transfer amount I cannot be obtained... m and power outflow O m When the trend of change is observed, I will be used. m and O m According to electricity consumption U m The trend of change is predicted; when historical inbound and outbound volumes are available, I is predicted based on the changing trends of the historical inbound and outbound volumes. m and O m ; In step S33, based on the predicted renewable energy share of GE in year m m Calculate the projected annual renewable energy power generation G. m =Q m ×GE m and the total power generation Q m Excluding the renewable energy generation G m The subsequent power generation is determined as the initial thermal power generation P. m .

[0027] In one embodiment, the processing device calculates the predicted annual electricity consumption U based on the data. m Total power generation Q m Loss amount L m Electricity transfer in volume I m and power outflow O m Relationship U m =Q m -L m +I m -O m Determine the total power generation Q for the predicted year m. m The electricity consumption Um is used to characterize the electricity consumption demand of the target area in the predicted year m, and the total power generation Q is... m The loss L is used to characterize the power production scale of the target region in the predicted year m. m The power inflow I is used to characterize power loss in power transmission and distribution and statistical terms. m The power outflow amount Om is used to characterize the amount of electricity input from the outside to the target area, and the power outflow amount Om is used to characterize the amount of electricity output from the target area to the outside. Loss amount L m When statistical data is available, it is used as the value; loss amount L m When statistical data is lacking, the loss rate should be determined according to the preset loss rate, or ignored if the budget accuracy requirements are met.

[0028] The electricity consumption includes the end-user electricity demand of each sector within the target area, including the increased electricity demand from industrial electrification upgrades (such as oil-to-electricity conversion and electric drive replacement). This ensures that the reduction in direct fuel emissions from the industrial sector and the increase in power generation emissions from the power sector are calculated separately and without overlap. The sum of the calculation results by sector is consistent with the actual carbon emissions of the target area.

[0029] The processing equipment predicts the annual power inflow I. m and power outflow O m Forecasting is not possible. Power inflow (I) cannot be obtained. m and power outflow O m When considering the changing trends, Im and Om will be calculated based on electricity consumption U. m Predicting the changing trend of electricity import I m Electricity outflow O m Electricity consumption U in the target area m The changes remain consistent. When historical inbound and outbound volumes are available, I is predicted based on the changing trends of the historical inbound and outbound volumes. m and O m This will increase the predicted total power generation Q in the year m. m Calculation accuracy.

[0030] The processing equipment is based on the projected annual renewable energy share of GE. m Calculate the projected annual renewable energy power generation G. m The calculation relationship is G. m =Q m ×GE m The percentage of renewable energy in GE m Used to characterize the renewable energy generation in the predicted year m in the total power generation Q m The proportion of renewable energy power generation G m This is used to characterize the electricity generated in a predicted year, m, from wind, solar, hydro, and other power generation types with zero direct emissions. Since renewable energy sources have zero direct carbon emissions, the processing equipment will process the total electricity generation Q. m Excluding the renewable energy generation G m The subsequent power generation is determined as the initial thermal power generation P. m The initial thermal power generation P m It is used to characterize the scale of electricity supply undertaken by thermal power generation in the predicted year m, and serves as the basic variable for calculating the initial carbon emission budget in the subsequent direct carbon emission accounting model.

[0031] In one embodiment, such as Figure 4 As shown, the initial energy intensity EI for obtaining the base year and the predicted year m is... m and the initial carbon emissions budget Em This includes the following steps S41-S43: In step S41, the annual fuel consumption and annual thermal power generation of thermal power plants in the target area are obtained, the energy intensity of each thermal power plant is calculated, and the energy intensity change trend is formed according to the predicted annual m. In step S42, for thermal power plants lacking an energy intensity change trend, the energy intensity change trend is supplemented according to the change trend of power generation energy consumption level or power supply energy consumption level corresponding to the installed capacity of the thermal power plant, to obtain the initial energy intensity EI. m ; In step S43, the initial thermal power generation P is... m The initial energy consumption intensity EI m The fuel emission factor EF is input into the direct emission accounting model, according to E m =P m ×EI m ×EF calculates the initial carbon emission budget E m And set targets for reducing P based on preset peak year, preset peak value, or preset carbon emission intensity. m or EI m Perform parameter calibration.

[0032] In one embodiment, the processing device acquires the annual fuel consumption and annual thermal power generation of thermal power plants within a target area, and calculates the energy intensity of each thermal power plant based on the correspondence between annual fuel consumption and annual thermal power generation. The energy intensity characterizes the fuel consumption level of a thermal power plant when generating a unit of electricity. The processing device organizes the energy intensity of each thermal power plant according to the time series corresponding to the predicted year m, forming an energy intensity change trend. This energy intensity change trend is used to predict the unit power generation energy intensity of the power industry in the target area in the predicted year m.

[0033] For thermal power plants that generate combined heat and power (CHP), their annual fuel consumption is divided into power generation and heating components according to the heat-to-power ratio or energy allocation method. Only the ratio of fuel consumption in the power generation component to the annual thermal power generation is taken as the energy intensity of the enterprise. The fuel combustion emissions corresponding to the heating component are accounted for by the heat production and supply industry in the industrial sector and are not included in this power industry to avoid double metering in the CHP process.

[0034] For thermal power plants lacking trends in their energy intensity changes, the processing equipment completes the missing trends by matching the power generation or power supply energy consumption levels corresponding to the plant's installed capacity. These power generation or power supply energy consumption levels can be determined based on publicly available statistical data, industry planning data, or historical operating data from thermal power plants of similar installed capacity. Based on the completed energy intensity trends, the processing equipment obtains the initial energy intensity EI for the base year and the predicted year m. m .

[0035] The processing equipment will process the initial thermal power generation P m The initial energy consumption intensity EI m The fuel emission factor EF is input into the direct emission accounting model, and according to E... m =P m ×EI m ×EF calculates the initial carbon emission budget E m Among them, the initial thermal power generation P m The initial energy intensity EI is used to characterize the scale of electricity supply to be provided by thermal power generation in the predicted year m. m The fuel emission factor EF is used to characterize the fuel consumption level corresponding to the predicted annual m thermal power generation, and to characterize the carbon emissions per unit of fuel consumption. The processing equipment uses preset peak year, preset peak value, or preset carbon emission intensity reduction targets as correction constraints for P. m or EI m Perform parameter corrections to ensure that the calculated initial carbon emission budget E m It aligns with the carbon emission control targets of the power industry in the target region.

[0036] In one embodiment, such as Figure 5 As shown, it also includes the following steps S51-S54: In step S51, the emission reduction projects are classified into new, expanded or phased-out power generation projects and energy-saving and carbon-reducing projects according to the project type, and it is determined whether the emission reduction project will take effect in the predicted year m according to the project's planned completion time. In step S52, for newly built, expanded, or phased power generation projects that take effect in the predicted year m, the installed capacity C of the project is used as the basis for calculation. i and average annual power generation duration H i Calculate the project's annual power generation C i ×H i The project's annual power generation is then converted into an adjustment for thermal power generation based on the project's energy type, resulting in the thermal power generation P′ after the project's implementation. m ; In step S53, for energy-saving and carbon-reduction projects that take effect in the predicted year m, the post-measure fuel consumption AK-XK is calculated based on the fuel consumption AK before the measures are taken and the fuel savings XK after the measures are taken, and then calculated according to EI′. m = (AK - XK) / P′ m Calculate the energy intensity EI′ after project implementation m ; In step S54, the thermal power generation P′ after the project is implemented is... m and the energy intensity EI′ after the project is implemented m Write the model variables corresponding to the predicted year m into the direct emission accounting model.

[0037] In one embodiment, the processing equipment classifies emission reduction projects into new, expanded, or phased-out power generation projects and energy-saving and carbon-reduction projects based on their project type. Emission reduction project parameters include at least the project type, planned completion date, installed capacity, average annual power generation duration, fuel consumption before implementing energy-saving and carbon-reduction measures, and fuel savings after implementing them. The processing equipment determines whether an emission reduction project will take effect in predicted year m based on the correspondence between the planned completion date and the predicted year m. If the planned completion date is earlier than or equal to predicted year m, the emission reduction project is determined to take effect in predicted year m; if the planned completion date is later than predicted year m, the emission reduction project is determined not to participate in the calculation of thermal power generation adjustments or energy intensity adjustments for predicted year m.

[0038] For newly built, expanded, or phased-out power generation projects that take effect in the predicted year m, the processing equipment shall be based on the project's installed capacity C. i and average annual power generation duration H i Calculate the project's annual power generation C i ×H i When the energy type of the project is green electricity, the annual power generation C of the project is... i ×H i As a basis for reducing the demand for thermal power generation, and converted into an adjustment amount for thermal power generation; when the energy type of the project is thermal power generation, the annual power generation C of the project is... i ×H i This serves as the basis for increasing the supply of thermal power generation and is converted into an adjustment amount for thermal power generation. The processing equipment matches the adjustment amount with the initial thermal power generation for the predicted year m to obtain the thermal power generation P′ after the project is implemented. m The thermal power generation P′ after the project is implemented. m This is used to characterize the scale of electricity supply to be provided by thermal power generation after the new, expanded, or phased-out power generation projects take effect in the predicted year m.

[0039] For energy-saving and carbon-reduction projects that take effect in the predicted year m, the processing equipment uses company K as the calculation object, obtains the fuel consumption AK of company K before implementing the energy-saving and carbon-reduction project and the fuel savings XK after implementing the project, and calculates the fuel consumption after project implementation according to AK-XK. AK represents company K's fuel consumption without implementing the energy-saving and carbon-reduction project, XK represents the reduction in standard fuel consumption by company K through the energy-saving and carbon-reduction project, and AK-XK represents company K's fuel consumption after project implementation. The processing equipment is based on EI′ m = (AK - XK) / P′ m Calculate the energy intensity EI′ after project implementation m This allows the impact of energy-saving and carbon-reducing projects on fuel consumption levels to be converted into energy intensity adjustments in the direct emissions accounting model.

[0040] The processing equipment will process the thermal power generation P′ after the project is implemented. m and the energy intensity EI′ after the project is implemented m Write the model variables corresponding to the predicted year m into the direct emission accounting model. The thermal power generation P′ after project implementation will be written into this model. m Energy intensity EI′ after project implementation m These values ​​replace the initial thermal power generation and initial energy intensity of the predicted year m, respectively, to calculate the annual carbon emissions of the predicted year m after the emission reduction projects take effect. Through the above processing, the changes in power supply corresponding to new, expanded, or phased power generation projects, and the changes in fuel consumption corresponding to energy-saving and carbon-reduction projects, can all be incorporated into the direct emission accounting model as unified model variables.

[0041] In one embodiment, such as Figure 6 As shown, adjusting the emission reduction project parameters and substituting them back into the direct emission accounting model includes the following steps S61-S65: In step S61, the elastic coefficient λ is set, and according to E m ×λ determines the carbon budget threshold for the forecast year m, where λ is set according to the policy constraint intensity of the target region in that year; In step S62, when E′ m ≤E m When ×λ, keep the current emission reduction project parameters unchanged, and output the carbon budget satisfaction result for the predicted year m and the corresponding project execution status; In step S63, when E′ m >E m ×λ and P′ m >P mWhen ×λ, adjust the generation-side parameters in the emission reduction project parameters. The generation-side parameters include parameters for new renewable energy projects, renewable energy imports, or thermal power exports. Then, recalculate P′ based on the adjusted generation-side parameters. m and E′ m ; In step S64, when E′ m >E m ×λ and P′ m ≤P m ×λ and EI′ m >EI m When ×λ, the targets for stricter energy conservation and carbon reduction measures are determined based on the energy consumption intensity of each thermal power plant. The fuel savings XK of the aforementioned energy conservation and carbon reduction projects are adjusted, or new energy conservation and carbon reduction projects are added. EI′ is then recalculated based on the adjusted energy conservation and carbon reduction projects. m and E′ m ; In step S65, after the predicted year m ends, the corresponding P′ is replaced with the actual thermal power generation and the actual energy consumption intensity. m and EI′ m And update the emission reduction project parameters for subsequent forecast years.

[0042] In one embodiment, the processing equipment adjusts the emission reduction project parameters and substitutes them back into the direct emission accounting model. The processing equipment sets an elasticity coefficient λ and, based on E... m ×λ determines the carbon budget threshold for the forecast year m, where λ is set according to the policy constraint intensity of the target region in that year. λ is used to characterize the stringency of the carbon emission budget implementation in the target region in the forecast year m; when the policy constraint intensity is high, the value of λ decreases accordingly, and when the policy constraint intensity is low, the value of λ increases accordingly. The carbon budget threshold is used to determine the annual carbon emissions E′ after the project is implemented. m Does it meet the budget constraints for the projected year m?

[0043] When E' m ≤E m When ×λ, the treatment equipment determines the annual emissions E′ after the project is implemented. m The system satisfies the carbon budget threshold for forecast year m, keeps the current emission reduction project parameters unchanged, and outputs the carbon budget satisfaction result for forecast year m and the corresponding project execution status. The project execution status includes whether the emission reduction project is effective, the project type, the planned project completion time, the power generation side parameter values, and the fuel savings XK of the energy-saving and carbon-reducing project. By retaining the current emission reduction project parameters, combinations of emission reduction projects that meet the carbon budget threshold can be identified as executable schemes for forecast year m.

[0044] When E' m >E m ×λ and P′m >P m When ×λ, the processing equipment determines the thermal power generation P′ after the project is implemented. m Exceeding the initial thermal power generation P m The thermal power generation constraint range is determined by the elasticity coefficient λ, and the generation-side parameters in the emission reduction project parameters are adjusted. These generation-side parameters include parameters for new renewable energy projects, renewable energy imports, or thermal power exports. The processing equipment recalculates P′ based on the adjusted generation-side parameters. m and E′ m This allows the power generation-side adjustment results to be re-entered into the direct emission accounting model for budget verification.

[0045] When E' m >E m ×λ and P′ m ≤P m ×λ and EI′ m >EI m When ×λ, the processing equipment determines that the thermal power generation P′m after the project implementation does not exceed the thermal power generation constraint range, but the energy consumption intensity EI′ after the project implementation m Exceeding the initial energy intensity EI m The energy consumption intensity constraint range is determined by the elasticity coefficient λ. The processing equipment determines the targets for stricter energy conservation and carbon reduction measures based on the energy consumption intensity of each thermal power plant, adjusts the fuel savings XK of the aforementioned energy conservation and carbon reduction projects, or adds new energy conservation and carbon reduction projects, and recalculates EI′ based on the adjusted energy conservation and carbon reduction projects. m and E′ m After the predicted year m ends, the processing equipment replaces the corresponding P′ with the actual thermal power generation and actual energy consumption intensity. m and EI′ m And update the emission reduction project parameters for subsequent forecast years.

[0046] In one embodiment, Figure 7 This is a block diagram illustrating a device for carbon emission budgeting and dynamic optimization in the power industry, according to an exemplary embodiment. Figure 7 The device for setting and dynamically optimizing carbon emission budgets in the power industry includes an acquisition module 71, an establishment module 72, a mapping module 73, and an adjustment module 74.

[0047] The acquisition module 71 is used to acquire basic data of the power industry in the target area in the base year n and the forecast year m. The basic data includes any one or more of the following: thermal power generation, renewable energy ratio, electricity consumption, power inflow, power outflow, thermal power generation energy intensity, fuel emission factor, and emission reduction project parameters. The module 72 is used to establish a direct emission accounting model for the power industry in the target area based on the basic data and in the LEAP-based energy and environmental scenario model. It uses thermal power generation, energy intensity, and emission factors as model variables to obtain the initial thermal power generation P for the predicted year m. m Initial energy intensity EI m and the initial carbon emissions budget E m ; The mapping module 73 is used to map new, expanded, or phased-out thermal power projects to thermal power generation adjustment amounts, and energy-saving and carbon-reducing projects to energy intensity adjustment amounts, based on the project type of the emission reduction project parameters, and to map the initial thermal power generation P. m and the initial energy consumption intensity EI m The thermal power generation adjustment and the energy intensity adjustment are respectively superimposed to obtain the thermal power generation P′ after the project is implemented. m Energy intensity EI′ after project implementation m ; The adjustment module 74 is used to adjust the thermal power generation P′ after the project is implemented. m and the energy intensity EI′ after the project is implemented m Substituting into the direct emission accounting model, the annual emission E′ after project implementation is obtained. m and E′ m With the initial carbon emission budget E m Compared with the carbon budget threshold determined by the elasticity coefficient, at E′ m When the carbon budget threshold is not met, according to P′ m With P m Comparison results and EI′ m With EI m The comparison results are used to adjust the parameters of the emission reduction projects and substitute them back into the direct emission accounting model until E′ m The carbon budget threshold is met.

[0048] The acquisition module 71, the establishment module 72, the mapping module 73, and the adjustment module 74 included in the block diagram of the power industry carbon emission budgeting and dynamic optimization device are controlled to execute the power industry carbon emission budgeting and dynamic optimization method described in any of the above embodiments.

[0049] like Figure 8 As shown, the present invention provides an electronic device 800, which includes: a communication interface, a processor 801, and a memory 802; The memory 802 stores program instructions. When executed by the processor 801, which is connected to the memory 802 via the communication interface, the program instructions acquire basic data on the target region's power industry in the base year and forecast year, including thermal power generation, renewable energy ratio, electricity consumption, energy intensity, emission factors, and emission reduction project parameters. Based on the LEAP energy and environment scenario model, a direct emission accounting model is established to obtain the thermal power generation, energy intensity, and carbon emission budget for the base year and forecast year. Then, according to project type, power generation projects and energy conservation and carbon reduction projects are mapped to thermal power generation adjustment and energy intensity adjustment, respectively. The annual emissions after project implementation are calculated, and the emission reduction project parameters are dynamically adjusted in conjunction with the carbon budget threshold.

[0050] This invention provides a computer-readable storage medium storing computer program instructions. When the computer program instructions are executed by a processor, they acquire basic data on the target region's power industry, including thermal power generation, renewable energy share, electricity consumption, energy intensity, emission factors, and emission reduction project parameters, for the base year and forecast year. Based on the LEAP energy and environment scenario model, a direct emission accounting model is established to obtain the thermal power generation, energy intensity, and carbon emission budget for the base year and forecast year. Then, according to project type, power generation projects and energy conservation and carbon reduction projects are mapped to thermal power generation adjustment and energy intensity adjustment, respectively. The annual emissions after project implementation are calculated, and the emission reduction project parameters are dynamically adjusted in conjunction with the carbon budget threshold.

[0051] It should be understood that the specific features, operations, and details described above regarding the method of the present invention can also be similarly applied to the apparatus and system of the present invention, or vice versa. Furthermore, each step of the method of the present invention described above can be performed by a corresponding component or unit of the apparatus or system of the present invention.

[0052] It should be understood that the various modules / units of the device of the present invention can be implemented wholly or partially through software, hardware, firmware, or a combination thereof. Each module / unit can be embedded in the processor of a computer device in hardware or firmware form or independent of the processor, or it can be stored in the memory of a computer device in software form for the processor to call to execute the operation of each module / unit. Each module / unit can be implemented as an independent component or module, or two or more modules / units can be implemented as a single component or module.

[0053] In one embodiment, a computer device is provided, including a memory and a processor. The memory stores computer instructions executable by the processor, which, when executed by the processor, instruct the processor to perform steps of the methods of embodiments of the present invention. The computer device can be broadly categorized as a server, terminal, or any other electronic device with the necessary computing and / or processing capabilities. In one embodiment, the computer device may include a processor, memory, network interface, communication interface, etc., connected via a system bus. The processor of the computer device can be used to provide the necessary computing, processing, and / or control capabilities. The memory of the computer device may include a non-volatile storage medium and internal memory. The non-volatile storage medium may store an operating system, computer programs, etc. The internal memory can provide an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface and communication interface of the computer device can be used to connect and communicate with external devices via a network. When the computer program is executed by the processor, it performs the steps of the methods of the present invention.

[0054] This invention can be implemented as a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, causes the steps of the methods of embodiments of the invention to be performed. In one embodiment, the computer program is distributed across multiple network-coupled computer devices or processors, such that the computer program is stored, accessed, and executed in a distributed manner by one or more computer devices or processors. A single method step / operation, or two or more method steps / operations, may be executed by a single computer device or processor or by two or more computer devices or processors. One or more method steps / operations may be executed by one or more computer devices or processors, and one or more other method steps / operations may be executed by one or more other computer devices or processors. One or more computer devices or processors may execute a single method step / operation, or execute two or more method steps / operations.

[0055] It will be understood by those skilled in the art that the method steps of the present invention can be performed by a computer program instructing related hardware, such as a computer device or processor. The computer program may be stored in a non-transitory computer-readable storage medium, and its execution causes the steps of the present invention to be performed. Depending on the context, any references herein to memory, storage, databases, or other media may include non-volatile and / or volatile memory. Examples of non-volatile memory include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid-state drive, etc. Examples of volatile memory include random access memory (RAM), external cache memory, etc.

[0056] The technical features described above can be combined arbitrarily. Although not all possible combinations of these technical features are described, any combination of these technical features should be considered to be covered by this specification, provided that such combination does not contain contradictions.

[0057] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for formulating and dynamically optimizing carbon emission budgets in the power industry, characterized in that, include: Obtain basic data of the power industry in the target region for the base year n and the forecast year m. The basic data includes any one or more of the following: thermal power generation, renewable energy ratio, electricity consumption, electricity inflow, electricity outflow, thermal power generation energy intensity, fuel emission factor, and emission reduction project parameters. Based on the aforementioned fundamental data, a direct emission accounting model for the power industry in the target region is established within the LEAP-based energy and environmental scenario model. Using thermal power generation, energy intensity, and emission factors as model variables, the initial thermal power generation P for the predicted year m is obtained. m Initial energy intensity EI m and the initial carbon emissions budget E m ; Based on the project type of the emission reduction project parameters, new, renovated, expanded, or phased-out thermal power projects are mapped to thermal power generation adjustment amounts, and energy-saving and carbon-reduction projects are mapped to energy intensity adjustment amounts, and based on the initial thermal power generation P... m and the initial energy consumption intensity EI m The thermal power generation adjustment and the energy intensity adjustment are respectively superimposed to obtain the thermal power generation P′ after the project is implemented. m Energy intensity EI′ after project implementation m ; The thermal power generation P′ after the project is implemented m and the energy intensity EI′ after the project is implemented m Substituting into the direct emission accounting model, the annual emission E′ after project implementation is obtained. m and E′ m With the initial carbon emission budget E m Compared with the carbon budget threshold determined by the elasticity coefficient, at E′ m When the carbon budget threshold is not met, according to P′ m With P m Comparison results and EI′ m With EI m The comparison results are used to adjust the parameters of the emission reduction projects and substitute them back into the direct emission accounting model until E′ m The carbon budget threshold is met.

2. The method for formulating and dynamically optimizing carbon emission budgets in the power industry as described in claim 1, characterized in that, The acquisition of basic data for the power industry in the target region in the base year n and the forecast year m includes: The total power generation, thermal power generation, renewable energy power generation, electricity consumption, electricity inflow, and electricity outflow for the base year n are obtained from energy balance sheets, statistical yearbooks, and power development plans. The proportion of renewable energy in the base year is determined by the renewable energy power generation and the total power generation. Based on the data of the base year n, the electricity consumption growth trend, renewable energy construction plan, energy intensity change trend of thermal power enterprises and fuel emission factor are organized according to the time series of the forecast year m to obtain the model input sequence; Collect information on the project unit, project type, planned completion time, installed capacity, average annual power generation duration, or fuel savings of emission reduction projects, and associate the model input sequence with the emission reduction projects to the predicted year m.

3. The method for formulating and dynamically optimizing carbon emission budgets in the power industry as described in claim 2, characterized in that, The initial thermal power generation P for the predicted year m is obtained. m ,include: According to the predicted annual electricity consumption U m Total power generation Q m Loss amount L m Electricity transfer volume I m and power outflow O m Relationship U m =Q m -L m +I m -O m Determine the total power generation Q for the predicted year m. m Among them, the loss amount L m Ignored or assigned a value according to a preset loss rate; Unable to obtain power transfer volume I m and power outflow O m When the trend of change is observed, I will be used. m and O m According to electricity consumption U m The trend of change is predicted; when historical inbound and outbound volumes are available, I is predicted based on the changing trends of the historical inbound and outbound volumes. m and O m ; According to the projected annual renewable energy share of GE m Calculate the predicted annual renewable energy power generation G m =Q m ×GE m and the total power generation Q m Excluding the renewable energy generation G m The subsequent power generation is determined as the initial thermal power generation P. m .

4. The method for formulating and dynamically optimizing carbon emission budgets in the power industry as described in claim 3, characterized in that, The initial energy intensity EI for the predicted year m is obtained. m and the initial carbon emissions budget E m ,include: Obtain the annual fuel consumption and annual thermal power generation of thermal power plants in the target area, calculate the energy intensity of each thermal power plant, and form the trend of energy intensity change according to the predicted annual m. For thermal power plants lacking an energy intensity change trend, the energy intensity change trend is supplemented according to the change trend of power generation energy consumption level or power supply energy consumption level corresponding to the installed capacity of the thermal power plant, thus obtaining the initial energy intensity EI. m ; The initial thermal power generation P m The initial energy consumption intensity EI m The fuel emission factor EF is input into the direct emission accounting model, according to E m =P m ×EI m ×EF calculates the initial carbon emission budget E m And set targets for reducing P based on preset peak year, preset peak value, or preset carbon emission intensity. m or EI m Perform parameter calibration.

5. The method for formulating and dynamically optimizing carbon emission budgets in the power industry as described in claim 4, characterized in that, The mapping of newly built, renovated, expanded, or phased-out thermal power projects to thermal power generation adjustment amounts, and the mapping of energy-saving and carbon-reduction projects to energy intensity adjustment amounts, includes: Based on the project type, the emission reduction projects are divided into new, renovated, expanded or phased-out power generation projects and energy-saving and carbon-reducing projects, and it is determined whether the emission reduction project will take effect in the predicted year m based on the project's planned completion time. For newly built, expanded, or phased-out power generation projects that take effect in the predicted year m, the project's installed capacity C will be used as the basis. i and average annual power generation duration H i Calculate the project's annual power generation C i ×H i The project's annual power generation is then converted into an adjustment for thermal power generation based on the project's energy type, resulting in the thermal power generation P′ after the project's implementation. m ; For energy-saving and carbon-reduction projects that take effect in the predicted year m, the post-measure fuel consumption AK-XK is calculated based on the fuel consumption AK before the measures are taken by enterprise K and the fuel savings XK after the measures are taken, and then calculated according to EI′. m = (AK - XK) / P′ m Calculate the energy intensity EI′ after project implementation m ; The thermal power generation P′ after the project is implemented m and the energy intensity EI′ after the project is implemented m Write the model variables corresponding to the predicted year m into the direct emission accounting model.

6. The method for formulating and dynamically optimizing carbon emission budgets in the power industry as described in claim 5, characterized in that, The adjustment of the emission reduction project parameters and their subsequent substitution back into the direct emission accounting model includes: Set the elastic coefficient λ, and according to E m ×λ determines the carbon budget threshold for the forecast year m, where λ is set according to the policy constraint intensity of the target region in that year; When E' m ≤E m When ×λ, keep the current emission reduction project parameters unchanged, and output the carbon budget fulfillment result and the corresponding project execution status for the predicted year m; When E' m >E m ×λ and P′ m >P m When ×λ, adjust the generation-side parameters in the emission reduction project parameters. The generation-side parameters include parameters for new renewable energy projects, renewable energy imports, or thermal power exports. Then, recalculate P′ based on the adjusted generation-side parameters. m and E′ m ; When E' m >E m ×λ and P′ m ≤P m ×λ and EI′ m >EI m When ×λ, the targets for stricter energy conservation and carbon reduction measures are determined based on the energy consumption intensity of each thermal power plant. The fuel savings XK of the aforementioned energy conservation and carbon reduction projects are adjusted, or new energy conservation and carbon reduction projects are added. EI′ is then recalculated based on the adjusted energy conservation and carbon reduction projects. m and E′ m ; After the predicted year m ends, replace the corresponding P′ with the actual thermal power generation and actual energy consumption intensity. m and EI′ m And update the emission reduction project parameters for subsequent forecast years.

7. A device for formulating and dynamically optimizing carbon emission budgets in the power industry, characterized in that, include: The acquisition module is used to acquire basic data of the power industry in the target area in the base year n and the forecast year m. The basic data includes any one or more of the following: thermal power generation, renewable energy ratio, electricity consumption, power inflow, power outflow, thermal power generation energy intensity, fuel emission factor, and emission reduction project parameters. The module is used to establish a direct emission accounting model for the power industry in the target region within the LEAP-based energy and environmental scenario model, based on the aforementioned basic data. It uses thermal power generation, energy intensity, and emission factors as model variables to obtain the initial thermal power generation P for the predicted year m. m Initial energy intensity EI m and the initial carbon emissions budget E m ; The mapping module is used to map new, expanded, or phased-out thermal power projects to thermal power generation adjustment amounts, and energy-saving and carbon-reduction projects to energy intensity adjustment amounts, based on the project type of the emission reduction project parameters, and according to the thermal power generation P... m and the initial energy consumption intensity EI m The thermal power generation adjustment and the energy intensity adjustment are respectively superimposed to obtain the thermal power generation P′ after the project is implemented. m Energy intensity EI′ after project implementation m ; The adjustment module is used to adjust the thermal power generation P′ after the project is implemented. m and the energy intensity EI′ after the project is implemented m Substituting into the direct emission accounting model, the annual emission E′ after project implementation is obtained. m and E′ m With the initial carbon emission budget E m Compared with the carbon budget threshold determined by the elasticity coefficient, at E′ m When the carbon budget threshold is not met, according to P′ m With P m Comparison results and EI′ m With EI m The comparison results are used to adjust the parameters of the emission reduction projects and substitute them back into the direct emission accounting model until E′ m The carbon budget threshold is met.

8. The device for formulating and dynamically optimizing carbon emission budgets in the power industry as described in claim 7, characterized in that: The acquisition module, the establishment module, the mapping module, and the adjustment module are controlled to execute the method for formulating and dynamically optimizing carbon emission budgets in the power industry as described in any one of claims 2 to 6.

9. An electronic device, characterized in that, include: Communication interface, processor, memory; The memory is used to store program instructions, which, when executed by the processor connected to the memory via the communication interface, cause the electronic device to implement the power industry carbon emission budgeting and dynamic optimization method according to any one of claims 1 to 6.

10. A computer-readable storage medium having program instructions stored thereon, characterized in that, When the program instructions are executed by a computer, the computer enables the computer to implement the method for formulating and dynamically optimizing carbon emission budgets in the power industry as described in any one of claims 1 to 6.

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