Coal power unit carbon emission prediction method, device, equipment and medium

Abstract

This invention discloses a method, apparatus, equipment, and medium for predicting carbon emissions from coal-fired power units. It acquires historical and planning data of the power system in the target area, as well as multiple preset development paths. Using system dynamics, it dynamically extrapolates each preset path to generate expected output level data for each coal-fired power unit. Subsequently, it constructs a load factor-coal consumption relationship model for coal-fired power units and a unit-level carbon emission prediction model. The expected output level data is input into the model, and a coupled calculation of macro-level power development scenarios and micro-level unit operating characteristics is performed to generate carbon emission prediction results for each unit under each preset path. By transmitting the dynamic impact of regional power system evolution on coal-fired power plant operation to the unit carbon emission calculation, it achieves multi-scenario, refined carbon emission prediction. It can quantify the total carbon emissions, intensity, and peak-shaving additional emissions under different development paths, providing a scientific basis for low-carbon decision-making by coal-fired power enterprises and regional carbon peaking path planning.

Description

Technical Field

[0001] This invention relates to the field of carbon emission prediction technology in the power industry, and in particular to a method, apparatus, equipment and medium for predicting carbon emissions from coal-fired power units. Background Technology

[0002] Carbon emission forecasting for coal-fired power units is a crucial foundation for the power industry to achieve its carbon reduction targets. Currently, carbon emission forecasting methods primarily focus on internal production and operation data of the units, such as fuel consumption and energy efficiency indicators. The forecast results are mostly annual total carbon emissions or average emission intensity, failing to consider the unit's carbon emissions within the broader context of the dynamic evolution of the regional power system. This makes it difficult to reveal the refined impact of fluctuations in unit operating conditions caused by macroeconomic factors such as regional renewable energy penetration rates, inter-regional power transmission plans, and seasonal fluctuations on carbon emissions, resulting in poor accuracy in carbon emission forecasting results. Summary of the Invention

[0003] In view of this, the present invention provides a method, apparatus, equipment and medium for predicting carbon emissions from coal-fired power units, in order to solve the technical problem that existing methods for predicting carbon emissions from coal-fired power units ignore regional macro-dynamics, resulting in low accuracy of carbon emission prediction.

[0004] Firstly, a method for predicting carbon emissions from coal-fired power units is provided, the method comprising: Acquire historical power system data, power system planning data, and multiple preset development paths for the target area; Based on historical power system data and power system planning data, power system development parameters for various pre-set development paths are determined; Based on historical power system data, power system planning data, and power system development parameters, various preset development paths are simulated using a system dynamics model to generate expected power output data for each coal-fired power unit in the target area under various preset development paths within a preset time period. Acquire historical operating data, carbon emission characteristics data, energy-saving and carbon-reduction measures data, and load factor-power coal consumption relationship models for each coal-fired power unit within the target area; Based on the historical operating data and carbon emission characteristics data of each coal-fired power unit, a unit-level carbon emission prediction model is constructed. Based on expected power output data, historical operating data, carbon emission characteristic data, energy-saving and carbon reduction measures data, load factor-power supply coal consumption relationship model, and unit-level carbon emission prediction model, the macro-regional power development scenario and micro-coal-fired power unit operating characteristics are coupled and calculated for the target area to generate carbon emission prediction results for each coal-fired power unit under various preset development paths.

[0005] Secondly, a carbon emission prediction device for coal-fired power units is provided, the device comprising: The first acquisition module is used to acquire historical power system data, power system planning data, and multiple preset development paths for the target area. The determination module is used to determine the power system development parameters for various preset development paths based on historical power system data and power system planning data. The first generation module is used to generate expected power output data of each coal-fired power unit in the target area under various preset development paths within a preset time period, based on historical power system data, power system planning data and power system development parameters, and through system dynamics model to deduce various preset development paths. The second acquisition module is used to acquire historical operating data, carbon emission characteristic data, energy-saving and carbon reduction measures data of each coal-fired power unit in the target area, as well as the load rate-power supply coal consumption relationship model of each coal-fired power unit. The construction module is used to build unit-level carbon emission prediction models based on the historical operating data and carbon emission characteristics data of each coal-fired power unit; The second generation module is used to perform coupled calculations of macro-regional power development scenarios and micro-coal-fired power unit operating characteristics on the target area based on expected power output level data, historical operating data, carbon emission characteristic data, energy-saving and carbon reduction measures data, load factor-power supply coal consumption relationship model and unit-level carbon emission prediction model, and generate carbon emission prediction results for each coal-fired power unit under various preset development paths.

[0006] Thirdly, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described carbon emission prediction method for coal-fired power units.

[0007] Fourthly, a computer-readable storage medium is provided, which stores a computer program that, when executed by a processor, implements the steps of the above-described method for predicting carbon emissions from coal-fired power units.

[0008] The aforementioned carbon emission prediction methods, devices, equipment, and media for coal-fired power units utilize historical and planning data of the power system in the target area, along with multiple pre-defined development paths, to determine power system development parameters for each path. Subsequently, system dynamics are used to dynamically extrapolate each pre-defined path, generating expected output levels for each coal-fired power unit. Following this, historical operating data, carbon emission characteristics data, energy-saving and carbon-reduction measures data, and a load factor-power supply coal consumption relationship model are acquired to construct a unit-level carbon emission prediction model. The expected output levels are input into this model, and a coupled calculation of macro-level power development scenarios and micro-level unit operating characteristics is performed to generate carbon emission prediction results for each unit under each pre-defined path. By transmitting the dynamic impact of regional power system evolution on coal-fired power plant operation to unit carbon emission calculations, multi-scenario, hourly, and refined carbon emission prediction is achieved. This overcomes the shortcomings of traditional methods, which are isolated, static, and coarse-grained. It can quantify the total carbon emissions, intensity, and peak-shaving additional emissions under different development paths, providing a scientific basis for low-carbon decision-making by coal-fired power enterprises and regional carbon peaking path planning. Attached Figure Description

[0009] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 This is a flowchart illustrating a carbon emission prediction method for coal-fired power units in one embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of a carbon emission prediction device for a coal-fired power unit in one embodiment of the present invention. Detailed Implementation

[0010] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the accompanying drawings in the present invention are only for illustrative and descriptive purposes and are not intended to limit the scope of protection of the present invention.

[0011] Furthermore, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this invention illustrate operations implemented according to some embodiments of the invention. It should be understood that the operations in the flowcharts may not be implemented in sequence, and steps without logical contextual relationships may be reversed or performed simultaneously. Moreover, those skilled in the art, guided by the content of this invention, may add one or more other operations to the flowcharts, or remove one or more operations from the flowcharts.

[0012] Furthermore, the embodiments described herein are merely some, not all, of the embodiments of the invention. The components of the embodiments of the invention described and illustrated herein can typically be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0013] It should be noted that the term "comprising" will be used in the embodiments of the present invention to indicate the presence of a feature subsequently declared, but does not exclude the addition of other features. It should also be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0014] Currently, existing methods for predicting carbon emissions from coal-fired power units have the following main technical shortcomings: First, existing methods primarily focus on internal production and operation data of enterprises or generating units during the forecasting process, such as fuel consumption and energy efficiency indicators, failing to consider enterprise carbon emissions within the broader context of the dynamic evolution of the regional power system. In the new power system, coal-fired power is shifting from a baseload power source to a regulating and supporting power source. Its operating mode (such as peak shaving frequency and load factor) is significantly affected by macroeconomic factors such as the penetration rate of regional renewable energy, inter-regional power transmission plans, and seasonal fluctuations. Isolated internal enterprise data cannot reflect the true impact of external environmental changes on carbon emissions.

[0015] Second, existing forecasting models are mostly static calculations or trend extrapolations based on historical data, lacking the ability to extrapolate multiple scenarios and dynamic evolutions in the future. Specifically, they cannot assess the differentiated impacts of different regional power development paths (such as high-proportion renewable energy development scenarios and coal-fired power as a safety net) on the carbon emissions of specific coal-fired power plants, making it difficult to meet the policy requirements of multi-path planning under the goals of carbon peaking and carbon neutrality.

[0016] Third, existing methods mostly predict annual total carbon emissions or average emission intensity, failing to reveal the refined impact of fluctuations in unit operating conditions (such as deep peak shaving and frequent start-stop) caused by changes in macroeconomic power scenarios on carbon emissions. This makes it difficult to support refined carbon emission management and emission reduction decisions for coal-fired power companies.

[0017] Based on the above problems, this application proposes a carbon emission prediction method for coal-fired power units based on regional power development. By coupling macro-regional power development scenarios with micro-operational characteristics of coal-fired power units, dynamic, refined and multi-scenario carbon emission prediction can be achieved.

[0018] The following is a detailed description of this case, in conjunction with the relevant accompanying drawings in the instruction manual.

[0019] Please see Figure 1 This description and embodiment provide a method for predicting carbon emissions from coal-fired power units, specifically including the following steps: S10: Obtain historical power system data, power system planning data, and multiple preset development paths for the target area.

[0020] It is understood that the executing entity of this invention can be a carbon emission prediction device for coal-fired power units, or it can be a terminal or a server; no specific limitation is made here. This embodiment of the invention will be described using a server as an example.

[0021] In this step, the target area can be a provincial or inter-provincial regional power grid, the scope of which is determined based on data availability and forecast targets. Historical power system data for the target area is collected from the power grid dispatch center, energy statistical yearbooks, and power plant operation records, including at least one of the following: total electricity consumption of the target area over the years, typical daily and annual grid load curves, installed capacity of various types of pre-designated power sources (coal power, hydropower, wind power, photovoltaic, nuclear power, etc.), historical output curves, and power generation utilization hours. Simultaneously, power system planning data for the region is obtained from government-issued energy development plans, power development plans, and public reports from power grid companies, including at least one of the following: new installed capacity plans for various types of pre-designated power sources during the pre-designated planning period, renewable energy consumption responsibility weight targets, planned capacity and power transmission and reception plans for inter-regional transmission channels, and policies for adjusting the functional positioning of coal power (such as transitioning from baseload power to regulating power).

[0022] Furthermore, based on historical and planning data of the target area, and combined with the assessment of different future policy orientations and technological and economic conditions, a set of regional power development scenarios are pre-defined, which includes multiple preset development paths.

[0023] Optionally, several preset development paths include: a benchmark development path, which determines the growth rate of each power source entirely according to the existing power development plan as a reference path; a high renewable energy development path, which assumes that the installed capacity growth rate of new energy sources such as wind power and photovoltaics is higher than the planned target, while appropriately lowering the growth rate of coal power development to simulate a path of accelerated energy transition; and a coal power flexibility support development path, which assumes that coal power is accelerating its transformation into a regulating power source, lowers its annual utilization hours and high-load operation expectations, while increasing the growth rate of renewable energy and reducing the minimum technical output of coal-fired power units to meet the system's peak-shaving needs.

[0024] S20: Based on historical power system data and power system planning data, determine the power system development parameters for various preset development paths.

[0025] In this step, based on the power structure, load characteristics, and operating patterns of the target area as reflected in historical power system data, and using the new installed capacity plan, renewable energy consumption targets, and inter-regional transmission arrangements specified in the power system planning data as boundary constraints, the power system development status under each preset development path is characterized by setting quantitative power system development parameters to address the policy orientation and development logic corresponding to that path. These power system development parameters include at least one of the following: annual installed capacity, annual utilization hours, power generation share, net inter-regional power imports, system peak-shaving demand, and minimum technical output of coal-fired power units. These parameters collectively constitute a quantitative description of each preset development path, used to drive subsequent system dynamics simulations.

[0026] Optionally, for the baseline development path, the indicators in the planning data are directly used as development parameters; for the high renewable energy development path, under the premise of meeting the regional power supply and demand balance, the installed capacity growth rate and power generation ratio of renewable energy are adjusted to values ​​higher than the planning targets, while the growth rate and utilization hours of fossil energy such as coal power are correspondingly lowered; for the coal power flexibility support development path, the annual utilization hours and high load operation ratio of coal power are lowered, the minimum technical output parameter of coal power is adjusted downward, and the priority parameter for renewable energy consumption is raised.

[0027] In practical applications, for the baseline development path, based on the power development path determined by the power development plan, and without considering possible development variables, the basic growth rate of each type of power source is clarified, thereby establishing a baseline scenario database for power development. For the high renewable energy development path, based on policy assessments of the accelerated development of new energy power such as wind and solar, the growth rate of renewable energy power is appropriately increased, while the expected growth rate of fossil energy such as coal power is correspondingly lowered, provided that the power demand of the target region is met, thus establishing a scenario database for power development under this condition. For the coal power flexibility support development path, based on policy assessments of the accelerated transformation of coal power from a main power source to a regulating power source, the expected utilization hours and high-load operation mode of coal power are appropriately lowered, while the expected growth rate of renewable energy such as wind and solar is correspondingly increased, provided that the power demand of the target region is met, thus establishing a scenario database for power development under this condition.

[0028] S30: Based on historical power system data, power system planning data, and power system development parameters, various preset development paths are simulated through system dynamics models to generate expected power output data of each coal-fired power unit in the target area under various preset development paths within a preset time period.

[0029] In this step, the historical power system data, including the total electricity consumption curve, grid load curve, and historical output characteristics of various power sources, along with future new installed capacity plans, inter-regional transmission plans, and development parameters such as power source installed capacity, utilization hours, and minimum technical output of coal-fired power plants under various preset development paths, are input into the constructed system dynamics model. The model, using hours as the time step, simulates the output allocation process of various power sources within the region hourly, while satisfying multiple preset constraints. The model outputs the hourly output sequence required by each coal-fired power unit under each preset development path to ensure safe system operation, i.e., the expected output level data. This data is represented in time series form (e.g., output value or load factor over 8760 hours throughout the year) and serves as a dynamic input for subsequent unit-level carbon emission calculations.

[0030] Optionally, the preset constraints include at least one of the following: power balance, power output characteristics constraints (including the intermittency and anti-peak characteristics of wind power / solar power, the seasonality of hydropower, and the base load characteristics of nuclear power), system regulation demand constraints (peak-shaving capacity, ramp rate), and policy and market constraints (guaranteed purchase of renewable energy, inter-regional power transmission plans).

[0031] In one embodiment of this application, a specific scheme for generating expected power output level data is provided. In S30, based on historical power system data, power system planning data, and power system development parameters, various preset development paths are extrapolated through a system dynamics model to generate expected power output level data for each coal-fired power unit in the target area under various preset development paths within a preset time period. Specifically, this includes the following steps S31-S32: S31: Construct a system dynamics model and configure multiple preset constraints in the system dynamics model.

[0032] In this step, a system dynamics model is constructed using a stock-flow diagram or time-series production simulation method, focusing on the regional power system. This model simulates the long-term evolution and operating conditions of the power system under different development paths. The core variables of the model include the installed capacity, power generation, utilization hours, output curves, and net system load of various power sources. The model can reflect the dynamic impact of changes in the power source structure on system balance.

[0033] Furthermore, several pre-defined constraints are configured in the system dynamics model. These pre-defined constraints include at least: power balance constraints, i.e., the sum of total power generation and power input must meet the total power demand; power output characteristic constraints, including the intermittency and anti-peak-shaving characteristics of wind power and photovoltaic power, the seasonal characteristics of hydropower, and the baseload operation characteristics of nuclear power; system regulation demand constraints, including the peak-shaving capacity and ramp-up rate required to smooth out fluctuations in new energy sources, with the peak-shaving capacity of coal-fired power units as a key constraint; and policy and market constraints, including the renewable energy guaranteed purchase policy and inter-regional power transmission plans.

[0034] S32: Input historical power system data, power system planning data and power system development parameters into the system dynamics model. Through the system dynamics model and multiple preset constraints, dynamically deduce the annual output level of each coal-fired power unit required to meet system balance under various preset development paths, and generate the expected output level data of each coal-fired power unit within a preset time period.

[0035] In this step, historical power system data, power system planning data, and power system development parameters are input into a pre-constructed system dynamics model. The model uses hours as its time step and, under the premise of meeting various preset constraints, simulates the output distribution of various power sources within the region hourly. It outputs the hourly output sequence required by each coal-fired power unit to maintain the safe and stable operation of the system under each preset development path. This sequence is represented in time series form (e.g., output value or load factor over 8760 hours throughout the year), representing the expected output level data of each coal-fired power unit within a preset time period (e.g., 2030), serving as a dynamic input for subsequent unit-level carbon emission calculations.

[0036] S40: Obtain historical operating data, carbon emission characteristics data, energy-saving and carbon-reduction measures data, and load rate-power coal consumption relationship models for each coal-fired power unit within the target area.

[0037] In this step, historical operating data is collected from the distributed control systems or plant-level monitoring systems of each coal-fired power unit. This data includes at least hourly power generation curves, heating curves, operating load rates, and unit start-up and shutdown records, used to characterize the actual operating status of the units at different time scales. Carbon emission characteristic data is obtained from the coal metering systems of each coal-fired power unit, coal quality test reports, and enterprise carbon emission accounting ledgers. This data includes at least the type and quantity of fuel consumed by each unit, the elemental carbon content of the fuel, the lower heating value of the fuel, and the carbon oxidation rate. Simultaneously, data on energy-saving and carbon-reduction measures are obtained from the technical transformation plans, equipment operation manuals, or third-party energy-saving assessment reports of coal-fired power enterprises within the target area. This includes the impact factors of implemented measures (such as turbine flow path modification, boiler combustion optimization, and flexibility modification) on unit performance, as well as the expected performance parameters of planned measures (such as heating system modification and carbon capture and storage).

[0038] Furthermore, based on historical operating data of each coal-fired power unit, the coal consumption for power supply is collected at different preset load rates (e.g., 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100%). Regression analysis or interpolation methods are used to construct dynamic relationship curves or functions that reflect the nonlinear rising characteristics of coal consumption in low-load areas, thereby fitting and establishing a load rate-power supply coal consumption relationship model for the unit. This model can output the corresponding dynamic power supply coal consumption based on any input load rate value.

[0039] In one embodiment of this application, a specific scheme for acquiring operational and carbon emission characteristic data of each coal-fired power unit is provided. S40, which involves acquiring historical operational data, carbon emission characteristic data, energy-saving and carbon-reduction measures data, and load factor-power supply coal consumption relationship models for each coal-fired power unit within the target area, specifically includes the following steps S41-S44: S41: Obtain historical operating data of each coal-fired power unit within the target area; The historical operating data includes at least one of the following: power generation curve, heating curve, operating load rate, main steam parameters, and coal feed curve.

[0040] In this step, historical operating data is collected from the distributed control system or plant-level monitoring system of each coal-fired power unit. This historical operating data includes at least one of the following: power generation curves, used to characterize the changes in active power output of the unit over a continuous period; heating curves, used to record the temporal distribution of heating load for combined heat and power (CHP) units; operating load rate, i.e., the sequence of ratios between the unit's actual output and rated capacity; main steam parameters, such as temperature and pressure, used to assist in judging the unit's operating status and efficiency; and coal feed rate curves, used to reflect the instantaneous or cumulative consumption of coal fed into the furnace. These data collectively constitute a digital description of the unit's historical operating status.

[0041] S42: Obtain carbon emission characteristics data for each coal-fired power unit within the target area; The carbon emission characteristics data include at least one of the following: fuel consumption data, fuel elemental carbon content data, fuel lower heating value data, and carbon oxidation rate data.

[0042] In this step, carbon emission characteristic data for each coal-fired power unit is collected from the coal metering system, coal quality test reports, and the enterprise's carbon emission accounting ledger. This includes at least one of the following: fuel consumption data, i.e., the total amount of coal and other fuels (such as co-fired biomass and sludge) consumed by each unit during the statistical period; fuel elemental carbon content data, preferably using the measured value of the received carbon content of the coal; if this cannot be measured, the reference default value in the accounting guide appendix is ​​used, with the uncertainty range indicated; lower heating value of fuel data, used to convert fuel consumption into energy input, which can be obtained through measurement or by using the default value; and carbon oxidation rate data, reflecting the proportion of carbon in the fuel oxidized during combustion, usually using industry standard values ​​or determined based on the measured value of the unit's combustion efficiency. These data collectively constitute the basic parameter set for unit carbon emission accounting.

[0043] S43: Obtain data on energy-saving and carbon-reduction measures for each coal-fired power unit; The data on energy conservation and carbon reduction measures includes the impact factors of the implemented measures on the dynamic change curve of load rate-coal consumption for power supply of coal-fired power units, as well as the expected performance parameters of the planned measures.

[0044] In this step, data on energy-saving and carbon-reduction measures are collected from the technical transformation plans, equipment operation manuals, or third-party energy-saving assessment reports of coal-fired power plants within the target area. This data includes the impact factors of implemented measures on the dynamic curve of load rate versus coal consumption for power generation of coal-fired power units, as well as the expected performance parameters of planned measures. For implemented measures such as turbine flow path modification, boiler combustion optimization, and flexibility modification, the impact factor is a correction coefficient obtained from performance comparison tests before and after the modification (e.g., 0.98, indicating a 2% reduction in coal consumption for power generation at the same load rate). This impact factor is used to multiply or add to the original dynamic curve of load rate versus coal consumption for power generation to reflect the efficiency improvement effect of the unit after the measures take effect. For planned measures such as heating system modification and carbon capture and storage, the expected performance parameters include the expected reduction in coal consumption for power generation (e.g., a reduction of 5 g / kWh), the expected increase in investment costs, the expected carbon emission reduction rate, and the expected implementation time of the measures. These parameters are used to assess the potential emission reduction effect of the measures before their actual application in simulation predictions. The above data, after being processed, can be used by subsequent carbon emission prediction models.

[0045] S44: Based on the historical operating data of each coal-fired power unit, construct the dynamic change curve of power supply coal consumption of each coal-fired power unit under multiple preset load rates, and use the curve as the load rate-power supply coal consumption relationship model.

[0046] In this step, the coal consumption for power generation of each coal-fired power unit during stable operation at different preset load rates (e.g., 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100%) is extracted from the acquired historical operating data. Each load rate corresponds to multiple historical sample points, and the average or median is taken as the typical coal consumption value at that load rate. Then, regression analysis (e.g., multinomial regression) or interpolation methods (e.g., cubic spline interpolation) are used to fit these discrete points, constructing a continuous, smooth, and dynamic curve that reflects the nonlinear upward trend of coal consumption in low-load areas. This curve, with load rate as the independent variable and coal consumption for power generation as the dependent variable, represents the load rate-coal consumption relationship model for each coal-fired power unit. This model can quickly output the corresponding dynamic coal consumption for power generation based on any input load rate value, providing an efficiency mapping basis for subsequent unit-level carbon emission prediction models.

[0047] By using the above methods, the dynamic curve of carbon emissions driven by the power output level can be obtained, which can accurately capture carbon emission fluctuations under transient conditions such as deep peak shaving and frequent start-stop, and output the total amount of carbon emissions, intensity, peak and valley characteristics and dynamic curves, overcoming the shortcomings of traditional methods in predicting the annual total amount or average intensity with coarse granularity.

[0048] In practical applications, a dynamic database is used to store multi-source heterogeneous data, specifically including: storing key parameters under different preset development paths, such as annual wind power penetration rate, annual photovoltaic penetration rate, net cross-regional electricity intake, and system peak-shaving demand; storing the results output by the system dynamics model, including the total output curves of each coal-fired power unit or the expected load rate data of each unit in each typical period (e.g., 8760 hours per year) under different scenarios; and storing the unit performance parameters involved in the unit-level carbon emission prediction model, including the dynamic power supply coal consumption function, start-up and shutdown energy consumption parameters, and heating condition correction coefficients under different load rates.

[0049] S50: Based on the historical operating data and carbon emission characteristics data of each coal-fired power unit, construct a unit-level carbon emission prediction model.

[0050] In this step, a unit-level carbon emission prediction model is constructed based on the acquired operational and carbon emission characteristic data of each coal-fired power unit. This model is a modular computational framework oriented towards a single coal-fired power unit and capable of responding to dynamic operating condition inputs. First, the load rate-power supply coal consumption relationship model of each unit serves as the core efficiency mapping module. This module receives externally input data on the expected output level of the unit and outputs the corresponding dynamic power supply coal consumption value. Second, the model incorporates a fuel consumption conversion module, which maps operating conditions to fuel consumption rates based on the physical relationship between power supply coal consumption, power generation, and fuel calorific value. For heating units, the model additionally includes a heat allocation module to distinguish the fuel consumption share of electricity output and heat output. Third, the model integrates a carbon emission accounting module, which uses measured carbon content as the accounting logic based on fuel consumption and fuel carbon content. Finally, the model includes an interface for integrating the effects of energy-saving and carbon-reducing measures, loading the impact factors of these measures, and automatically correcting key parameters in the efficiency mapping module based on the effectiveness status of the measures. The completed unit-level carbon emission prediction model has hourly dynamic response capability. Its input interface receives expected output level data (such as hourly load rate sequence), and its output interface provides carbon emission dynamic curves, carbon emission intensity, and cumulative total emissions, thus forming a complete mapping link from operating conditions to carbon emission results.

[0051] In one embodiment of this application, a specific scheme for constructing a unit-level carbon emission prediction model is provided. In S50, that is, based on the historical operating data and carbon emission characteristic data of each coal-fired power unit, a unit-level carbon emission prediction model is constructed, which specifically includes the following steps: For any coal-fired power unit, if the coal-fired power unit has the capability to measure the carbon content of fuel elements, the emissions from the combustion of fossil fuels can be calculated using the measured carbon content method. The formula for calculating emissions from fossil fuel combustion is:

[0052] Among them, E 燃烧 Emissions from the combustion of fossil fuels, expressed in tons of carbon dioxide (tCO2); FC i The consumption of the i-th type of fossil fuel is expressed in tons (t) for solid or liquid fuels and in ten thousand standard cubic meters (10⁻⁶) for gaseous fuels. 4 Nm 3 ); C ar,i The carbon content of the i-th fossil fuel is expressed as the basic element. For solid or liquid fuels, the unit is tons of carbon per ton (tC / t), and for gaseous fuels, the unit is tons of carbon per 10,000 standard cubic meters (tC / 10⁻¹⁰). 4 Nm 3 ); OF idenoted as , representing the carbon oxidation rate of the i-th fossil fuel, expressed as %; 44 / 12 is the ratio of the relative molecular masses of carbon dioxide to carbon; and i is the code for the type of fossil fuel. Optionally, for coal elemental carbon measurements, the received-basis elemental carbon content is calculated based on the air-dried basis elemental carbon content and the received-basis moisture content, or based on the dried basis elemental carbon content and the received-basis moisture content. The formula for converting the received-basis elemental carbon content is:

[0053] Among them, C ar The carbon content of the basic element is given, in tons of carbon per ton (tC / t); C ad The elemental carbon content is based on air drying, expressed in tons of carbon per ton (tC / t); M ar To determine the base moisture content, measurements from key emission units are used, expressed as a percentage (%). ad Moisture content is expressed as a percentage (%) based on the measured sample value; or:

[0054] Among them, C d The carbon content is on a dry basis, in tons of carbon per ton (tC / t). In the absence of the ability to measure the carbon content of fuel elements in coal-fired power units, the carbon content of the fuel elements is calculated by the calorific value method based on the received lower heating value and the carbon content per unit calorific value. The formula for calculating calorific value using the calorific value method is:

[0055] Among them, C ar,i The carbon content of the i-th fossil fuel is expressed as the basic element. For solid or liquid fuels, the unit is tons of carbon per ton (tC / t), and for gaseous fuels, the unit is tons of carbon per 10,000 standard cubic meters (tC / 10⁻¹⁰). 4 Nm 3 ); NCV ar,i The lower heating value of the i-th fossil fuel is expressed as a received basis, in gigajoules per ton (GJ / t) for solid or liquid fuels, and in gigajoules per 10,000 standard cubic meters (GJ / 10⁻¹⁰) for gaseous fuels. 4 Nm 3 ); CC i denoted as the carbon content per unit calorific value of the i-th fossil fuel, expressed in tons of carbon per gigajoul (tC / GJ). When biomass is co-fired in a coal-fired power unit, calculate the percentage of heat generated by the co-fired biomass in the total heat generated by the coal-fired power unit. The formula for calculating the percentage of calories is:

[0056] Among them, P biomass Q represents the percentage of biomass co-firing heat in the total fuel heat of a coal-fired power unit, expressed as a percentage. cr The boiler's heat output is expressed in gigajoules (GJ); η gl Boiler efficiency, expressed as a percentage; FC i The consumption of the i-th type of fossil fuel is expressed in tons (t) for solid or liquid fuels and in ten thousand standard cubic meters (10⁻⁶) for gaseous fuels. 4 Nm 3 ); NCV ar,i The lower heating value of the i-th fossil fuel is expressed as a received basis, in gigajoules per ton (GJ / t) for solid or liquid fuels, and in gigajoules per 10,000 standard cubic meters (GJ / 10⁻¹⁰) for gaseous fuels. 4 Nm 3 ).

[0057] In this embodiment, for any coal-fired power unit, if the unit has the capability to measure the elemental carbon content of the fuel, the measured carbon content method is preferentially used to calculate the emissions from fossil fuel combustion. This is because the elemental carbon content obtained through direct sampling and analysis of the coal fed into the furnace best reflects the true carbon input of the fuel, minimizing the uncertainty caused by using default values, thus ensuring the accuracy of carbon emission accounting and facilitating enterprise-level verification. If the unit lacks the capability for measurement, the calorific value method is used instead, estimating the elemental carbon content based on the received basis lower heating value and carbon content per unit calorific value. The calorific value method is a standard alternative method recognized in accounting guidelines. The lower heating value of fuel can usually be routinely measured, and combined with industry averages or government-published default values ​​for carbon content per unit calorific value, acceptable estimation accuracy can be obtained even without measured carbon data. Furthermore, when a coal-fired power unit co-fires biomass (including waste and sludge), the proportion of biomass co-firing heat to the total fuel heat of the unit needs to be calculated. This is because biomass fuels are typically considered zero-carbon or low-carbon fuels, and their heat contribution must be separated from the total fuel heat; otherwise, the biomass component would be included in total emissions according to the carbon content of fossil fuels. Only by calculating them separately can the carbon dioxide emissions generated solely from the combustion of fossil fuels be accurately calculated, thus meeting the requirement in carbon accounting that biomass carbon is not included in emissions.

[0058] S60: Based on expected power output data, historical operating data, carbon emission characteristic data, energy-saving and carbon reduction measures data, load factor-power supply coal consumption relationship model, and unit-level carbon emission prediction model, it performs coupled calculations of macro-regional power development scenarios and micro-coal-fired power unit operating characteristics in the target area, and generates carbon emission prediction results for each coal-fired power unit under various preset development paths.

[0059] In this step, for each preset development path, the hourly expected output data of each coal-fired power unit in the target area under that path is used as a driving variable and input into the corresponding unit-level carbon emission prediction model. The model initializes the performance curves by combining the historical operating data of each unit, calls the load factor-power supply coal consumption relationship model to convert the expected output level into dynamic power supply coal consumption, and then calculates the carbon emissions hourly based on the fuel carbon content and lower heating value in the carbon emission characteristic data. At the same time, the calculation results are corrected based on the influencing factors in the energy-saving and carbon reduction measures data. By traversing all preset development paths and all coal-fired power units, the carbon emission prediction results of each coal-fired power unit under each preset development path are finally generated. The results include at least the total annual carbon emissions, the hourly dynamic carbon emission curve, the carbon emission intensity sequence, and peak-valley characteristic values.

[0060] By combining system dynamics models with unit-level carbon emission prediction models, the above approach enables the prediction of multiple future scenarios and dynamic evolution processes. It allows for the comparison of carbon emission differences under various preset paths, such as the baseline scenario, the high renewable energy development scenario, and the coal power flexibility support scenario, providing quantitative support for carbon peaking path planning.

[0061] In one embodiment of this application, a specific scheme for generating carbon emission prediction results is provided. In S60, based on expected power output data, historical operating data, carbon emission characteristic data, energy-saving and carbon reduction measures data, load factor-power supply coal consumption relationship model, and unit-level carbon emission prediction model, a coupled calculation of macro-regional power development scenario and micro-coal-fired power unit operating characteristics is performed on the target area to generate carbon emission prediction results for each coal-fired power unit under various preset development paths. Specifically, this includes the following steps S61-S67: S61: For any coal-fired power unit, input the expected output level data into the load rate-power supply coal consumption relationship model of each coal-fired power unit to determine the dynamic power supply coal consumption corresponding to the expected output level data.

[0062] In this step, for each coal-fired power unit, its expected output level data L(t) within a preset time period is used as an input variable and substituted into the constructed load factor-power supply coal consumption relationship model. The model outputs the corresponding dynamic power supply coal consumption g(t) based on the input value, which is the amount of standard coal consumed by the unit per unit of power generation under this operating condition.

[0063] S62: Determine the dynamic fuel consumption rate based on dynamic power supply coal consumption, expected power output data, and fuel low calorific value data.

[0064] In this step, for each coal-fired power unit, the dynamic coal consumption for power supply g(t) of the unit at each moment within a preset time period is obtained, and the power generation P(t) of the unit at the corresponding moment is obtained from the generated expected output level data. Combined with the received basis lower heating value Q of the fuel used from the collected carbon emission characteristic data, the process is completed. net， Calculate the dynamic fuel consumption rate FC(t) at this moment, where the formula for FC(t) is: FC(t) = P(t) × g(t) / Q net This calculation yields the hourly fuel consumption sequence of the unit at each moment, which is then used for the dynamic calculation of subsequent carbon emissions.

[0065] S63: If the coal-fired power unit is a heating unit, the total coal consumption shall be dynamically allocated to the power output and heat output using the heat allocation method.

[0066] In this step, for each cogeneration unit, if the coal-fired power unit is a heating unit, its total fuel consumption simultaneously produces both electricity and heat. Therefore, the total coal consumption needs to be rationally allocated to the power supply and heating stages according to a certain allocation principle. The core of the heat allocation method is to calculate based on the energy ratio of heat supply to power generation under heating conditions, typically using the heat ratio as the allocation coefficient. First, obtain the heat supply curve and power generation curve of the unit at the corresponding time, converting the heat supply into equivalent power generation using thermal-work equivalents or directly using energy units. Then, calculate the heat ratio, which is the proportion of heat supply to the unit's total heat output (the sum of heat supply and power generation). Finally, multiply the calculated dynamic total coal consumption by the power generation allocation coefficient (1 minus the heat ratio) to obtain the power supply coal consumption, and multiply by the heat ratio to obtain the heating coal consumption.

[0067] The above method can accurately distinguish the amount of fuel consumed by electricity production and heat production, thereby calculating carbon emissions from power supply and heating separately, meeting the requirements for refined carbon emission accounting of combined heat and power units.

[0068] S64: Determine the dynamic carbon emission curves for each coal-fired power unit based on dynamic fuel consumption rate and fuel carbon content.

[0069] In this step, for each coal-fired power unit, the calculated dynamic fuel consumption rate FC(t) at each time point is compared with the obtained carbon content C of the corresponding fuel. arMultiplying the carbon oxidation rate OF (expressed as a percentage) by the ratio of carbon dioxide to carbon molecular weight, 44 / 12, yields the carbon dioxide emission rate E(t) at that moment. For heating units, if the total coal consumption has been allocated to electricity and heat output, the fuel consumption rates corresponding to power supply and heating are substituted into formula (1) to obtain the carbon emission rates for power supply and heating, respectively. Arranging the emission rates at all moments in chronological order forms the dynamic carbon emission curve E(t) of the unit within a preset time period. This curve can precisely depict the carbon emission fluctuation characteristics of the unit under different operating conditions (including base load, peak shaving, start-up and shutdown), providing basic data for subsequent multi-scenario prediction and emission reduction effect evaluation.

[0070] S65: The influence factors of energy-saving and carbon-reduction measures data are built into the unit-level carbon emission prediction model. When the simulated energy-saving and carbon-reduction measures take effect, the dynamic power supply coal consumption and dynamic fuel consumption rate are corrected based on the influence factors to update the dynamic carbon emission curve.

[0071] In this step, the unit-level carbon emission prediction model is pre-loaded with data on energy-saving and carbon-reduction measures. This includes the impact factors of implemented measures on the dynamic change curve of load rate-power supply coal consumption (e.g., a multiplication factor of 0.98, indicating a 2% reduction in power supply coal consumption at the same load rate), and the expected performance parameters of planned measures. During the simulation, when a certain measure is pre-set to be in effect, the model automatically applies the impact factor corresponding to that measure to the dynamic power supply coal consumption g(t), i.e., the corrected dynamic power supply coal consumption g′(t) = g(t) × α, where α is the impact factor (α < 1 for measures that reduce coal consumption). Subsequently, based on the corrected dynamic power supply coal consumption, the model re-executes the carbon emission calculation, generating an updated dynamic carbon emission curve E′(t).

[0072] Through the above methods, the model can quantitatively evaluate the differentiated emission reduction effects of energy-saving and carbon-reducing measures under different operating conditions, providing a contextual basis for technological upgrading investment decisions.

[0073] S66: Input the expected output level data of each coal-fired power unit into the unit-level carbon emission prediction model, and combine it with the current performance status of the target coal-fired power unit or the technical transformation measures planned to be implemented in the future to calculate the total predicted carbon emissions of each coal-fired power unit within a preset time period under various preset development paths.

[0074] In this step, for each preset development path, the expected output level data of each coal-fired power unit under that path is used as the driving input and loaded into the constructed unit-level carbon emission prediction model. The model first reads the current performance status of each unit, including the calibrated load factor-power coal consumption relationship model, the current fuel carbon content, and carbon emission characteristic parameters such as lower heating value. If there are planned technological upgrades to be implemented in the future, the corresponding impact factors or expected performance parameters of the upgrades are loaded simultaneously, and the upgrades are set to active status. Subsequently, the model calculates the carbon emissions of each unit and accumulates them over a preset time period (such as a calendar year) to obtain the total predicted carbon emissions of each coal-fired power unit under that path. The above process is repeated, traversing all preset development paths and all coal-fired power units, and finally outputting the total predicted carbon emissions of each coal-fired power unit under different paths for multi-scenario comparative analysis and emission reduction effect evaluation.

[0075] S67: Based on the updated carbon emission dynamic curve, determine the peak and valley characteristics of carbon emissions, and based on the updated carbon emission dynamic curve, the peak and valley characteristics of carbon emissions, and the total predicted carbon emissions, generate the carbon emission prediction results for each coal-fired power unit under various preset development paths.

[0076] In this step, for each coal-fired power unit and each preset development path, the peak and valley characteristics of carbon emissions are determined based on the updated carbon emission dynamic curve, such as the period of highest emissions and the additional emission increment during peak shaving. Subsequently, the updated carbon emission dynamic curve, the calculated total carbon emission forecast for the unit under that path, and the peak and valley characteristics of carbon emissions are integrated to obtain the carbon emission forecast result. The carbon emission dynamic curve is used to show the change in carbon emissions of coal-fired power units over time within a preset period, especially the emission fluctuation characteristics under transient operating conditions such as deep peak shaving and start-up / shutdown; the total carbon emission forecast within the preset period serves as the basis for enterprises to formulate carbon emission compliance and emission reduction targets.

[0077] Optionally, for heating units, carbon emission intensity indicators can be calculated by comparing total carbon emissions with corresponding output, including carbon emission intensity per unit of electricity generation and carbon emission intensity per unit of heat supply, for cross-comparison of the clean production levels of different units.

[0078] In practical applications, comparative analysis reports can be generated based on carbon emission prediction results. For example, under a high renewable energy development scenario, the increased wind power penetration leads to a 10% decrease in the average load factor of coal-fired power plants. This includes an increase of 200 hours in deep peak shaving (load factor below 40%), resulting in an 8g / kWh increase in the average coal consumption for power generation, ultimately leading to a 5% increase in total carbon emissions compared to the baseline scenario. Through this type of analysis, the differences in total carbon emissions under different scenarios can be compared, and the causes of these differences can be analyzed.

[0079] By coupling the macro-regional power system evolution with the micro-coal-fired power unit operating characteristics, the method breaks the isolation of traditional methods that only focus on internal unit data. It can quantitatively analyze the dynamic impact of macro-factors such as regional renewable energy penetration rate, inter-regional power transmission plans, and seasonal fluctuations on coal-fired power carbon emissions, and significantly improve the correlation between prediction results and changes in the external power environment.

[0080] In one embodiment of this application, a specific visualization scheme for prediction results is provided. This scheme, based on expected power output data, historical operating data, carbon emission characteristic data, energy-saving and carbon-reduction measures data, a load factor-power supply coal consumption relationship model, and a unit-level carbon emission prediction model, performs coupled calculations of macro-regional power development scenarios and micro-level coal-fired power unit operating characteristics to generate carbon emission prediction results for each coal-fired power unit under various preset development paths. The scheme further includes the following steps: Based on a preset display format, the carbon emission prediction results are graphically processed. The carbon emission prediction results after graphical processing are visualized.

[0081] In this embodiment, the carbon emission prediction results of each coal-fired power unit under various preset development paths are graphically output according to a preset display format.

[0082] Optionally, the preset display format includes at least the following two types of views: a scenario comparison dashboard, which uses parallel bar charts, line charts, or dashboard components to compare and display the total carbon emissions, carbon emission intensity per unit of power generation, and estimated carbon emission cost curves of coal-fired power units under different preset development paths on the same interface, making it easy for decision-makers to intuitively identify the emission reduction potential and cost differences of different paths; and a carbon emission source tracing view, which uses a time-series chart that is linked vertically. The upper part displays the regional wind power output curve, photovoltaic power output curve, and grid net load curve (the remaining load after subtracting the output of new energy from the load), while the lower part simultaneously displays the actual load rate curve and real-time carbon emission intensity curve of coal-fired power units. When the user selects a time period (such as the deep peak shaving period of a certain day) in the upper chart, the lower chart automatically links and highlights the changes in the load rate and carbon emission intensity of the units within the corresponding time period, thereby intuitively revealing the dynamic driving relationship of the external power environment (such as fluctuations in new energy output and changes in net load peak and valley) on the carbon emissions of the units.

[0083] In practical application scenarios, an interactive simulator is set up, allowing users to adjust regional new energy installed capacity targets by dragging and dropping, or to select the technical upgrade measures to be implemented by the unit. The system calls the existing load factor-power supply coal consumption relationship model and the unit-level carbon emission prediction model in real time, recalculates and dynamically displays the changes in the prediction results.

[0084] The above methods help coal-fired power companies quickly understand the impact of regional power development on their own carbon emissions, providing intuitive and interactive support tools for production scheduling and low-carbon investment decisions.

[0085] In practical application scenarios, take a 600MW supercritical unit in a certain provincial region (installed capacity 45GW, coal power accounts for 57%) as an example. First, scenario simulation: under the high renewable energy installed capacity target scenario (renewable energy accounts for 54% by 2030), the power balance analysis of the region is carried out according to the power installed capacity plan to predict the future coal power generation utilization hours. The balance principle is as follows: (1) The average annual utilization hours of conventional hydropower units are considered to be 2000 hours. (2) The average annual utilization hours of pumped storage power stations under power generation and pumping conditions are considered to be 1500 hours and 2000 hours respectively (net utilization hours are -500 hours). (3) The net utilization hours of electrochemical energy storage power stations are considered to be -260 hours (charge and discharge efficiency is 86%). (4) The average annual utilization hours of wind turbine units are considered to be 2500 hours. (5) The average annual utilization hours of photovoltaic power generation units are considered to be 1600 hours. (6) The total social electricity consumption is considered to be 4.0% per year. Electricity balance calculations were performed under two scenarios: one based on the regional planned wind and solar installed capacity targets, and the other based on the actual wind and solar installed capacity growth rate over the past five years. The average utilization hours of coal-fired power plants were then calculated. The results show that the utilization hours of coal-fired power plants will be 3631 hours in 2025 and 2483 hours in 2030. The number of hours the unit undertakes deep peak shaving (load rate below 40%) will increase from the current 500 hours to 1200 hours, and the number of days with two-shift operation (daily start-stop) will increase significantly. Secondly, based on the historical operating data of this 600MW unit, its load rate-power supply coal consumption relationship curve was fitted. When the load rate decreases from 100% to 40%, the power supply coal consumption increases from 290g / kWh to 320g / kWh. A dynamic carbon emission prediction model for this unit was established, enabling it to accept unit output level data as input. The measured carbon content of the coal used in this 600MW supercritical unit is 0.4555 tC / t. Under the conditions of generating 2.28 million MWh of electricity and supplying 2.26 million GJ of heat, the carbon emissions are 2.26 million tCO2. After simulating carbon reduction measures such as adjusting fly ash carbon content, turbine overhaul, and addressing low reheat steam temperature, and comprehensively considering the effects of deep peak shaving and frequent start-ups and shutdowns, carbon emissions can be reduced by 36,751 tCO2 / year, power generation emission intensity reduced by 0.0144 tCO2 / MWh, heating emission intensity reduced by 0.0021 tCO2 / GJ, and coal consumption for power generation reduced by 5.82 g / kWh. After conversion, annual carbon emissions are reduced by 1.63%, power generation emission intensity by 1.63%, heating emission intensity by 1.63%, and coal consumption for power generation by 1.94%. Finally, by coupling the simulation results of the high renewable energy installed capacity scenario with the simulation prediction results, it was found that the total carbon emissions of the 600MW supercritical unit are reduced by about 40% compared with the baseline scenario. At the same time, the changes in carbon emission intensity of power generation and heating, as well as coal consumption for power generation, can be predicted simultaneously and displayed through a visualization interface.

[0086] As can be seen, the above scheme acquires historical and planning data of the power system in the target area, as well as multiple preset development paths, and determines the power system development parameters under each path. Subsequently, system dynamics are used to dynamically extrapolate each preset path, generating expected output level data for each coal-fired power unit. Then, historical operating data, carbon emission characteristic data, energy-saving and carbon-reduction measure data, and a load factor-power supply coal consumption relationship model of the coal-fired power units are acquired to construct a unit-level carbon emission prediction model. The expected output level data is input into this model, and a coupled calculation of macro-level power development scenarios and micro-level unit operating characteristics is performed to generate carbon emission prediction results for each unit under each preset path. By transmitting the dynamic impact of regional power system evolution on coal-fired power plant operating conditions to unit carbon emission calculations, multi-scenario, hourly, and refined carbon emission prediction is achieved. This overcomes the shortcomings of traditional methods, which are isolated, static, and coarse-grained. It can quantify the total carbon emissions, intensity, and peak-shaving additional emissions under different development paths, providing a scientific basis for low-carbon decision-making by coal-fired power enterprises and regional carbon peaking path planning.

[0087] In one embodiment, a carbon emission prediction device for coal-fired power units is provided, which corresponds one-to-one with the carbon emission prediction method for coal-fired power units described in the above embodiments. For example... Figure 2 As shown, the carbon emission prediction device 100 for the coal-fired power unit includes: a first acquisition module 101, a determination module 102, a first generation module 103, a second acquisition module 104, a construction module 105, and a second generation module 106. Detailed descriptions of each functional module are as follows: The first acquisition module 101 is used to acquire historical power system data, power system planning data, and multiple preset development paths for the target area. The determination module 102 is used to determine the power system development parameters for various preset development paths based on historical power system data and power system planning data. The first generation module 103 is used to generate expected power output data of each coal-fired power unit in the target area under various preset development paths within a preset time period by using a system dynamics model to extrapolate various preset development paths based on historical power system data, power system planning data and power system development parameters. The second acquisition module 104 is used to acquire historical operating data, carbon emission characteristic data, energy-saving and carbon reduction measures data of each coal-fired power unit in the target area, as well as the load rate-power supply coal consumption relationship model of each coal-fired power unit. Module 105 is used to build a unit-level carbon emission prediction model based on the historical operating data and carbon emission characteristic data of each coal-fired power unit. The second generation module 106 is used to perform coupled calculations of macro-regional power development scenarios and micro-coal-fired power unit operating characteristics on the target area based on expected power output level data, historical operating data, carbon emission characteristic data, energy-saving and carbon reduction measures data, load factor-power supply coal consumption relationship model and unit-level carbon emission prediction model, and generate carbon emission prediction results for each coal-fired power unit under various preset development paths.

[0088] In one embodiment, the historical power system data of the target area includes at least one of the following: the total electricity consumption of the target area over the years, typical daily and annual grid load curves, the installed capacity of various preset power sources, historical output curves, historical power generation, and historical utilization hours. The power system planning data for the target area includes at least one of the following: the new installed capacity of various types of pre-planned power sources during the pre-planning period, the renewable energy consumption responsibility weight target, the planned capacity of inter-regional transmission channels and power transmission and receiving plans, and the policy on adjusting the functional positioning of coal-fired power plants.

[0089] In one embodiment, the first generation module 103 is specifically used for: Construct a system dynamics model and configure multiple preset constraints in the system dynamics model; Historical power system data, power system planning data, and power system development parameters are input into the system dynamics model. Through the system dynamics model and multiple preset constraints, the annual output level of each coal-fired power unit required to meet system balance under various preset development paths is dynamically deduced, and the expected output level data of each coal-fired power unit within a preset time period is generated.

[0090] In one embodiment, the second acquisition module 104 specifically includes: Obtain historical operating data for each coal-fired power unit within the target area. The historical operating data includes at least one of the following: power generation curve, heating curve, operating load rate, main steam parameters, and coal feed rate curve. Obtain carbon emission characteristic data for each coal-fired power unit within the target area. The carbon emission characteristic data includes at least one of the following: fuel consumption data, fuel elemental carbon content data, fuel lower heating value data, and carbon oxidation rate data. Data on energy conservation and carbon reduction measures for each coal-fired power unit were obtained. This data included the impact factors of the implemented measures on the dynamic change curve of load rate-coal consumption for power supply of the coal-fired power unit, as well as the expected performance parameters of the planned measures. Based on the historical operating data of each coal-fired power unit, dynamic change curves of power supply coal consumption for each coal-fired power unit under multiple preset load rates are constructed, and these curves are used as load rate-power supply coal consumption relationship models.

[0091] In one embodiment, the construction module 105 is specifically used for: For any coal-fired power unit, if the coal-fired power unit has the capability to measure the carbon content of fuel elements, the emissions from the combustion of fossil fuels can be calculated using the measured carbon content method. In the absence of the ability to measure the carbon content of fuel elements in coal-fired power units, the carbon content of the fuel elements is calculated by the calorific value method based on the received lower heating value and the carbon content per unit calorific value. When biomass is co-fired in a coal-fired power unit, calculate the percentage of heat generated by the co-fired biomass in the total heat generated by the coal-fired power unit.

[0092] In one embodiment, the second generation module 106 is specifically used for: For any coal-fired power unit, the expected output level data is input into the load rate-power supply coal consumption relationship model of each coal-fired power unit to determine the dynamic power supply coal consumption corresponding to the expected output level data; The dynamic fuel consumption rate is determined based on dynamic power supply coal consumption, expected power output data, and fuel lower heating value data. If the coal-fired power unit is a heating unit, the total coal consumption is dynamically allocated to electricity output and heat output using the heat allocation method. Based on dynamic fuel consumption rate and fuel carbon content, determine the dynamic carbon emission curves of each coal-fired power unit; The influence factors of energy-saving and carbon-reduction measures data are built into the unit-level carbon emission prediction model. When the simulated energy-saving and carbon-reduction measures take effect, the dynamic power supply coal consumption and dynamic fuel consumption rate are corrected based on the influence factors to update the dynamic carbon emission curve. The expected output level data of each coal-fired power unit is input into the unit-level carbon emission prediction model. Combined with the current performance status of the target coal-fired power unit or the technical transformation measures planned for the future, the total predicted carbon emissions of each coal-fired power unit in the preset time period under various preset development paths are calculated. Based on the updated carbon emission dynamic curve, the peak and valley characteristics of carbon emissions are determined. Based on the updated carbon emission dynamic curve, the peak and valley characteristics of carbon emissions, and the total predicted carbon emissions, the carbon emission prediction results of each coal-fired power unit under various preset development paths are generated.

[0093] In one embodiment, the device further includes: The data processing module is used to graphically process the carbon emission prediction results based on a preset display format; The display module is used to visualize the graphically processed carbon emission prediction results.

[0094] This invention provides a carbon emission prediction device 100 for coal-fired power units. It acquires historical and planning data of the power system in a target area, as well as various preset development paths, and determines the power system development parameters under each path. Subsequently, it uses system dynamics to dynamically extrapolate each preset path, generating expected output level data for each coal-fired power unit. Then, it acquires historical operating data, carbon emission characteristic data, energy-saving and carbon-reduction measure data, and a load factor-power supply coal consumption relationship model for the coal-fired power units, constructing a unit-level carbon emission prediction model. The expected output level data is input into this model, and a coupled calculation of macro-level power development scenarios and micro-level unit operating characteristics is performed to generate carbon emission prediction results for each unit under each preset path. By transmitting the dynamic impact of regional power system evolution on coal-fired power plant operation to the unit carbon emission calculation, it achieves multi-scenario, hourly, and refined carbon emission prediction, overcoming the shortcomings of traditional methods that are isolated, static, and coarse-grained. It can quantify the total carbon emissions, intensity, and peak-shaving additional emissions under different development paths, providing a scientific basis for low-carbon decision-making by coal-fired power enterprises and regional carbon peaking path planning.

[0095] Specific limitations regarding the carbon emission prediction device for coal-fired power units can be found in the limitations on carbon emission prediction methods for coal-fired power units described above, and will not be repeated here. Each module in the aforementioned carbon emission prediction device for coal-fired power units can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in the electronic device, or stored in the memory of the electronic device as software, so that the processor can call and execute the corresponding operations of each module.

[0096] In one embodiment, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the aforementioned carbon emission prediction method for coal-fired power units.

[0097] In one embodiment, a computer-readable storage medium is provided, which stores a computer program that, when executed by a processor, implements the aforementioned carbon emission prediction method for coal-fired power units.

[0098] It should be noted that the functions or steps that can be implemented by the computer-readable storage medium or electronic device described above can be referred to the relevant descriptions on the server side and client side in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0099] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0100] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0101] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. 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 of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for carbon emission prediction of a coal-fired power plant, characterized in that, include: Acquire historical power system data, power system planning data, and multiple preset development paths for the target area; Based on the historical data and planning data of the power system, the power system development parameters for various preset development paths are determined; Based on the historical data of the power system, the planning data of the power system, and the development parameters of the power system, the expected output level data of each coal-fired power unit in the target area under various preset development paths are generated through the system dynamics model. Acquire historical operating data, carbon emission characteristics data, energy-saving and carbon-reduction measures data, and load rate-power coal consumption relationship models for each coal-fired power unit within the target area; Based on the historical operating data and carbon emission characteristic data of each coal-fired power unit, a unit-level carbon emission prediction model is constructed. Based on the expected power output data, the historical operating data, the carbon emission characteristic data, the energy-saving and carbon reduction measures data, the load factor-power supply coal consumption relationship model, and the unit-level carbon emission prediction model, a coupled calculation of macro-regional power development scenarios and micro-coal-fired power unit operating characteristics is performed on the target area to generate carbon emission prediction results for each coal-fired power unit under various preset development paths.

2. The carbon emission prediction method for coal-fired power units according to claim 1, characterized in that, Historical power system data for the target area includes at least one of the following: total electricity consumption of the target area over the years, typical daily and annual grid load curves, installed capacity of various preset power sources, historical output curves, historical power generation, and historical utilization hours. The power system planning data for the target area includes at least one of the following: the new installed capacity of various types of pre-planned power sources during the pre-planning period, the renewable energy consumption responsibility weight target, the planned capacity of inter-regional transmission channels and power transmission and receiving plans, and the policy on adjusting the functional positioning of coal-fired power plants.

3. The method of carbon emissions prediction for a coal-fired power plant of claim 1, wherein, The step of generating expected power output data for each coal-fired power unit in the target area under various preset development paths within a preset time period, based on the historical data, planning data, and development parameters of the power system, through a system dynamics model, specifically includes: Construct the system dynamics model and configure multiple preset constraints in the system dynamics model; The historical power system data, the power system planning data, and the power system development parameters are input into the system dynamics model. Through the system dynamics model and the multiple preset constraints, the annual output level of each coal-fired power unit required to meet system balance under various preset development paths is dynamically deduced, and the expected output level data of each coal-fired power unit within a preset time period is generated.

4. The method of carbon emissions prediction for a coal-fired power plant of claim 1, wherein, The steps of acquiring historical operating data, carbon emission characteristic data, energy-saving and carbon-reduction measures data, and load factor-power coal consumption relationship models for each coal-fired power unit within the target area specifically include: Obtain historical operating data of each coal-fired power unit within the target area, wherein the historical operating data includes at least one of the following: power generation curve, heating curve, operating load rate, main steam parameters, and coal feed rate curve; Obtain carbon emission characteristic data for each coal-fired power unit within the target area, wherein the carbon emission characteristic data includes at least one of the following: fuel consumption data, fuel elemental carbon content data, fuel lower heating value data, and carbon oxidation rate data; Data on energy-saving and carbon-reduction measures for each coal-fired power unit are obtained. The data includes the influence factors of the implemented measures on the dynamic change curve of load rate-coal consumption for power supply of the coal-fired power unit, as well as the expected performance parameters of the planned measures. Based on the historical operating data of each coal-fired power unit, dynamic change curves of power supply coal consumption for each coal-fired power unit under multiple preset load rates are constructed, and these curves are used as load rate-power supply coal consumption relationship models.

5. The method of carbon emissions prediction for a coal-fired power plant of claim 1 wherein, The steps for constructing a unit-level carbon emission prediction model based on the historical operating data and carbon emission characteristic data of each coal-fired power unit specifically include: For any coal-fired power unit, if the coal-fired power unit has the capability to measure the carbon content of fuel elements, the emissions from the combustion of fossil fuels can be calculated using the measured carbon content method. In the absence of the ability to measure the carbon content of fuel in coal-fired power units, the carbon content of the fuel is calculated by the calorific value method based on the received lower heating value and the carbon content per unit calorific value. When biomass is co-fired in a coal-fired power unit, calculate the percentage of heat generated by the co-fired biomass in the total heat generated by the coal-fired power unit.

6. The method of carbon emissions prediction for a coal-fired power plant of claim 1 wherein, The step of performing coupled calculations of macro-regional power development scenarios and micro-coal-fired power unit operating characteristics on the target area based on the expected power output level data, the historical operating data, the carbon emission characteristic data, the energy-saving and carbon reduction measures data, the load factor-power supply coal consumption relationship model, and the unit-level carbon emission prediction model, to generate carbon emission prediction results for each coal-fired power unit under various preset development paths, specifically includes: For any coal-fired power unit, the expected output level data is input into the load rate-power supply coal consumption relationship model of each coal-fired power unit to determine the dynamic power supply coal consumption corresponding to the expected output level data; The dynamic fuel consumption rate is determined based on dynamic power supply coal consumption, expected power output data, and fuel lower heating value data. If the coal-fired power unit is a heating unit, the total coal consumption is dynamically allocated to electricity output and heat output using the heat allocation method. Based on dynamic fuel consumption rate and fuel carbon content, determine the dynamic carbon emission curves of each coal-fired power unit; The influence factors of energy-saving and carbon-reduction measures data are built into the unit-level carbon emission prediction model. When the simulated energy-saving and carbon-reduction measures take effect, the dynamic power supply coal consumption and dynamic fuel consumption rate are corrected based on the influence factors to update the dynamic carbon emission curve. The expected output level data of each coal-fired power unit is input into the unit-level carbon emission prediction model. Combined with the current performance status of the target coal-fired power unit or the technical transformation measures planned for the future, the total predicted carbon emissions of each coal-fired power unit in the preset time period under various preset development paths are calculated. Based on the updated carbon emission dynamic curve, the peak and valley characteristics of carbon emissions are determined, and based on the updated carbon emission dynamic curve, the peak and valley characteristics of carbon emissions, and the total predicted carbon emissions, carbon emission prediction results for each coal-fired power unit under various preset development paths are generated.

7. The method of carbon emissions prediction for a coal-fired power plant of claim 1 wherein, After generating carbon emission prediction results for each coal-fired power unit under various preset development paths by performing coupled calculations of macro-regional power development scenarios and micro-coal-fired power unit operating characteristics on the target area based on the expected power output level data, the historical operating data, the carbon emission characteristic data, the energy-saving and carbon reduction measures data, the load factor-power supply coal consumption relationship model, and the unit-level carbon emission prediction model, the method further includes: The carbon emission prediction results are graphically processed based on a preset display format. The carbon emission prediction results after graphical processing are visualized.

8. A carbon emissions prediction device for a coal-fired power plant, comprising: include: The first acquisition module is used to acquire historical power system data, power system planning data, and multiple preset development paths for the target area. The determination module is used to determine the power system development parameters for various preset development paths based on the historical data and planning data of the power system. The first generation module is used to generate expected power output data of each coal-fired power unit in the target area under various preset development paths within a preset time period by using a system dynamics model to deduce various preset development paths based on the historical data of the power system, the planning data of the power system, and the development parameters of the power system. The second acquisition module is used to acquire historical operating data, carbon emission characteristic data, energy-saving and carbon reduction measures data of each coal-fired power unit in the target area, as well as the load rate-power supply coal consumption relationship model of each coal-fired power unit. The construction module is used to build a unit-level carbon emission prediction model based on the historical operating data and carbon emission characteristic data of each coal-fired power unit; The second generation module is used to perform coupled calculations of macro-regional power development scenarios and micro-coal-fired power unit operating characteristics on the target area based on the expected power output level data, the historical operating data, the carbon emission characteristic data, the energy-saving and carbon reduction measures data, the load factor-power supply coal consumption relationship model, and the unit-level carbon emission prediction model, and generate carbon emission prediction results for each coal-fired power unit under various preset development paths.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the carbon emission prediction method for coal-fired power units as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the carbon emission prediction method for coal-fired power units as described in any one of claims 1 to 7.

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