A low-carbon operation scheduling method and system for a coal-fired unit based on actual carbon emissions

Abstract

The application discloses a kind of low-carbon operation scheduling method and system of coal-fired unit based on measured carbon emission.The method of the application comprises: obtaining the carbon emission concentration of each coal-fired unit in real time;Based on historical measured data, the reference carbon emission concentration curve of each unit group under different load rates is constructed;According to the load demand of power grid, ultra-supercritical unit group is preferentially scheduled in the high load section of the preferred operation interval, and subcritical unit group is scheduled in the deep load peak section or standby state, to generate initial scheduling instruction;Execute initial scheduling instruction, obtain the real-time carbon emission concentration of target unit, if the real-time carbon emission concentration exceeds the preset threshold value on the reference carbon emission concentration curve of the same type unit group of the target unit under current load, then adjust the operating condition of the target unit or reallocate the load between units in reverse.The application realizes the organic integration of unit type differentiation scheduling and operation feedback correction, and can significantly improve the low-carbon operation level of coal-fired unit.

Description

Technical Field

[0001] This invention belongs to the field of unit operation and scheduling technology, and in particular to a low-carbon operation and scheduling method and system for coal-fired units based on measured carbon emissions. Background Technology

[0002] Currently, the dispatching of coal-fired power generating units (hereinafter referred to as coal-fired units) is mainly based on the grid load demand and the unit's economic indicators (such as coal consumption for power supply) for load allocation. However, this traditional dispatching mode has the following shortcomings: First, it lacks timely perception of the real-time carbon emission status of the units, making it difficult to adapt to the emission characteristic deviations caused by changes in operating conditions such as coal quality fluctuations and equipment aging; second, existing dispatching methods mostly optimize the load from the perspective of the overall grid, ignoring the differentiation in carbon emission characteristics between different types of units such as subcritical, supercritical, and ultra-supercritical units due to differences in technical parameters.

[0003] In recent years, most published patent documents have focused on constructing low-carbon economic dispatch models for the entire power grid. For example, Chinese patent CN117638943A proposes a pre-analysis-based unit combination sequencing method, which ranks and loads units based on their coal consumption for power generation to achieve economic goals. Regarding carbon emission monitoring, Chinese patent CN119130489A proposes a method for monitoring abnormal carbon emissions from coal-fired power plants, identifying anomalies through multiple detection methods and providing corresponding solutions. However, existing methods do not consider the grouping of unit technical parameters to compare and analyze the carbon emission characteristics of different types of units and formulate differentiated dispatch accordingly. Furthermore, existing technologies lack a unit operation feedback correction mechanism based on measured carbon emission data, making it difficult to optimize and adjust unit operating conditions in real time after dispatch instructions are executed.

[0004] Therefore, there is an urgent need for a low-carbon operation scheduling method and system for coal-fired power units based on measured carbon emissions, so that the overall operation of coal-fired power units can be more low-carbon and environmentally friendly. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to overcome the defects of the prior art and provide a method and system for low-carbon operation scheduling of coal-fired power units based on measured carbon emissions. By combining measured carbon emission data and grouping the units according to their technical parameters for differentiated scheduling, the invention achieves low-carbon operation scheduling with an operation feedback correction mechanism, thereby improving the overall low-carbon operation level of coal-fired power units.

[0006] Therefore, the present invention adopts the following technical solution.

[0007] In a first aspect, the present invention provides a low-carbon operation scheduling method for coal-fired power units based on measured carbon emissions, comprising the following steps: S1 acquires the carbon emission concentration, flue gas flow rate and unit operating parameters of each coal-fired unit in real time, and constructs a unit parameter dataset; S2. Using the aforementioned unit parameter dataset, all coal-fired unit types are classified into subcritical, supercritical, and ultra-supercritical unit groups. Based on historical measured data of unit load, carbon emission concentration, and flue gas flow, benchmark carbon emission concentration curves for each unit group under different load rates are constructed. Technical parameters of power generation coal consumption and carbon emission concentration among units are compared to determine the optimal operating range for each unit group under low-carbon operation mode. S3, based on the grid load demand, prioritize scheduling the ultra-supercritical unit group in the high load section of the preferred operating range, and schedule the subcritical unit group in the deep peak shaving section or standby state, and generate initial scheduling instructions; S4, execute the initial scheduling instruction, obtain the real-time carbon emission concentration data of the target unit after the execution of the initial scheduling instruction, and if the real-time carbon emission concentration exceeds the preset threshold on the benchmark carbon emission concentration curve of the target unit under the current load of the same type of unit group, then reverse the operating conditions of the target unit or redistribute the load among the units.

[0008] Furthermore, in step S1, the carbon emission concentration of each coal-fired unit is obtained through any of the following methods: The data were obtained in real time from online monitoring based on the continuous flue gas emission monitoring system at the chimney outlets of each coal-fired unit. The operating parameters collected by the distributed control systems of each coal-fired unit, including fuel consumption, boiler efficiency and flue gas temperature, are used to obtain the elemental analysis results of the fuel (such as the percentage content of carbon, hydrogen, oxygen, nitrogen and sulfur). Finally, the heat balance method is used to calculate the result.

[0009] Further, step S2 includes: S21 collects static design parameters for boiler efficiency, turbine heat consumption, and design coal type for subcritical, supercritical, and ultra-supercritical units. S22, using the static design parameters and fitting the measured data, generate envelope clusters for different unit types in the coordinate system of "power supply load - power supply carbon emission concentration"; S23, by comparing the envelope cluster and referring to the static design parameters, the inflection point where the carbon emission concentration of the ultra-supercritical unit increases with the decrease of load, and the carbon emission economic boundary of the subcritical unit under low load are identified.

[0010] Furthermore, the preferred operating range for each unit group under low-carbon operation mode refers to the range in which the carbon emission concentration of the unit is more than 5% lower than the average carbon emission concentration of the same type of unit group, and the load rate variation of the unit remains between 5% and 10%.

[0011] Further, step S4 includes: S41, Establish a twin model of the unit and input real-time operating parameters; S42. When the measured carbon emission concentration is higher than the preset threshold on the baseline carbon emission concentration curve, the source analysis is performed through the twin model to locate the key controllable parameters that cause the increase in carbon emission concentration, so that the carbon emission concentration falls back to the allowable range, thereby obtaining the optimal combination of operating parameters. S43, based on the obtained optimal combination of operating parameters, outputs adjustment commands to the unit coordination and control system to correct the key controllable parameters.

[0012] Furthermore, the real-time operating parameters include main steam pressure, main steam temperature, reheat steam pressure, reheat steam temperature, feedwater temperature, and flue gas oxygen content.

[0013] Furthermore, in S42, methods for identifying key controllable parameters that lead to increased carbon emission concentrations include: S421, Construct the parameter influence matrix: Establish vectors for each real-time operating parameter. X =[ x 1, x 2, …, x i …, x n With carbon emission concentration E The correlation model between them, where real-time running parameters x i This includes main steam pressure, main steam temperature, reheat steam pressure, reheat steam temperature, feedwater temperature, and flue gas oxygen content; a regression equation was obtained by fitting the data using a multiple linear regression method.

[0014] Among them, the regression coefficient β i Indicates real-time running parameters x i The degree of impact on carbon emission concentration when the amount changes by a unit, i.e., the contribution. Indicates the initial value; This represents the random error term.

[0015] S422, Sensitivity Ranking: Take the absolute value of the regression coefficients and normalize them to calculate the contribution weight of each parameter:

[0016] Sort the contribution weights from largest to smallest, and select the top k parameters with a contribution weight of 60% or more as candidate key controllable parameters; S423, Feasibility verification: Perform feasibility verification on the candidate key controllable parameters and eliminate parameters that cannot be adjusted due to the current equipment status or security boundary limitations; S424, Rolling Optimization: For key controllable parameters that have passed the verification, make step-by-step fine adjustments in order of contribution weight from high to low. After each adjustment, evaluate the response of carbon emission concentration in real time until the carbon emission concentration falls back to the allowable range. Record the parameter combination at this time as the optimal operating parameter combination.

[0017] Furthermore, the reverse adjustment of the target unit's operating conditions includes: when the initial scheduling instruction is to increase the load of the target unit, the reverse adjustment is to reduce its load; when the initial scheduling instruction is to increase a certain operating parameter, the reverse adjustment is to decrease that parameter; the redistribution of load among units includes: transferring part of the load corresponding to the carbon emissions of the target unit exceeding a preset threshold to other units whose carbon emission concentration in the current load segment is lower than the benchmark carbon emission concentration curve.

[0018] Secondly, the present invention provides a low-carbon operation scheduling system for coal-fired power units based on measured carbon emissions, used to implement the aforementioned low-carbon operation scheduling method for coal-fired power units based on measured carbon emissions, comprising: Carbon emission monitoring network: set up at the chimney outlets of each coal-fired unit to obtain the measured flue gas flow rate and corresponding carbon emission concentration of the unit; Unit type characteristic library: Stores the thermodynamic characteristics of subcritical, supercritical, and ultra-supercritical units and their corresponding historical carbon emission data; Low-carbon scheduling strategy machine: used to generate optimized scheduling instructions with the goal of minimizing carbon emissions across the entire network, based on measured carbon emission data and the unit type feature library; Unit feedback control module: used to connect to the unit's distributed control system and perform closed-loop control of the unit according to the optimized scheduling instructions of the low-carbon scheduling strategy machine.

[0019] Furthermore, the low-carbon scheduling strategy machine also includes: Priority sorting unit: used to sort units of the same type from low to high based on real-time carbon emission concentration, and prioritize the scheduling of units with low carbon emission concentration. Cross-type matching unit: used to calculate the optimal load allocation ratio among different types of unit groups based on the total load demand and the preferred operating range.

[0020] The beneficial effects of this invention are as follows: This invention compares and groups subcritical, supercritical, and ultra-supercritical units based on differences in their technical parameters, constructs benchmark carbon emission concentration curves for each type of unit based on measured data, forms differentiated optimal operating ranges, and achieves refined scheduling; at the same time, by establishing a closed-loop feedback correction mechanism based on measured carbon emission data, it locates key controllable parameters and performs rolling optimization adjustment according to their contribution, enabling the scheduling system to have self-optimization capabilities, continuously correcting operating condition deviations during unit operation, and dynamically maintaining low-carbon operation. Attached Figure Description

[0021] To more clearly illustrate the embodiments of the present invention, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0022] Figure 1 This is a schematic flowchart of a low-carbon operation scheduling method for coal-fired power units based on measured carbon emissions, according to the present invention. Figure 2 This is a schematic diagram of a low-carbon operation scheduling system for coal-fired power units based on measured carbon emissions, according to the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the embodiments described herein are merely illustrative and not intended to limit the invention.

[0024] Conversely, this invention encompasses any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of the invention as defined in the claims. Furthermore, to provide a better understanding of the invention, certain specific details are described in detail below. However, those skilled in the art will fully understand the invention even without these detailed descriptions.

[0025] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of the invention.

[0026] Example 1 This embodiment presents a low-carbon operation scheduling method for coal-fired power units based on measured carbon emissions. Figure 1 As shown, the steps are as follows: S1 acquires the carbon emission concentration, flue gas flow rate and unit operating parameters of each coal-fired unit in real time, and constructs a unit parameter dataset, which includes unit technical level, rated capacity, unit load, coal feed rate, flue gas temperature, etc. S2. Using the aforementioned unit parameter dataset, all coal-fired unit types are classified into subcritical, supercritical, and ultra-supercritical unit groups. Based on historical measured data such as unit load, carbon emission concentration, and flue gas flow, benchmark carbon emission concentration curves for each unit group under different load rates are constructed. Technical parameters such as power generation coal consumption and carbon emission concentration among units are compared to determine the optimal operating range for each unit group under low-carbon operation mode. S3, based on the grid load demand, prioritize scheduling the ultra-supercritical unit group in the high load section of the preferred operating range, and schedule the subcritical unit group in the deep peak shaving section or standby state, and generate initial scheduling instructions; S4, execute the initial scheduling instruction, obtain the real-time carbon emission concentration data of the target unit after the execution of the initial scheduling instruction, and if the real-time carbon emission concentration exceeds the preset threshold on the benchmark carbon emission concentration curve of the target unit under the current load of the same type of unit group, then reverse the operating conditions of the target unit or redistribute the load among the units.

[0027] Specifically, in step S1, the carbon emission concentration of each coal-fired unit is obtained through any of the following methods: The data was obtained in real time from online monitoring based on the Continuous Emission Monitoring System (CEMS) at the chimney outlets of each coal-fired unit. Operating parameters, including fuel consumption, boiler efficiency, and flue gas temperature, are collected using the distributed control system (DCS) of each coal-fired unit. The elemental analysis results of the fuel (such as the percentage content of carbon, hydrogen, oxygen, nitrogen, and sulfur) are obtained. Finally, the heat balance method is used to calculate the result (the calculation method is described in Chinese patent document CN120746004A).

[0028] Specifically, step S2 includes: S21 collects static design parameters for boiler efficiency, turbine heat consumption, and design coal type for subcritical, supercritical, and ultra-supercritical units. S22, using the static design parameters and fitting the measured data, generate envelope clusters for different unit types in the coordinate system of "power supply load - power supply carbon emission concentration"; S23, by comparing the envelope cluster and referring to the static design parameters, the inflection point where the carbon emission concentration of the ultra-supercritical unit increases with the decrease of load, and the carbon emission economic boundary of the subcritical unit under low load are identified.

[0029] In step S2, the preferred operating range of each unit group in the low-carbon operation mode refers to the range in which the carbon emission concentration of the unit is more than 5% lower than the average carbon emission concentration of the same type of unit group, and the load rate variation of the unit is maintained between 5% and 10%, so as to ensure that the unit can achieve continuous, stable, low-carbon and efficient operation within this range.

[0030] Specifically, step S4 includes: S41, Establish a twin model of the unit and input real-time operating parameters, including main steam pressure, main steam temperature, reheat steam pressure, reheat steam temperature, feedwater temperature and flue gas oxygen content. S42. When the measured carbon emission concentration is higher than the preset threshold on the baseline carbon emission concentration curve, the source analysis is performed through the twin model to locate the key controllable parameters that cause the increase in carbon emission concentration, so that the carbon emission concentration falls back to the allowable range, thereby obtaining the optimal combination of operating parameters. S43, based on the obtained optimal combination of operating parameters, outputs adjustment commands to the unit coordination and control system to correct the key controllable parameters.

[0031] Specifically, in S42, the methods for identifying key controllable parameters that lead to increased carbon emission concentrations include: S421, Construct the parameter influence matrix: Establish vectors for each real-time operating parameter. X =[ x 1, x 2, …, x i …, x n With carbon emission concentration E The correlation model between them, where real-time running parameters x i This includes main steam pressure, main steam temperature, reheat steam pressure, reheat steam temperature, feedwater temperature, and flue gas oxygen content; a regression equation was obtained by fitting the data using a multiple linear regression method.

[0032] Among them, the regression coefficient β i Indicates real-time running parameters x i The degree of impact on carbon emission concentration when the amount changes by a unit, i.e., the contribution. Indicates the initial value; This represents the random error term.

[0033] S422, Sensitivity Ranking: Take the absolute value of the regression coefficients and normalize them to calculate the contribution weight of each parameter:

[0034] Sort the contribution weights from largest to smallest, and select the top k parameters with a contribution weight of 60% or more as candidate key controllable parameters; S423, Feasibility verification: Perform feasibility verification on the candidate key controllable parameters and eliminate parameters that cannot be adjusted due to the current equipment status or security boundary limitations; S424, Rolling Optimization: For key controllable parameters that have passed the verification, make step-by-step fine adjustments in order of contribution weight from high to low. After each adjustment, evaluate the response of carbon emission concentration in real time until the carbon emission concentration falls back to the allowable range. Record the parameter combination at this time as the optimal operating parameter combination.

[0035] By using a closed-loop feedback correction mechanism based on measured carbon emission data, key controllable parameters are located and adjusted in a rolling optimization manner according to their contribution, enabling the dispatching system to have self-optimization capabilities. This allows the system to continuously correct operating condition deviations during unit operation and achieve dynamic maintenance of low-carbon operation.

[0036] Example 2 This embodiment is a low-carbon operation scheduling system for coal-fired power units based on measured carbon emissions, used to implement the low-carbon operation scheduling method for coal-fired power units based on measured carbon emissions described in Embodiment 1. It consists of a carbon emission monitoring network, a unit type feature database, a low-carbon scheduling strategy machine, and a unit feedback control module. Figure 2 As shown.

[0037] The carbon emission monitoring network is set up at the chimney outlets of each coal-fired unit to obtain the measured flue gas flow rate and corresponding carbon emission concentration of the unit.

[0038] The aforementioned unit type feature database stores the thermal characteristics of subcritical, supercritical, and ultra-supercritical units and their corresponding historical carbon emission data.

[0039] The low-carbon scheduling strategy machine is used to generate optimized scheduling instructions with the goal of minimizing the carbon emissions of the entire network, based on measured carbon emission data and the unit type feature library. The aforementioned unit feedback control module is used to connect to the unit's distributed control system and perform closed-loop control of the unit according to the optimized scheduling instructions of the low-carbon scheduling strategy machine.

[0040] The low-carbon scheduling strategy machine also includes: Priority sorting unit: used to sort units of the same type from low to high based on real-time carbon emission concentration, and prioritize the scheduling of units with low carbon emission concentration. Cross-type matching unit: used to calculate the optimal load allocation ratio among different types of unit groups based on the total load demand and the preferred operating range.

[0041] It should be noted that each unit in the aforementioned low-carbon operation scheduling system for coal-fired power units based on measured carbon emissions can be implemented entirely or partially through software, hardware, or a combination thereof. These units can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each unit.

[0042] For specific limitations regarding a low-carbon operation scheduling system for coal-fired power units based on measured carbon emissions, please refer to the limitations of a low-carbon operation scheduling method for coal-fired power units based on measured carbon emissions (i.e., Example 1) above. The two have the same function and role, and will not be repeated here.

[0043] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A low-carbon operation scheduling method for coal-fired power units based on measured carbon emissions, characterized in that, Including the following steps: S1 acquires the carbon emission concentration, flue gas flow rate and unit operating parameters of each coal-fired unit in real time, and constructs a unit parameter dataset; S2. Using the aforementioned unit parameter dataset, all coal-fired unit types are classified into subcritical, supercritical, and ultra-supercritical unit groups. Based on historical measured data of unit load, carbon emission concentration, and flue gas flow, benchmark carbon emission concentration curves for each unit group under different load rates are constructed. Technical parameters of power generation coal consumption and carbon emission concentration among units are compared to determine the optimal operating range for each unit group under low-carbon operation mode. S3, based on the grid load demand, prioritize scheduling the ultra-supercritical unit group in the high load section of the preferred operating range, and schedule the subcritical unit group in the deep peak shaving section or standby state, and generate initial scheduling instructions; S4, execute the initial scheduling instruction, obtain the real-time carbon emission concentration data of the target unit after the execution of the initial scheduling instruction, and if the real-time carbon emission concentration exceeds the preset threshold on the benchmark carbon emission concentration curve of the target unit under the current load of the same type of unit group, then reverse the operating conditions of the target unit or redistribute the load among the units.

2. The method for low-carbon operation scheduling of coal-fired power units based on measured carbon emissions according to claim 1, characterized in that, In step S1, the carbon emission concentration of each coal-fired unit is obtained through any of the following methods: The data were obtained in real time from online monitoring based on the continuous flue gas emission monitoring system at the chimney outlets of each coal-fired unit. Operating parameters, including fuel consumption, boiler efficiency, and flue gas temperature, are collected using the distributed control systems of each coal-fired unit. Elemental analysis results of the fuel are obtained, and the final calculation is performed using the heat balance method.

3. The method for low-carbon operation scheduling of coal-fired power units based on measured carbon emissions according to claim 1, characterized in that, Step S2 includes: S21 collects static design parameters for boiler efficiency, turbine heat consumption, and design coal type for subcritical, supercritical, and ultra-supercritical units. S22, using the static design parameters and fitting the measured data, generate envelope clusters for different unit types in the "power supply load - power supply carbon emission concentration" coordinate system; S23, by comparing the envelope cluster and referring to the static design parameters, the inflection point where the carbon emission concentration of the ultra-supercritical unit increases with the decrease of load, and the carbon emission economic boundary of the subcritical unit under low load are identified.

4. The method for low-carbon operation scheduling of coal-fired power units based on measured carbon emissions according to claim 1, characterized in that, The preferred operating range for each unit group under low-carbon operation mode refers to the range in which the carbon emission concentration of the unit is more than 5% lower than the average carbon emission concentration of the same type of unit group, and the load rate variation of the unit remains between 5% and 10%.

5. The method for low-carbon operation scheduling of coal-fired power units based on measured carbon emissions according to claim 1, characterized in that, Step S4 specifically includes: S41, Establish a twin model of the unit and input real-time operating parameters; S42. When the measured carbon emission concentration is higher than the preset threshold on the baseline carbon emission concentration curve, the source analysis is performed through the twin model to locate the key controllable parameters that cause the increase in carbon emission concentration, so that the carbon emission concentration falls back to the allowable range, thereby obtaining the optimal combination of operating parameters. S43, based on the obtained optimal combination of operating parameters, outputs adjustment commands to the unit coordination and control system to correct the key controllable parameters.

6. The method for low-carbon operation scheduling of coal-fired power units based on measured carbon emissions according to claim 5, characterized in that, The real-time operating parameters include main steam pressure, main steam temperature, reheat steam pressure, reheat steam temperature, feedwater temperature, and flue gas oxygen content.

7. A method for low-carbon operation scheduling of coal-fired power units based on measured carbon emissions according to claim 5, characterized in that, In S42, methods for identifying key controllable parameters that lead to increased carbon emission concentrations include: S421, Construct the parameter influence matrix: Establish vectors for each real-time operating parameter. X =[ x 1, x 2, …, x i …, x n With carbon emission concentration E The correlation model between them, where real-time running parameters x i This includes main steam pressure, main steam temperature, reheat steam pressure, reheat steam temperature, feedwater temperature, and flue gas oxygen content; a regression equation was obtained by fitting the data using a multiple linear regression method. Among them, the regression coefficient β i Indicates real-time running parameters x i The degree of impact on carbon emission concentration when the amount changes by a unit, i.e., the contribution. Indicates the initial value; Represents the random error term; S422, Sensitivity Ranking: Take the absolute value of the regression coefficients and normalize them to calculate the contribution weight of each parameter: Sort the contribution weights from largest to smallest, and select the top k parameters with a contribution weight of 60% or more as candidate key controllable parameters; S423, Feasibility verification: Perform feasibility verification on the candidate key controllable parameters and eliminate parameters that cannot be adjusted due to the current equipment status or security boundary limitations; S424, Rolling Optimization: For key controllable parameters that have passed the verification, make step-by-step fine adjustments in order of contribution weight from high to low. After each adjustment, evaluate the response of carbon emission concentration in real time until the carbon emission concentration falls back to the allowable range. Record the parameter combination at this time as the optimal operating parameter combination.

8. The method for low-carbon operation scheduling of coal-fired power units based on measured carbon emissions according to claim 1, characterized in that, In step S4, the reverse adjustment of the operating conditions of the target unit includes: when the initial scheduling instruction is to increase the load of the target unit, the reverse adjustment is to reduce its load; when the initial scheduling instruction is to increase a certain operating parameter, the reverse adjustment is to decrease that parameter; the redistribution of load between units includes: transferring part of the load corresponding to the carbon emissions of the target unit that exceed the preset threshold to other units whose carbon emission concentration in the current load segment is lower than the benchmark carbon emission concentration curve.

9. A low-carbon operation scheduling system for coal-fired power units based on measured carbon emissions, used to implement the method described in any one of claims 1-8, characterized in that, include: Carbon emission monitoring network: set up at the chimney outlets of each coal-fired unit to obtain the measured flue gas flow rate and corresponding carbon emission concentration of the unit; Unit type characteristic database: Stores the thermodynamic characteristics of subcritical, supercritical, and ultra-supercritical units and their corresponding historical carbon emission data; Low-carbon scheduling strategy machine: used to generate optimized scheduling instructions with the goal of minimizing carbon emissions across the entire network, based on measured carbon emission data and the unit type feature library; Unit feedback control module: used to connect to the unit's distributed control system and perform closed-loop control of the unit according to the optimized scheduling instructions of the low-carbon scheduling strategy machine.

10. The low-carbon operation and dispatching system for coal-fired power units according to claim 9, characterized in that, The low-carbon scheduling strategy machine also includes: Priority sorting unit: used to sort units of the same type from low to high based on real-time carbon emission concentration, and prioritize the scheduling of units with low carbon emission concentration. Cross-type matching unit: used to calculate the optimal load allocation ratio among different types of unit groups based on the total load demand and the preferred operating range.

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