A multi-mode coordinated control method and device for a carbon capture system of a thermal power unit

Abstract

The application discloses a kind of multi-mode coordinated control method and device of carbon capture system of thermal power generating unit, belong to thermal power generation technical field, comprising: step one: multi-time scale state perception and prediction;Real-time data acquisition, the key state variable of k time is collected, and based on key state variable, establish unit and carbon capture system dynamic mathematical model, the external condition and internal state variable in future a limited time domain are carried out rolling prediction, generate prediction sequence;Step two: operation mode decision based on rolling optimization;Based on prediction sequence, construct a mixed integer rolling optimization problem with future a sampling period as optimization time domain, it also includes objective function, decision variable, constraint condition and online solution and decision;Step three: multivariable coordinated control;According to the decision result of step two, generate specific control instruction and execute.The application solves the dynamic optimization scheduling problem under multi-time scale, multi-objective and multi-constraint.

Description

Technical Field

[0001] This invention belongs to the field of thermal power generation technology, specifically relating to a multi-mode coordinated control method and device for a carbon capture system of a thermal power unit. Background Technology

[0002] With the development of clean energy technologies, installing carbon capture, utilization, and storage (CCUS) systems in existing thermal power plants is a key pathway to reduce carbon emissions. Among these, post-combustion chemical absorption (CCI) methods (such as the amine process) are currently the most mature and commercially viable technologies. A typical process involves flue gas entering an absorption tower and coming into countercurrent contact with an amine solvent. The solvent absorbs CO2 to form a rich solution, which is then regenerated in a desorption tower by heating, releasing high-purity CO2. The regenerated lean solution is returned to the absorption tower for recycling. This regeneration process consumes a large amount of steam, typically extracted from a steam turbine, resulting in significant energy consumption.

[0003] This high energy consumption characteristic severely impairs the peak-shaving capacity of power plants after they are connected to carbon capture systems. When grid load demands decrease, the operating load of the carbon capture system must be significantly reduced to ensure the stable operation of the main unit, leading to increased CO2 emissions and failing to achieve the environmental goal of continuous emission reduction. Conversely, when the load increases, existing carbon capture systems cannot fully utilize the surplus energy of the main unit to improve capture performance. At its root, the operation of integrated thermal power-carbon capture systems generally adopts a "follow-the-leader" strategy based on classical control theory (such as PID control), meaning that the operating load of the carbon capture system (mainly manifested as solvent regeneration energy consumption) passively adjusts proportionally to changes in the main unit's power generation load. This system is a rigidly coupled dynamic system, and existing technologies face the following insurmountable bottlenecks, which constitute the core scientific problem that this invention aims to solve: 1. The mismatch between dynamic characteristics and spatiotemporal scales: The response of thermal power units to grid peak-shaving commands is a rapid dynamic process on the order of seconds to minutes; while in carbon capture systems, solvent absorption / desorption involves complex mass transfer, heat transfer, and chemical reactions, and is a typical slow process on the order of minutes to hours. This inherent mismatch in multi-timescale characteristics means that traditional single-timescale control methods cannot simultaneously guarantee the rapid response requirements of the grid and the stable operation of the carbon capture process. The system often operates in oscillation or transient processes, resulting in poor control quality.

[0004] 2. Lack of endogenous response to external market signals: Price signals in the electricity spot market and carbon trading market are time-varying and highly volatile. Current technology lacks a mechanism to transform these exogenous economic signals into endogenous system operational decisions, preventing the system from actively participating in market arbitrage and hindering the exploration of its operational economic potential.

[0005] 3. The contradiction between peak-shaving capacity and carbon emission reduction targets: When grid load demand decreases, the steam output of the generating units decreases. To maintain the stability of the main unit, the carbon capture system needs to reduce its operating load, resulting in a decrease in CO2 capture rate. This makes it impossible to guarantee continuous emission reduction effects and severely restricts the peak-shaving capacity of the generating units as a flexible resource. Therefore, there is an urgent need in this field for an innovative control method. Summary of the Invention

[0006] This invention provides a multi-mode coordinated control method and device for a carbon capture system of a thermal power unit. The purpose is to break the instantaneous coupling constraints of the current control system and design a corresponding optimized control architecture to solve the dynamic optimization scheduling problem under multiple time scales, multiple objectives and multiple constraints.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: A multi-mode coordinated control method for a carbon capture system in a thermal power unit includes: Step 1: Multi-timescale state perception and prediction; Real-time data acquisition involves collecting key state variables at time k, and based on these key state variables, establishing a dynamic mathematical model of the unit and carbon capture system for a future finite time domain. The system performs rolling predictions based on both external conditions and internal state variables to generate a prediction sequence. Step 2: Operational mode decision based on rolling optimization; Based on the predicted sequence, a mixed integer rolling optimization problem is constructed with the next sampling period as the optimization time domain. It also includes the objective function, decision variables, constraints, and online solution and decision-making. Step 3: Multivariate Coordination Control; Based on the decision results from step two, specific control instructions are generated and executed.

[0008] A further improvement of this invention is that, in step one, key state variables at time k are collected, including: Generating capacity of the unit and power grid dispatch instructions , as well as energy market signals and the internal status of carbon capture in thermal power units.

[0009] A further improvement of this invention is that, in step two, the objective function is to minimize the total operating cost within the prediction time domain. J For multi-objective weighted sums: The cost of lost electricity revenue is related to the power generation capacity squeezed out by the energy consumption of carbon capture systems during periods of high electricity prices. Carbon capture costs and potential penalties for failing to meet emission reduction targets; The stability penalty term is designed to minimize drastic changes in control variables and ensure system stability.

[0010] A further improvement of this invention is that, in step two, the decision variables include: Continuous variables: including rich liquid split ratio and reboiler heat load process control variables; Discrete variables: Introduce the operating mode variable Mode(k) {0,1,2} is defined as a key decision variable as follows: Mode(k)=1: "Energy Storage Mode"; Under this mode, the optimization algorithm tends to introduce the rich liquid generated by the absorption tower into the storage tank to reduce the immediate regeneration energy consumption; Mode(k)=2: "Carbon capture enhancement mode"; In this mode, the algorithm tends to draw rich solution from the storage tank to increase the regeneration load; Mode(k)=0: "Steady-state optimization operation mode"; Under this mode, the system operates according to the static optimal operating point based on nonlinear programming.

[0011] A further improvement of the present invention is that, in step two, the constraints include: system dynamic model constraints, equipment physical limit constraints, and logical constraints between modal and continuous variables.

[0012] A further improvement of this invention is that, in step two, the online solution and decision-making process includes: in each sampling period k, using a mathematical programming solver to solve the optimization problem online to obtain the optimal solution. Based on the optimal sequence of corresponding continuous control variables, determine the optimal operating mode that the system should be in at the current moment and the setpoints of each actuator.

[0013] A further improvement of this invention is that, in step three, based on the decision result of step two, specific control instructions are generated and executed, including: like The control system will adjust the opening of the rich liquid diversion valve to allow the rich liquid to flow preferentially to the storage tank, and correspondingly reduce the opening of the reboiler steam valve to achieve energy storage. like The control system will start the rich liquor transfer pump and adjust its flow rate, while increasing the opening of the reboiler steam valve to handle the additional rich liquor and enhance carbon capture. like The control system will drive each variable to operate near the optimal setpoint obtained through offline or online steady-state optimization calculations.

[0014] A multi-mode coordinated control device for a carbon capture system in a thermal power unit includes: Multi-timescale state perception and prediction unit: Real-time data acquisition, collecting key state variables at time k, and based on these key state variables, establishing a dynamic mathematical model of the unit and carbon capture system for a finite future time domain. The system performs rolling predictions based on both external conditions and internal state variables to generate a prediction sequence. The rolling optimization-based operational modal decision unit: Based on the prediction sequence, a mixed integer rolling optimization problem with the future sampling period as the optimization time domain is constructed, which also includes the objective function, decision variables, constraints, and online solution and decision-making; Multivariable Coordination and Control Unit: Generates and executes specific control commands based on the decision results of the rolling optimization-based operating mode decision unit.

[0015] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the multi-mode coordinated control method for a carbon capture system of a thermal power unit.

[0016] Compared with the prior art, the present invention has at least the following beneficial technical effects: This invention provides a multi-mode coordinated control method and device for a carbon capture system in a thermal power unit. By adding a solvent buffer (rich liquid storage tank and related pipeline valves) and optimizing the operating modes of the carbon capture system based on external market signals (such as electricity prices), it achieves the basic principles of energy time-shifting and decoupled operation. The core of this multi-mode optimization control method lies in dividing the operating modes into "energy storage mode," "carbon capture enhancement mode," and "steady-state optimization operation mode" as discrete decision variables, and solving them together with continuous control variables within a rolling optimization framework.

[0017] The physical system for implementing the above method according to the present invention is, in particular, a complete system architecture comprising the solvent buffer subsystem and an intelligent control unit configured to execute the multimodal optimization algorithm. Attached Figure Description

[0018] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a schematic diagram of the unit and carbon capture system model; Figure 3 This is a structural block diagram of the device of the present invention. Detailed Implementation

[0020] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the spirit or scope of the invention. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0021] In the description of this invention, it should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0022] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0023] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0024] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0025] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0026] Example 1 like Figure 1 As shown, the control algorithm proposed in this invention performs one iteration in each sampling period, and its core process is described below. This process aims to achieve intelligent switching and coordinated control of the carbon capture system between different operating modes through rolling optimization.

[0027] Step 1: State Estimation and Multi-scale Prediction In this step, the control system performs data acquisition and state prediction to provide an information basis for optimization decisions. Real-time data acquisition: The system acquires key state variables at time k, including but not limited to: Generating capacity of the unit and power grid dispatch instructions . Energy market signals, such as real-time electricity prices With carbon quota prices . Internal conditions of carbon capture systems in thermal power units, such as the level of the rich liquid storage tank. Reboiler temperature and cumulative carbon capture . Multi-timescale prediction: Based on the established dynamic mathematical model of the unit and carbon capture system, predictions are made for a finite time domain in the future. (For example, for the next 2 to 4 hours) external conditions (such as electricity price trends and load command changes) and internal state variables are rolled out to generate a forecast sequence. Step 2: Rolling-horizon Optimization for Operational Mode Decision This step is the core of the algorithm, which determines the optimal running strategy by solving an optimization problem. Problem Formulation: Construct a mixed integer rolling optimization problem with the future sampling period as the optimization time domain. Objective function: The objective is to minimize the total operating cost within the prediction time domain. J This function is a multi-objective weighted sum containing the following terms: The cost of lost electricity revenue is related to the power generation capacity squeezed out by the energy consumption of carbon capture systems during periods of high electricity prices. Carbon capture costs and potential penalties for failing to meet emission reduction targets. The stability penalty term is designed to minimize drastic changes in control variables and ensure system stability. Decision variables: including continuous variables and discrete variables Continuous variables: These include process control variables such as rich liquid split ratio and reboiler heat load. Discrete variables: Introduce the operating mode variable Mode(k) {0,1,2} is defined as a key decision variable as follows: Mode(k)=1: "Energy Storage Mode". In this mode, the optimization algorithm tends to introduce the rich liquid generated by the absorption tower into the storage tank to reduce the energy consumption of immediate regeneration. Mode(k)=2: "Carbon capture enhancement mode". In this mode, the algorithm tends to extract rich solution from the storage tank to increase the regeneration load. Mode(k)=0: "Steady-state optimal operation mode". In this mode, the system operates according to the static optimal operating point based on nonlinear programming. Constraints include system dynamic model constraints, equipment physical limit constraints (such as valve opening degree, tank capacity, etc.), and logical constraints between modal and continuous variables. Online Solving and Decision Making: At each sampling period k, the above optimization problem is solved online using a mathematical programming solver (such as CPLEX or Gurobi). Based on the obtained optimal solution... Based on the optimal sequence of corresponding continuous control variables, determine the optimal operating mode that the system should be in at the current moment and the setpoints of each actuator. Step 3: Multivariable Coordinated Control Based on the decision results from step two, specific control instructions are generated and executed.

[0028] like The control system will adjust the opening of the rich liquid diversion valve to allow the rich liquid to flow preferentially to the storage tank, and correspondingly reduce the opening of the reboiler steam valve to achieve energy storage. like The control system will start the rich liquor transfer pump and adjust its flow rate, while increasing the opening of the reboiler steam valve to handle the additional rich liquor and enhance carbon capture. like The control system will drive each variable to operate near the optimal setpoint obtained through offline or online steady-state optimization calculations. After the instruction is issued, the algorithm enters a waiting state until the next sampling period arrives, and then repeats the above steps to form a closed-loop model predictive control. Example 2 The core of this invention is to construct a "physical potential buffer layer" and an "intelligent decision optimization layer". By introducing the new degree of freedom of solvent chemical potential energy storage, the instantaneous coupling relationship is decoupled in the time dimension.

[0029] 2.1 Specific Innovations in the Physical System Architecture: Based on the traditional amine-based carbon capture system, the following hardware is added to form a chemical potential energy buffer layer: Liquid-rich chemical potential energy storage subsystem: Rich liquid storage tank: Used to store excess rich liquid. Made of SS316L steel, with insulation, level gauge and temperature sensor.

[0030] Rich liquid three-way diverter valve: Installed on the pipeline at the outlet of the rich phase pump, it is a pneumatic regulating valve controlled by an intelligent positioner. Its control signal determines the proportion of rich liquid flowing to the regeneration tower and the proportion flowing to the storage tank.

[0031] Rich liquid booster pump: Installed at the outlet of the rich liquid storage tank, it is a variable frequency controlled centrifugal pump used to deliver the stored rich liquid to the inlet of the regeneration tower at a controllable flow rate when needed.

[0032] Integrated Measurement and Control System: Sensor Networks: A new electromagnetic flowmeter has been added to measure the flow rate of rich solutions.

[0033] Advanced Process Controller (APC): Employs an industrial-grade server to run the multi-time-scale optimization algorithm described in this invention. It interacts with the underlying DCS via the OPC UA protocol, outputting control commands to the frequency converter, the rich liquid three-way diverter valve, and the reboiler steam regulating valve.

[0034] 2.2 The control algorithm described above performs the following steps in each sampling period k: Step 1: Multi-timescale state perception and prediction Real-time data collection includes generator power output, market electricity price, carbon price, and storage tank liquid level.

[0035] like Figure 2 As shown, based on the unit and carbon capture system model, the external conditions (such as electricity price trends and load commands) and internal status are predicted for the next few hours. Step 2: Operational Mode Decision Based on Rolling Optimization The runtime modal optimization decision constructs a rolling optimization problem with the optimization time domain being several future sampling periods (e.g., the next 2-4 hours).

[0036] Optimization objective: Minimize the total operating cost within the optimization time domain, primarily focusing on [power generation revenue loss] + [carbon capture cost] + [operational stability penalty]. Among these, power generation revenue loss is related to the reduced power generation caused by the energy consumption of the carbon capture system during periods of high electricity prices.

[0037] Decision variables include continuous variables (such as rich liquid split ratio and reboiler heat load) and a key discrete decision variable—the operating mode Mode(k).

[0038] Mode(k) = 1 represents the "energy storage mode": In this mode, the algorithm tends to import part or all of the rich liquid generated by the absorption tower into the rich liquid storage tank for storage, thereby reducing the energy consumption of immediate regeneration.

[0039] Mode(k) = 2 represents the “carbon capture enhancement mode”: In this mode, the algorithm tends to extract rich liquid from the storage tank, combine it with the current rich liquid, and send it into the desorption tower to increase the regeneration load.

[0040] Mode(k) = 0 represents the "steady-state optimal operating mode": in this mode, the system operates according to the static optimal operating point obtained based on nonlinear programming. Solution and Decision: Solve the optimization problem online, determine the current mode of the system based on the optimal solution, and calculate the corresponding continuous control setpoint.

[0041] Step 3: Multivariate Coordination Control Based on the Mode(k) determined in step two and its corresponding continuous control variables, the coordinated control execution generates specific execution instructions: If Mode(k) = 1, then control the opening of the rich liquid diversion valve to allow the rich liquid to flow to the storage tank, and correspondingly reduce the opening of the reboiler steam valve.

[0042] If Mode(k) = 2, the rich liquid transfer pump is started and its flow rate is controlled, while the opening of the reboiler steam valve is increased to handle the additional rich liquid.

[0043] If Mode(k) = 0, the system will operate according to the design conditions.

[0044] Example 3 To verify the feasibility and superiority of the present invention, "A Multi-mode Coordinated Control Method and Device for Carbon Capture in Thermal Power Units", a 600MW subcritical coal-fired power unit equipped with the present control method and device and its supporting amine carbon capture system are used as an example for simulation and actual operation data analysis.

[0045] 1. System and Operating Conditions Unit basic parameters: rated power generation 600MW, carbon capture system designed annual operating hours 7500 hours, baseline CO2 capture rate 90%.

[0046] Key new hardware additions: A rich liquid storage tank with an effective volume of 500 cubic meters is added, equipped with a precision three-way diverter valve and a variable frequency booster pump.

[0047] Market Condition Simulation: A typical 24-hour operating cycle is set. Peak electricity demand occurs between 12:00 PM and 2:00 PM, with electricity prices reaching 0.8 yuan / kWh; off-peak demand occurs between 12:00 AM and 6:00 AM, with prices dropping to 0.3 yuan / kWh. The power grid requires generating units to increase output to 550MW during the midday peak period and to decrease to 450MW at night.

[0048] Comparison scheme: A traditional "load-following" PID control system was used as the control group. In the control group, the energy consumption of the reboiler in the carbon capture system was always in a fixed proportional relationship with the power generation of the main generator.

[0049] 2. Operation process and data of this invention (experimental group) 00:00-08:00 (Low price, low load period): Operating mode: The system decision is "energy storage mode" (Mode=1).

[0050] Specific actions: The control system adjusts the diversion valve to divert approximately 70% of the rich liquid into the storage tank, reducing the reboiler heat load to 60% of the design value.

[0051] Data results: During this period, the unit's power generation was 450MW, and the carbon capture rate decreased to approximately 75%, but the tank level rose from 30% to 85%, storing rich liquid chemical potential energy equivalent to 4 hours of designed processing capacity. Due to low electricity prices and reduced capture energy consumption, the unit's net on-grid electricity increased, resulting in lower operating costs.

[0052] 10:00-12:00 (Period of rising electricity prices and increased load): Operating mode: The system decision is "steady-state optimization operating mode" (Mode=0).

[0053] Specific actions: The system operates under optimal steady-state conditions to prepare for the upcoming enhanced capture.

[0054] 12:00-14:00 (High price, high load period): Operating mode: The system decision is "carbon capture enhancement mode" (Mode=2).

[0055] Specific actions: Start the booster pump at the tank outlet to pump the stored rich liquid into the regeneration tower at the maximum design flow rate, and at the same time increase the heat load of the reboiler to 120% of the design value.

[0056] Data Results: The unit's power generation capacity increased to 550MW in response to grid commands. Despite a significant increase in reboiler energy consumption, the carbon capture rate increased to 105% (exceeding the design value) due to the utilization of previously stored rich liquid, achieving excess capture. During periods of high electricity prices, the unit reduced the power generation capacity squeezed out by carbon capture, resulting in a significant increase in power generation revenue. The tank liquid level decreased from 85% to 20%.

[0057] After 14:00: The system dynamically switches between three modes based on real-time electricity prices and load forecasts, ensuring smooth operation.

[0058] 3. Comparative Analysis and Superiority Verification Key performance indicators were compared over a 24-hour period:

[0059] 4. Conclusion This embodiment demonstrates, through specific data and operational scenarios, that the method and apparatus proposed in this invention are indeed feasible. Compared to traditional control methods, this invention, by introducing an "energy time-shifting" strategy and "operational mode" optimization decision-making, can effectively decouple the contradiction between the rapid peak-shaving demand of thermal power units and the slow process of the carbon capture system. It integrates external price signals into the control decision-making process, thereby significantly improving the economic efficiency of power plant operation and its flexibility in supporting the power grid while ensuring or even improving carbon capture performance. This intuitively and powerfully proves the superiority of this invention.

[0060] Example 4 like Figure 3 As shown, the present invention provides a multi-mode coordinated control device for a carbon capture system of a thermal power unit, comprising: Multi-timescale state perception and prediction unit: Real-time data acquisition, collecting key state variables at time k, and based on these key state variables, establishing a dynamic mathematical model of the unit and carbon capture system for a finite future time domain. The system performs rolling predictions based on both external conditions and internal state variables to generate a prediction sequence. The rolling optimization-based operational modal decision unit: Based on the prediction sequence, a mixed integer rolling optimization problem with the future sampling period as the optimization time domain is constructed, which also includes the objective function, decision variables, constraints, and online solution and decision-making; Multivariable Coordination and Control Unit: Generates and executes specific control commands based on the decision results of the rolling optimization-based operating mode decision unit.

[0061] Example 5 The present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of a multi-mode coordinated control method for a carbon capture system of a thermal power unit.

[0062] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0063] This application is described with reference to flowchart illustrations and / or block diagrams of methods, systems, and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A system that specifies functions in one or more boxes.

[0064] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0065] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0066] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the scope of the invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0067] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can be appropriately combined to form other embodiments that can be understood by those skilled in the art. The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A multi-mode coordinated control method for a carbon capture system in a thermal power unit, characterized in that, include: Step 1: Multi-timescale state perception and prediction; Real-time data acquisition involves collecting key state variables at time k, and based on these key state variables, establishing a dynamic mathematical model of the unit and carbon capture system for a future finite time domain. The system performs rolling predictions based on both external conditions and internal state variables to generate a prediction sequence. Step 2: Operational mode decision based on rolling optimization; Based on the predicted sequence, a mixed integer rolling optimization problem is constructed with the next sampling period as the optimization time domain. It also includes the objective function, decision variables, constraints, and online solution and decision-making. Step 3: Multivariate Coordination Control; Based on the decision results from step two, specific control instructions are generated and executed.

2. The multi-mode coordinated control method for a carbon capture system of a thermal power unit according to claim 1, characterized in that, In step one, key state variables at time k are collected, including: Generating capacity of the unit and power grid dispatch instructions , as well as energy market signals and the internal status of carbon capture in thermal power units.

3. The multi-mode coordinated control method for a carbon capture system of a thermal power unit according to claim 2, characterized in that, In step two, the objective function is to minimize the total operating cost within the prediction time domain. J For multi-objective weighted sums: The cost of lost electricity revenue is related to the power generation capacity squeezed out by the energy consumption of carbon capture systems during periods of high electricity prices. Carbon capture costs and potential penalties for failing to meet emission reduction targets; The stability penalty term is designed to minimize drastic changes in control variables and ensure system stability.

4. The multi-mode coordinated control method for a carbon capture system of a thermal power unit according to claim 2, characterized in that, In step two, the decision variables include: Continuous variables: including rich liquid split ratio and reboiler heat load process control variables; Discrete variables: Introduce the operating mode variable Mode(k) {0,1,2} is defined as a key decision variable as follows: Mode(k)=1: "Energy storage mode"; Under this mode, the optimization algorithm tends to introduce the rich liquid generated by the absorption tower into the storage tank to reduce the immediate regeneration energy consumption; Mode(k)=2: "Carbon capture enhancement mode"; in this mode, the algorithm tends to draw rich solution from the storage tank to increase the regeneration load; Mode(k)=0: "Steady-state optimization operation mode"; Under this mode, the system operates according to the static optimal operating point based on nonlinear programming.

5. The multi-mode coordinated control method for a carbon capture system of a thermal power unit according to claim 2, characterized in that, In step two, the constraints include: system dynamic model constraints, equipment physical limit constraints, and logical constraints between modal and continuous variables.

6. The multi-mode coordinated control method for a carbon capture system of a thermal power unit according to claim 4, characterized in that, In step two, online solution and decision-making include: in each sampling period k, using a mathematical programming solver, solving the optimization problem online and applying the obtained optimal solution. Based on the optimal sequence of corresponding continuous control variables, determine the optimal operating mode that the system should be in at the current moment and the setpoints of each actuator.

7. The multi-mode coordinated control method for a carbon capture system of a thermal power unit according to claim 6, characterized in that, In step three, based on the decision results from step two, specific control instructions are generated and executed, including: like The control system will adjust the opening of the rich liquid diversion valve to allow the rich liquid to flow preferentially to the storage tank, and correspondingly reduce the opening of the reboiler steam valve to achieve energy storage. like The control system will start the rich liquor transfer pump and adjust its flow rate, while increasing the opening of the reboiler steam valve to handle the additional rich liquor and enhance carbon capture. like The control system will drive each variable to operate near the optimal setpoint obtained through offline or online steady-state optimization calculations.

8. A multi-mode coordinated control device for a carbon capture system of a thermal power unit, characterized in that, include: Multi-timescale state perception and prediction unit: Real-time data acquisition, collecting key state variables at time k, and based on these key state variables, establishing a dynamic mathematical model of the unit and carbon capture system for a finite future time domain. The system performs rolling predictions based on both external conditions and internal state variables to generate a prediction sequence. The rolling optimization-based operational modal decision unit: Based on the prediction sequence, a mixed integer rolling optimization problem with the future sampling period as the optimization time domain is constructed, which also includes the objective function, decision variables, constraints, and online solution and decision-making; Multivariable Coordination and Control Unit: Generates and executes specific control commands based on the decision results of the rolling optimization-based operating mode decision unit.

9. A multi-mode coordinated control device for a carbon capture system of a thermal power unit according to claim 8, characterized in that, In the multi-timescale state perception and prediction unit, key state variables at time k are collected, including: Generating capacity of the unit and power grid dispatch instructions , as well as energy market signals and the internal status of carbon capture in thermal power units.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of a multi-mode coordinated control method for a carbon capture system of a thermal power unit according to any one of claims 1-7.

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