Carbon dioxide energy storage and supercritical power generation coupling optimization control system and method, electronic equipment and medium

Abstract

The invention discloses a carbon dioxide energy storage and supercritical power generation coupling optimization control system and method, electronic equipment and a medium. The system comprises a carbon dioxide energy storage and power generation coupling system; the prediction and optimization module is used for predicting the future load and the future heat source intensity based on the coupling system parameters and the actual operation state data to obtain prediction results, and the prediction results comprise a load prediction value and a heat source intensity prediction value; the prediction and optimization module dynamically optimizes the carbon dioxide flow and heat distribution based on target functions and prediction results to obtain target data, and the target functions include power generation efficiency maximization, energy loss minimization and heat source utilization priority; the control module controls the carbon dioxide energy storage and power generation coupling system to adjust the compressor power, the opening degree of the carbon dioxide release valve and heat distribution of the heat exchanger based on the control instruction. And by dynamically optimizing CO2 flow, pressure and heat source distribution, the power generation efficiency and the heat source utilization rate are improved.

Description

Technical Field

[0001] This invention relates to the field of thermal engine energy storage technology, specifically to a coupled optimization control system, method, electronic equipment, and medium for carbon dioxide energy storage and supercritical power generation. Background Technology

[0002] The widespread application of intermittent energy sources such as wind and solar power has placed higher demands on the stability and peak-shaving capabilities of power grids. Carbon dioxide energy storage technology stores and releases electrical energy by compressing and releasing carbon dioxide (CO2), and has the potential for long-term energy storage and environmental friendliness.

[0003] In related technologies, CO2 energy storage systems typically utilize off-peak electricity to drive a compressor to compress CO2 to high pressure (7.38-20 MPa), store it in a high-pressure tank, and release the CO2 to generate electricity through an expander during peak electricity demand. However, limited by fixed operating parameters and a single energy storage mode, existing systems are inefficient in dealing with grid load fluctuations, suffer significant energy conversion losses, and struggle to effectively utilize the heat generated during compression.

[0004] Supercritical CO2 Brayton cycles have attracted significant attention in the power generation field due to their high efficiency (up to 40%-50%) and compact design. Supercritical CO2 (temperature > 31.1°C, pressure > 7.38 MPa) possesses high density and low viscosity, enabling it to perform work efficiently in turbines, making it suitable for heat sources such as solar energy, geothermal energy, or industrial waste heat. Existing supercritical CO2 power generation systems heat CO2 to a high-temperature and high-pressure state (300-400°C) through heat exchangers to drive turbines for power generation. However, these systems are mostly used as independent power generation units. When heat sources fluctuate (such as changes in solar radiation) or grid demand changes dynamically, it is difficult to achieve real-time optimization of operating parameters, leading to insufficient heat source utilization and fluctuating power generation efficiency. Summary of the Invention

[0005] The purpose of this invention is to achieve a deep integration of carbon dioxide energy storage and power generation through adaptive prediction and dynamic optimization, thereby realizing a carbon dioxide energy storage and supercritical power generation coupled optimization control system and method that improves system efficiency, heat source utilization and grid adaptability.

[0006] To achieve the above objectives, this invention proposes a carbon dioxide energy storage and supercritical power generation coupled optimization control system, comprising: a carbon dioxide energy storage and power generation coupled system, adapted to generate compressed carbon dioxide and generate electricity; a prediction and optimization module, communicatively connected to the carbon dioxide energy storage and power generation coupled system, to obtain coupled system parameters and actual operating status data, and to predict future load and future heat source intensity based on the coupled system parameters and the actual operating status data, obtaining prediction results, the prediction results including load prediction values ​​and heat source intensity prediction values, and the prediction and optimization module dynamically optimizing carbon dioxide flow rate and heat distribution based on an objective function and the prediction results, obtaining target data, the objective function including maximizing power generation efficiency, minimizing energy loss, and prioritizing heat source utilization, the target data including target carbon dioxide flow rate, target pressure, heat storage tank ratio, and external heat source ratio; and a control module, communicatively connected to the prediction and optimization module, to generate control commands based on the target data, and to control the carbon dioxide energy storage and power generation coupled system to adjust compressor power, carbon dioxide release valve opening, and heat exchanger heat distribution based on the control commands.

[0007] In one optional embodiment, the carbon dioxide energy storage and power generation coupling system includes: a carbon dioxide energy storage subsystem, comprising a compressor, a high-pressure storage tank, and a thermal storage tank; the compressor compresses carbon dioxide to generate compressed carbon dioxide, which is stored in the high-pressure storage tank; the heat of compression generated during the compression process is stored in the thermal storage tank; the compressor is communicatively connected to the control module to control the pressure range of the carbon dioxide within a preset range according to the control command; and a supercritical carbon dioxide power generation system, comprising a heat exchanger, a turbine, and a generator; the heat exchanger is connected to the high-pressure storage tank and the thermal storage tank via pipelines to exchange heat between the heat of compression and the compressed carbon dioxide to generate supercritical carbon dioxide; the supercritical carbon dioxide enters the turbine to perform work and drive the generator to generate electricity; the heat exchanger and the turbine are respectively communicatively connected to the control module to dynamically distribute heat according to the control command.

[0008] In one optional implementation, the prediction and optimization module includes: a data acquisition module, which is communicatively connected to the carbon dioxide energy storage and power generation coupling system to acquire parameters of the coupling system; and a system status sensing module, which is communicatively connected to the carbon dioxide energy storage and power generation coupling system to obtain the actual operating status data.

[0009] The prediction module is communicatively connected to the system state perception module and the data acquisition module, respectively, to predict the future grid load and future heat source intensity based on the actual operating state data and the coupled system parameters, and obtain the prediction results; the optimization module is communicatively connected to the long short-term memory network prediction module to construct an objective function under constraints based on the prediction results, the objective function including maximizing power generation efficiency, minimizing energy loss, and prioritizing heat source utilization.

[0010] This invention also proposes a carbon dioxide energy storage and supercritical power generation coupling optimization control method, comprising: acquiring coupling system parameters and actual operating status data of the carbon dioxide energy storage and power generation coupling optimization control system as described in any one of the claims; predicting future load and future heat source intensity based on the coupling system parameters and the actual operating status data, and obtaining prediction results; constructing an objective function based on a model predictive control algorithm and the prediction results to dynamically optimize carbon dioxide flow and heat distribution, and obtaining target data; generating control commands based on the target data; and controlling the carbon dioxide energy storage and power generation coupling system to adjust compressor power, carbon dioxide release valve opening, and heat distribution in the heat exchanger based on the control commands.

[0011] In one optional implementation, predicting future load and future heat source intensity based on the coupled system parameters and the actual operating state data to obtain prediction results specifically includes: constructing a long short-term memory network prediction model based on the coupled system parameters and the actual operating state data; predicting future load and future heat source intensity based on the long short-term memory network prediction model to obtain prediction results; wherein, the long short-term memory network prediction model includes a forget gate, an output gate, candidate cell states, cell state updates, an output gate, hidden topics, and prediction output; the expression for the forget gate is: The expression for the output gate is: The expression for the candidate unit state is: The expression for updating the unit state is:

[0012] The expression for the output gate is: ;

[0013] The expression for the hidden state is: The expression for the predicted output is: ; This represents the predicted values ​​of the power grid load and heat source intensity at the next moment; where, This is the sigmoid activation function, used to map the input to the [0,1] interval; This is the hyperbolic tangent activation function, used to map the input to the interval [-1, 1]. This is the weight matrix, which corresponds to the weights of the forget gate, input gate, candidate unit state, output gate, and predicted output, respectively. These are bias vectors, corresponding to the biases of the forget gate, input gate, candidate unit state, output gate, and predicted output, respectively. The hidden state from the previous time step represents the historical information of the LSTM model. This represents the current grid load, measured in MW, indicating the power demand that the system needs to respond to. The intensity of the external heat source at the current moment is expressed in kW / m², representing the available heat from solar radiation or industrial waste heat. The current pressure of the CO2 storage tank is expressed in MPa, indicating the operating status of the CO2 storage subsystem. The temperature of the heat storage tank at the current moment is expressed in °C, representing the available heat in the heat storage subsystem. The LSTM model stores the cell states from the previous and current time steps as its long-term memory. The candidate cell state represents a potential update of the cell state by the current input; The activation values ​​of the forget gate, input gate, and output gate control the flow of information. The hidden state at the current time step represents the short-term memory of the LSTM model; The output is the predicted value for the next time step. The grid load forecast for the next moment, in MW, is used to guide CO2 release and power generation dispatch. The predicted heat source intensity for the next moment, expressed in kW / m², is used to optimize heat distribution.

[0014] In one optional implementation, an objective function is constructed based on the model predictive control algorithm and the prediction results to dynamically optimize carbon dioxide flow and heat allocation, thereby obtaining target data. Specifically, this includes: executing a heat source priority allocation strategy based on priority rules, where the priority rule prioritizes the use of heat storage tanks to store heat, and the expression for heat is: In the formula, Specific heat capacity of the heat storage medium; For medium quality; When the heat storage tank is insufficient, the system switches to an external heat source and optimizes the matching degree between the carbon dioxide flow rate and the heat source to obtain the target data.

[0015] In one optional implementation, the expression for the carbon dioxide flow rate and the heat source matching degree is: ; This refers to the carbon dioxide flow rate; Available heat; The target temperature; The current carbon dioxide temperature; This is the proportionality coefficient.

[0016] In one optional implementation, the expression for the objective function is: In the formula, For power generation efficiency; For energy loss; These are the weighting coefficients; For heat source utilization rate; , These are the weighting coefficients; the constraints include: Pressure constraints For temperature constraints, turbine power constraints, heat distribution constraints and Flow constraints.

[0017] The present invention also proposes an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform any of the carbon dioxide energy storage and supercritical power generation coupling optimization control methods described herein.

[0018] The present invention also proposes a medium storing a computer program, which, when executed by a processor, implements the multi-scenario coupled optimization control method for carbon dioxide energy storage as described in any one of the claims.

[0019] The beneficial effects of this invention are as follows: by constructing a carbon dioxide energy storage and power generation coupling system and a prediction and optimization module, dynamic optimization of CO2 flow, pressure and heat source allocation is achieved, thereby improving power generation efficiency and heat source utilization. Attached Figure Description

[0020] Figure 1 A block diagram of a multi-scenario coupled optimization control system for carbon dioxide energy storage provided in an embodiment of the present invention;

[0021] Figure 2 A flowchart of a multi-scenario coupled optimization control method for carbon dioxide energy storage provided in an embodiment of the present invention;

[0022] Figure 3 A flowchart illustrating the framework of a multi-scenario coupled optimization control method for carbon dioxide energy storage provided in another embodiment of the present invention.

[0023] Figure descriptions: 10. Carbon dioxide energy storage subsystem; 11. Compressor; 12. High-pressure storage tank; 13. Thermal storage tank; 20. Supercritical carbon dioxide power generation system; 21. Heat exchanger; 22. Turbine; 23. Generator; 30. Prediction and optimization module; 31. Data acquisition module; 32. System status perception module; 33. Prediction calculation unit; 34. Real-time feedback channel; 35. Command output module. Detailed Implementation

[0024] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0025] like Figure 1 and Figure 3 As shown, according to an embodiment of the present invention, in one aspect, a multi-scenario coupled optimization control system for carbon dioxide energy storage is provided, comprising: a carbon dioxide energy storage and power generation coupling system, adapted to generate compressed carbon dioxide and generate electricity; a prediction and optimization module 30, communicatively connected to the carbon dioxide energy storage and power generation coupling system, to obtain coupling system parameters and actual operating status data, and to predict future load and future heat source intensity based on the coupling system parameters and actual operating status data, obtaining prediction results, the prediction results including load prediction values ​​and heat source intensity prediction values, and the prediction and optimization module 30 dynamically optimizes carbon dioxide flow rate and heat distribution based on objective function and prediction results, obtaining target data, the objective function including maximizing power generation efficiency, minimizing energy loss and heat source utilization priority, the target data including target carbon dioxide flow rate, target pressure, heat storage tank 13 ratio and external heat source ratio; and a control module, communicatively connected to the prediction and optimization module 30, to generate control commands based on the target data, and to control the carbon dioxide energy storage and power generation coupling system to adjust compressor power, carbon dioxide release valve opening and heat exchanger heat distribution based on the control commands.

[0026] In this embodiment, the multi-scenario coupled optimization control system for carbon dioxide energy storage mainly includes a carbon dioxide energy storage and power generation coupling system, a prediction and optimization module 30, and a control module.

[0027] The carbon dioxide energy storage and power generation coupling system is the core of the entire control system's energy conversion, its main function being to generate compressed carbon dioxide and generate electricity. In the energy storage phase, the system utilizes low-cost electricity during off-peak hours to drive compressor 11 to compress carbon dioxide gas. During compression, the carbon dioxide's temperature rises, and the resulting heat is effectively collected and stored in thermal storage tank 13. The compressed carbon dioxide is stored in the tank in a high-pressure liquid form, realizing the conversion of electrical energy into pressure energy and thermal energy. In the power generation phase, the high-pressure liquid carbon dioxide is released and expands through an expander, driving generator 23 to generate electricity. Simultaneously, the heat stored in thermal storage tank 13 is used to heat the carbon dioxide, improving expansion efficiency and thus increasing power generation. The entire process achieves efficient conversion between electrical energy, pressure energy, and thermal energy, providing a foundation for flexible energy allocation and utilization.

[0028] The prediction and optimization module 30 establishes a real-time communication connection with the carbon dioxide energy storage and power generation coupling system through a high-speed communication network. This allows it to quickly and accurately acquire key parameters of the coupling system, such as carbon dioxide flow rate, pressure, and temperature; the power and efficiency of compressor 11; and the power generation of generator 23. Simultaneously, it collects real-time data on the system's actual operating status, including equipment runtime, fault information, and energy consumption. Based on this rich data, the prediction and optimization module 30 employs advanced machine learning algorithms and data analysis models to accurately predict future load demand and heat source intensity. For example, by comprehensively analyzing multi-dimensional information such as historical electricity consumption data, meteorological data, and economic activity data, a load prediction model is established to obtain load prediction values ​​for different future periods. Furthermore, by utilizing historical temperature changes, usage patterns, and related influencing factors of the heat source, future heat source intensity prediction values ​​are obtained.

[0029] After obtaining the prediction results, the prediction and optimization module 30 dynamically optimizes the carbon dioxide flow rate and heat distribution based on the set objective function. The objective function comprehensively considers several key factors: first, maximizing power generation efficiency by rationally adjusting the carbon dioxide flow rate and heat supply during the expansion process to ensure the power generation equipment operates under optimal conditions; second, minimizing energy loss by analyzing and optimizing energy losses in each stage of system operation to reduce energy waste caused by factors such as heat dissipation and friction; and third, determining the priority of heat source utilization by prioritizing the use of lower-cost or more stable heat sources, such as industrial waste heat, to improve the economy and sustainability of energy utilization. After a complex calculation and optimization process, the target data is finally obtained, including the target carbon dioxide flow rate, target pressure, the proportion of the thermal storage tank 13, and the proportion of external heat sources, providing precise control parameters for system operation.

[0030] By dynamically optimizing carbon dioxide flow and heat distribution, this system can fully utilize energy and reduce energy losses during conversion and storage. Regarding heat source utilization, it rationally allocates heat from the storage tank and external heat sources according to priority, improving the utilization rate of low-grade heat sources such as industrial waste heat. This achieves cascaded energy utilization and significantly improves overall energy efficiency, increasing it by 10%-15% compared to traditional carbon dioxide energy storage systems.

[0031] The prediction and optimization module 30 accurately forecasts future loads, enabling the system to adjust its operating status in advance and respond quickly to load changes. Whether facing peak-valley fluctuations in grid load or sudden changes in electricity demand, the system can adjust its power generation capacity in a timely manner, ensuring the stability and reliability of power supply, effectively reducing the peak-shaving pressure on the grid, and improving the flexibility and stability of the power system.

[0032] The objective function comprehensively considers multiple objectives, including maximizing power generation efficiency, minimizing energy loss, and prioritizing heat source utilization. Through multi-objective collaborative optimization, it achieves a comprehensive improvement in system performance. While ensuring power generation efficiency, it reduces operating costs, improves the system's economics and sustainability, and lays a solid foundation for the large-scale application and commercialization of carbon dioxide energy storage technology.

[0033] The control module is connected to the prediction and optimization module 30 via a stable communication link. After receiving the target data output by the prediction and optimization module 30, the control module quickly analyzes and processes the data, generating corresponding control commands based on preset control logic. These control commands are sent to various key actuators of the carbon dioxide energy storage and power generation coupling system to achieve precise control of the system's operating status. For example, the control commands can control the power regulation of the compressor 11. When a lower load is predicted in the future, the compressor power is reduced, decreasing energy storage; when the load increases, the compressor power is increased, increasing energy storage. Simultaneously, the control commands can also adjust the opening of the carbon dioxide release valve to control the carbon dioxide release flow rate, thereby affecting the power generation output. Furthermore, the control commands can precisely control the heat distribution of the heat exchanger 21, rationally adjusting the ratio of heat from the heat storage tank to the external heat source, ensuring that the system achieves efficient energy utilization while meeting power generation requirements.

[0034] This system integrates advanced predictive algorithms and intelligent control technology, achieving intelligent operation throughout the entire process from data acquisition and analysis to prediction and optimized control. It reduces manual intervention, lowers the risk of human error, and improves the safety and reliability of system operation, while also reducing maintenance costs and increasing management efficiency. Because the system can dynamically optimize according to different load demands and heat source conditions, it possesses strong adaptability to multiple scenarios. Whether applied to large-scale centralized energy storage power stations, distributed energy systems, or coupled with industrial waste heat recovery and renewable energy power generation, it can operate efficiently and stably, expanding the application scope of carbon dioxide energy storage technology.

[0035] Furthermore, the carbon dioxide energy storage and power generation coupling system includes: a carbon dioxide energy storage subsystem 10, including a compressor 11, a high-pressure storage tank 12, and a thermal storage tank 13. The compressor 11 compresses carbon dioxide to generate compressed carbon dioxide, which is stored in the high-pressure storage tank 12. The heat generated during compression is stored in the thermal storage tank 13. The compressor 11 is communicatively connected to a control module to control the pressure range of the carbon dioxide within a preset range according to control commands; and a supercritical carbon dioxide power generation system 20, including a heat exchanger 21, a turbine 22, and a generator 23. The heat exchanger 21 is connected to the high-pressure storage tank 12 and the thermal storage tank 13 through pipelines to exchange heat between the heat of compression and the compressed carbon dioxide and generate supercritical carbon dioxide. The supercritical carbon dioxide enters the turbine 22 to do work and drive the generator 23 to generate electricity. The heat exchanger 21 and the turbine 22 are communicatively connected to the control module to dynamically distribute heat according to control commands.

[0036] In this embodiment, the CO2 energy storage subsystem is used to compress and store CO2; the supercritical CO2 power generation system is used to release CO2 and heat it to a supercritical state to generate electricity.

[0037] The carbon dioxide energy storage subsystem 10 is a key component for converting electrical energy into pressure energy and thermal energy. It mainly consists of three core components: compressor 11, high-pressure storage tank 12, and thermal storage tank 13.

[0038] Compressor 11 employs multi-stage compression technology, equipped with a variable frequency motor, supporting dynamic power adjustment, and achieving a compression efficiency >85%. During compression, the intermolecular distance of carbon dioxide decreases, internal energy increases, temperature rises sharply, and pressure increases significantly, ultimately generating high-pressure, high-temperature compressed carbon dioxide gas. The generated compressed carbon dioxide gas is transported to high-pressure storage tank 12 for storage. High-pressure storage tank 12 is designed for a maximum pressure of 25 MPa, constructed of corrosion-resistant high-strength alloy steel, and equipped with pressure sensors and safety valves. The tank volume is customized according to the application scale. Heat storage tank 13 uses molten salt or heat transfer oil as the heat storage medium, with a heat storage temperature of 300-400°C. It is equipped with a high-efficiency insulation layer, resulting in a heat loss rate of <2% / day. Heat exchanger 21 is a plate or shell-and-tube type, with a heat exchange efficiency >90%. The subsystem dynamically adjusts the compressor power and CO2 flow rate according to instructions to avoid overpressure or heat waste, and is coupled to heat storage tank 13 through a piping system to ensure the coordinated storage of heat and CO2.

[0039] Meanwhile, the large amount of heat generated during the compression process of compressor 11 is collected and transferred to heat storage tank 13 through a high-efficiency heat exchange device. Heat storage tank 13 uses advanced insulation materials and heat storage medium to store compression heat efficiently and for extended periods, preventing heat loss and achieving effective heat energy recovery and utilization. Furthermore, compressor 11 establishes a real-time connection with the control module via a high-speed communication line. The control module sends control commands to compressor 11 in real time based on the target pressure data output by prediction and optimization module 30. Upon receiving the commands, compressor 11 precisely controls the degree of carbon dioxide compression by adjusting motor speed, compression stages, and intake valve opening, ensuring that the carbon dioxide pressure is stably maintained within a preset range, thus guaranteeing the safety and efficiency of the energy storage process.

[0040] The supercritical carbon dioxide power generation system 20 is used to release CO2 from the high-pressure storage tank 12 during peak electricity demand. The CO2 is then heated to a supercritical state via heat exchanger 21 using stored heat or an external heat source, driving turbine 22 to generate electricity. The supercritical carbon dioxide power generation system 20 includes a heat exchanger 21, turbine 22, generator 23, and cooler. The heat exchanger 21 is a high-temperature, high-pressure type, designed for a maximum temperature of 450°C and a pressure of 25 MPa, supporting multiple heat source inputs with a heat exchange efficiency >92%. The turbine 22 is specifically designed for supercritical CO2, with a rated power of 100-1000 kW and an efficiency >90%, equipped with a variable speed control system. The generator 23 is a synchronous generator with an efficiency >95%, supporting grid-connected or islanded operation. The cooler is used to cool the expanded CO2, reducing its temperature to 30-50°C. The supercritical carbon dioxide power generation system 20 dynamically adjusts the CO2 flow rate and heat exchanger 21 temperature according to an MPC algorithm, ensuring efficient operation of turbine 22 and supporting rapid start-up and shutdown to meet grid peak-shaving demands.

[0041] During the power generation phase, compressed carbon dioxide in high-pressure storage tank 12 is transported to heat exchanger 21. Simultaneously, the heat of compression in hot storage tank 13 is also introduced into heat exchanger 21. Heat exchanger 21 transfers the heat of compression to the compressed carbon dioxide, further increasing its temperature and pressure to a supercritical state. Supercritical carbon dioxide possesses unique physical properties; its density is close to that of a liquid, its viscosity is close to that of a gas, and its heat transfer performance is excellent, creating conditions for efficient power generation. The generated supercritical carbon dioxide then enters turbine 22. Inside turbine 22, the supercritical carbon dioxide expands and does work, driving the turbine blades to rotate at high speed. Turbine 22 and generator 23 are rigidly connected via a drive shaft. The rotational mechanical energy of turbine 22 is directly transferred to generator 23, driving the generator rotor to cut magnetic field lines, thereby converting mechanical energy into electrical energy and achieving power output.

[0042] Both heat exchanger 21 and turbine 22 establish bidirectional communication with the control module. Based on the heat distribution scheme determined by the prediction and optimization module 30, the control module sends control commands to heat exchanger 21, dynamically adjusting parameters such as the heat exchange area and hot fluid flow rate of heat exchanger 21. This precisely controls the degree of heat exchange between the heat storage tank and compressed carbon dioxide, ensuring the generation of supercritical carbon dioxide that meets power generation requirements. Simultaneously, the control module can also adjust the opening of the turbine 22's intake valve according to changes in grid load, controlling the flow rate of supercritical carbon dioxide, thereby adjusting the turbine 22's speed and the generator 23's output power, achieving flexible control of the power generation process.

[0043] This coupled system realizes a complete energy conversion chain from electrical energy to pressure energy to thermal energy and back to electrical energy. By recovering and utilizing the heat of compression, it avoids the energy waste caused by the direct loss of heat of compression in traditional energy storage systems. Compared with conventional energy storage systems, it can improve the overall energy utilization rate by 20%-30%, significantly improving energy utilization efficiency and reducing the cost of energy storage power generation.

[0044] The real-time communication mechanism between the compressor 11 and the control module can accurately control the carbon dioxide pressure within a preset range, effectively avoiding equipment damage or safety risks caused by excessive pressure; the high-pressure storage tank 12 adopts high-strength materials and a special structural design, possessing excellent pressure-bearing and sealing performance; the various components of the system are connected by precise pipelines and intelligent control, ensuring stable and reliable operation, significantly reducing the probability of equipment failure, and improving the overall safety of the system.

[0045] The supercritical carbon dioxide power generation system 20 utilizes the unique physical properties of supercritical carbon dioxide, resulting in higher cycle efficiency compared to traditional steam power generation systems. Under the same heat source conditions, it can improve power generation efficiency, and the system is smaller, lighter, and requires significantly less land, effectively reducing site construction costs while also reducing greenhouse gas emissions, thus demonstrating significant environmental benefits.

[0046] The control module's dynamic control of heat exchanger 21 and turbine 22 enables the system to respond quickly to changes in grid load. Whether dealing with high power demand during peak periods or low load conditions during off-peak periods, the system can flexibly adjust power generation by regulating heat distribution and carbon dioxide flow, achieving friendly interaction with the grid and improving the stability and peak-shaving capacity of the power system.

[0047] The carbon dioxide energy storage subsystem 10 and the supercritical carbon dioxide power generation system 20 adopt a modular design, with each module being relatively independent yet closely collaborative. In practical applications, the number of modules can be flexibly increased or decreased according to different energy storage scales and power generation needs, facilitating system expansion and upgrades. It is suitable for various application scenarios, such as large-scale energy storage power stations and distributed energy systems, and has broad market application prospects.

[0048] Furthermore, the prediction and optimization module 30 includes: a data acquisition module 31, which is communicatively connected to the carbon dioxide energy storage and power generation coupling system to acquire coupling system parameters; a system state perception module 32, which is communicatively connected to the carbon dioxide energy storage and power generation coupling system to obtain actual operating state data; a prediction module, which is communicatively connected to the system state perception module 32 and the data acquisition module 31 respectively, to predict future grid load and future heat source intensity based on actual operating state data and coupling system parameters, and obtain prediction results; and an optimization module, which is communicatively connected to the long short-term memory network prediction module, to construct an objective function under constraints based on the prediction results, the objective function including maximizing power generation efficiency, minimizing energy loss, and prioritizing heat source utilization.

[0049] The prediction and optimization module 30 is an adaptive predictive control system used to dynamically adjust CO2 flow, pressure, and heat source distribution through real-time feedback channel 34 and algorithm optimization to adapt to grid load and heat source fluctuations. The prediction and optimization module 30 includes a data acquisition module 31, a system status sensing module 32, a prediction calculation unit 33, a command output module 35, and a real-time feedback channel 34, used to dynamically optimize CO2 flow, pressure, and heat source distribution.

[0050] The data acquisition module 31 establishes a real-time connection with the carbon dioxide energy storage and power generation coupling system via high-speed communication protocols (such as ModbusTCP and OPC UA). The data acquisition module 31 collects data on grid load, external heat source intensity, storage tank pressure, and the temperature of the thermal storage tank 13. The system status perception module 32 monitors compressor power, turbine 22 efficiency, and heat exchanger heat distribution. The prediction calculation unit 33 runs an adaptive LSTM model to predict the grid load and heat source intensity for the next hour. The command output module 35 generates control commands for compressor power, CO2 release valve opening, and heat exchanger heat distribution based on the MPC algorithm. The real-time feedback channel 34 updates the operating status data at a frequency of 1 second, supporting online training of the LSTM model.

[0051] The prediction calculation unit 33 includes an Adaptive Long Short-Term Memory (LSTM) network model and a Model Predictive Control (MPC) algorithm. The LSTM model has a prediction timescale of 5 minutes to 1 hour and is used to predict grid load and external heat source fluctuations. The MPC algorithm calculates the control sequence for CO2 flow, pressure, and heat distribution with the objective functions of maximizing power generation efficiency, minimizing energy loss, and prioritizing heat source utilization. The prediction and optimization module 30 performs collaborative optimization using the LSTM and MPC algorithms. The LSTM model has a prediction error of <5%, while the MPC algorithm optimizes heat source utilization with the objectives of maximizing power generation efficiency, minimizing energy loss, and prioritizing the utilization of heat from the thermal storage tank.

[0052] The MPC algorithm uses the prediction results of the LSTM model as input and combines them with the real-time operating status of the system to construct an optimization model with multi-objective constraints. Its objective function comprehensively considers maximizing power generation efficiency (improving the power generation efficiency of turbine 22 by optimizing the thermodynamic parameters during the carbon dioxide expansion process), minimizing energy loss (reducing compression heat loss and pipeline resistance losses), and prioritizing heat source utilization (prioritizing the consumption of industrial waste heat to reduce external energy procurement costs). Through a rolling optimization strategy, the MPC algorithm calculates the carbon dioxide flow rate, pressure setpoint, and heat distribution scheme for a future period (e.g., 10 control steps) within each control cycle, and dynamically corrects the control sequence based on actual feedback to achieve optimal control of the complex nonlinear system.

[0053] The control parameters generated by the optimization module (such as the target carbon dioxide flow rate and the output power of the thermal storage tank 13) are converted into executable control commands by the command output module 35 and sent to the control module through a redundant communication link. Simultaneously, the real-time feedback channel 34 continuously receives actual operating data from the carbon dioxide energy storage and power generation coupling system and compares it with the prediction results. If a deviation occurs, the system immediately triggers an adaptive mechanism to adjust the prediction parameters of the LSTM model and the optimization boundary conditions of the MPC algorithm, forming a closed-loop control loop of "prediction-optimization-control-feedback" to ensure that the system always operates under optimal conditions.

[0054] The coupling mechanism in this invention includes physical coupling and operational coupling. Physical coupling means that after CO2 is compressed and stored in the energy storage subsystem, it is directly transported to the power generation system, reducing working fluid conversion losses. The thermal storage tank 13 serves as the heat hub for the two subsystems, storing compression heat during the energy storage phase and heating CO2 during the power generation phase, achieving a heat transfer efficiency of >90%. High-pressure and high-temperature pipelines are used, equipped with flow control valves and pressure regulators to ensure stable CO2 transmission. Operational coupling means that during the energy storage phase, the compressor power and CO2 flow rate are optimized based on the off-peak electricity price period predicted by LSTM, and the heat stored in the thermal storage tank 13 prioritizes meeting the power generation demand. During the power generation phase, the CO2 release and heat distribution of the heat exchanger are dynamically adjusted based on the peak load predicted by LSTM, and the MPC algorithm optimizes the matching degree between CO2 flow rate and heat source. The adaptive predictive control system monitors the status of the two subsystems through the real-time feedback channel 34 and uniformly adjusts the operating parameters of the compressor 11, valves, and heat exchanger 21.

[0055] like Figure 2 As shown, this invention also proposes an optimized control method for the coupling of carbon dioxide energy storage and supercritical power generation, comprising the following steps:

[0056] Step S101: Obtain the coupling system parameters and actual operating status data of the carbon dioxide energy storage and power generation coupling optimization control system based on carbon dioxide energy storage and supercritical carbon dioxide power generation coupling system.

[0057] Step S103: Based on the coupled system parameters and actual operating status data, predict the future load and future heat source intensity to obtain the prediction results.

[0058] Step S105: Construct an objective function based on the model predictive control algorithm and prediction results to dynamically optimize carbon dioxide flow and heat distribution, and obtain target data.

[0059] Step S107: Generate control commands based on target data.

[0060] Step S109: Based on control commands, control the carbon dioxide energy storage and power generation coupling system to adjust the compressor power, the opening degree of the carbon dioxide release valve, and the heat distribution of the heat exchanger.

[0061] By predicting future loads and heat source intensity based on coupled system parameters and actual operating status data, and constructing an objective function using model predictive control algorithms, dynamic optimization of carbon dioxide flow and heat distribution is achieved. This enables precise matching of system operating requirements with external changes, significantly improving the accuracy and adaptability of system operation, and optimizing overall system performance.

[0062] By optimizing and controlling the carbon dioxide flow and heat distribution, the energy flow in the carbon dioxide energy storage and supercritical power generation processes is effectively coordinated, achieving efficient synergy between energy storage and power generation, improving energy conversion efficiency and utilization efficiency, reducing energy loss, and increasing the overall energy utilization rate of the system.

[0063] Based on the prediction results and optimized target data, control commands are generated to adjust the compressor power, the opening of the carbon dioxide release valve, and the heat distribution of the heat exchanger. This enables the system to respond promptly to changes in load and heat source intensity, enhancing the stability and reliability of the system under different operating conditions, reducing system fluctuations, and ensuring stable power generation.

[0064] Further, step S103 involves predicting future loads and future heat source intensity based on coupled system parameters and actual operating status data to obtain prediction results, specifically including the following steps:

[0065] Step S1031: Construct a long short-term memory network prediction model based on coupled system parameters and actual operating state data.

[0066] Step S1033: Predict future load and future heat source intensity based on the long short-term memory network prediction model to obtain the prediction results.

[0067] The Long Short-Term Memory Network prediction model includes a forgetting gate, an output gate, candidate unit states, unit state updates, output gates, hidden topics, and predicted outputs.

[0068] The expression for the forget gate is:

[0069] ;

[0070] The expression for the output gate is:

[0071] ;

[0072] The expression for the candidate cell state is:

[0073] ;

[0074] The expression for updating the cell state is:

[0075] ;

[0076] The expression for the output gate is:

[0077] ;

[0078] The expression for the hidden state is:

[0079] ;

[0080] The expression for the predicted output is: ;

[0081] This represents the predicted values ​​of the power grid load and heat source intensity at the next moment;

[0082] In the formula, This is the sigmoid activation function, used to map the input to the [0,1] interval; This is the hyperbolic tangent activation function, used to map the input to the interval [-1, 1]. This is the weight matrix, which corresponds to the weights of the forget gate, input gate, candidate unit state, output gate, and predicted output, respectively. These are bias vectors, corresponding to the biases of the forget gate, input gate, candidate unit state, output gate, and predicted output, respectively. The hidden state from the previous time step represents the historical information of the LSTM model. This represents the current grid load, measured in MW, indicating the power demand that the system needs to respond to. The intensity of the external heat source at the current moment is expressed in kW / m², representing the available heat from solar radiation or industrial waste heat. The current pressure of the CO2 storage tank is expressed in MPa, indicating the operating status of the CO2 storage subsystem. The temperature of the heat storage tank 13 at the current moment is expressed in °C, representing the available heat of the heat storage subsystem. The LSTM model stores the cell states from the previous and current time steps as its long-term memory. The candidate cell state represents a potential update of the cell state by the current input; The activation values ​​of the forget gate, input gate, and output gate control the flow of information. The hidden state at the current time step represents the short-term memory of the LSTM model; The output is the predicted value for the next time step. The grid load forecast for the next moment, in MW, is used to guide CO2 release and power generation dispatch. The predicted heat source intensity for the next moment, expressed in kW / m², is used to optimize heat distribution.

[0083] Combination Figure 3 As shown, the adaptive prediction system's core algorithms are the adaptive LSTM model and the MPC algorithm. The adaptive LSTM model guides the operation and scheduling of CO2 storage and power generation by predicting the grid load and external heat source intensity for the next hour, ensuring the system's efficient response to grid demand and heat source fluctuations. Input parameters include: This represents the current grid load, reflecting real-time electricity demand. The intensity of the external heat source at the current moment reflects the available heat from solar energy or waste heat. The current pressure of the CO2 storage tank indicates the energy storage status. The current temperature of heat storage tank 13 represents the available heat. This is based on current weather data, which influences heat source intensity prediction. Output parameters include: The predicted grid load for the next moment is used to determine the CO2 release. The predicted heat source intensity for the next moment is used to optimize heat distribution in the heat exchanger. The adaptive mechanism obtains actual operating data, including the actual grid load, through real-time feedback channel 34. and heat source intensity Calculate the prediction error The gradient descent algorithm is used to update the weight matrix. and bias The updated formula is as follows and ,in, The learning rate is set to ensure the model adapts to dynamic changes in grid load and heat sources, with prediction errors controlled within 5%. In terms of time scale, the prediction window ranges from 5 minutes to 1 hour, with a step size of 1 minute, supporting minute-level real-time scheduling and hourly operation planning.

[0084] The MPC algorithm uses LSTM to predict the grid load. and heat source intensity This optimizes CO2 flow, pressure, and heat distribution to ensure maximum power generation efficiency, minimum energy loss, and efficient utilization of heat sources. Input parameters include: To determine the generation demand based on the predicted grid load; To assess the available heat source intensity as predicted; This represents the current pressure of the CO2 storage tank, reflecting the energy storage status. The current temperature of thermal storage tank 13 reflects the stored heat. This refers to available heat from an external heat source, such as solar energy or waste heat. Output parameters include: The CO2 flow rate is used to control the release rate. Adjust turbine operating condition 22 to achieve the target CO2 pressure. This refers to the heat distribution ratio of the thermal storage tank. The heat distribution ratio of the external heat source should meet the requirements. .

[0085] The expression for the objective function is:

[0086] ;

[0087] In the formula, For power generation efficiency; For energy loss; These are the weighting coefficients; For heat source utilization rate; , These are the weighting coefficients.

[0088] The constraints include: Pressure constraints For temperature constraints, turbine 22 power constraints, heat distribution constraints and Flow constraints.

[0089] in: ; For turbine efficiency 22; CO2 mass flow rate; Enthalpy difference; For energy loss, For compression work, Heat is recovered for heat storage tank 13; , For actual heat usage, This refers to the actual heat used; These are weighting coefficients used to balance thermal efficiency and heat utilization. Constraints include: CO2 pressure constraint. To ensure supercritical state; CO2 temperature constraint, This meets the operating requirements of turbine 22; turbine 22 power constraint, Matching grid demand; heat distribution constraints, To ensure heat balance; CO2 flow constraint, This satisfies the system capacity.

[0090] Further, in step S105, an objective function is constructed based on the model predictive control algorithm and the prediction results to dynamically optimize the carbon dioxide flow and heat distribution, thereby obtaining the target data. This specifically includes the following steps:

[0091] Step S1051: Execute the heat source priority allocation strategy based on the priority rule. The priority rule is to give priority to using the heat storage tank 13 to store heat. The expression for heat is:

[0092] ;

[0093] In the formula, Specific heat capacity of the heat storage medium; For medium quality; ;

[0094] When the heat storage tank is insufficient, it switches to an external heat source and optimizes the matching degree between carbon dioxide flow and heat source to obtain the target data.

[0095] The expression for the matching degree between carbon dioxide flow rate and heat source is:

[0096] ;

[0097] This refers to the carbon dioxide flow rate; Available heat; The target temperature; The current carbon dioxide temperature; This is the proportionality coefficient;

[0098] The priority rule is to prioritize the use of thermal storage tank 13 to store heat at a temperature of 300-400℃. ,in The specific heat capacity of the heat storage medium. For the quality of the medium, When the heat storage tank is insufficient, , Switch to an external heat source. Simultaneously, the CO2 flow rate and heat source matching degree are calculated using the formula: ,in, , , , The current CO2 temperature. The MPC algorithm is based on the predicted heat source intensity. Temperature of heating storage tank 13 Dynamic adjustment To ensure that energy utilization efficiency meets standards.

[0099] The technical effects achieved by this invention are as follows:

[0100] High-efficiency energy conversion: By optimizing CO2 flow and heat distribution through adaptive LSTM and MPC algorithms, the power generation efficiency is increased to 40%-50%, which is 10%-15% higher than the traditional fixed control strategy.

[0101] Improved heat source utilization: The heat storage tank's 13-priority allocation and multi-heat source input design enable a heat source utilization rate of >90%, reducing heat waste.

[0102] Dynamic response capability: The LSTM model has a prediction error of <5%, supports grid load and heat source intensity prediction at the minute to hour level, and adapts to the intermittency of renewable energy and fluctuations in industrial waste heat.

[0103] System integration: The CO2 working fluid and the thermal storage tank 13 are shared, reducing working fluid conversion losses and achieving a heat transfer efficiency of >90%.

[0104] Environmentally friendly: It utilizes off-peak electricity and industrial waste heat to reduce carbon emissions and is suitable for renewable energy grid connection and grid peak shaving.

[0105] The present invention also proposes an electronic device, comprising: at least one processor; a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to execute any of the carbon dioxide energy storage and supercritical power generation coupling optimization control methods.

[0106] On the other hand, the present invention proposes a medium, which is a computer storage medium storing a computer program, wherein the computer program, when executed by a processor, implements any one of the optimized control methods for the coupling of carbon dioxide energy storage and supercritical power generation.

[0107] Computer storage media may be simply referred to as media. 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 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 may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may 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 SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), Rambus Direct RAM (RDRAM), Direct Memory Bus Dynamic RAM (DRDRAM), and Memory Bus Dynamic RAM (RDRAM). The various embodiments described in this specification are presented in a progressive manner, with reference allowed to each other for similar or identical parts. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments for apparatuses, devices, and non-volatile computer storage media are described simply because they are substantially similar to the method embodiments; relevant details can be found in the descriptions of the method embodiments.

[0108] The above embodiments are merely illustrative examples and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A carbon dioxide energy storage and supercritical power generation coupling optimization control system, characterized in that, The system comprises: A carbon dioxide energy storage and power generation coupling system suitable for generating compressed carbon dioxide and generating power; A prediction and optimization module in communication with the carbon dioxide energy storage and power generation coupling system to obtain coupling system parameters and actual operating state data, and to predict future load and future heat source intensity based on the coupling system parameters and the actual operating state data to obtain prediction results, the prediction results including load prediction values and heat source intensity prediction values, and the prediction and optimization module dynamically optimizing carbon dioxide flow and heat distribution based on an objective function and the prediction results to obtain target data, the objective function including maximizing power generation efficiency, minimizing energy loss, and heat source utilization priority, and the target data including carbon dioxide target flow, target pressure, hot storage tank proportion, and external heat source proportion; A control module in communication with the prediction and optimization module to generate control instructions based on the target data, and to control the carbon dioxide energy storage and power generation coupling system to adjust compressor power, carbon dioxide release valve opening, and heat exchanger heat distribution based on the control instructions.

2. The carbon dioxide energy storage and supercritical power generation coupling optimization control system according to claim 1, characterized in that, The carbon dioxide energy storage and power generation coupling system comprises: A carbon dioxide energy storage subsystem comprising a compressor, a high-pressure storage tank, and a hot storage tank, the compressor compressing carbon dioxide to generate compressed carbon dioxide and store it in the high-pressure storage tank, the compression heat generated during the compression process being stored in the hot storage tank, the compressor being in communication with the control module to control the pressure range of the carbon dioxide to be within a preset range according to the control instructions; A supercritical carbon dioxide power generation subsystem comprising a heat exchanger, a turbine, and a generator, the heat exchanger being connected to the high-pressure storage tank and the hot storage tank through pipelines to exchange heat with the compression heat and the compressed carbon dioxide and generate supercritical carbon dioxide, the supercritical carbon dioxide entering the turbine to do work and driving the generator to generate power, the heat exchanger and the turbine being in communication with the control module to dynamically distribute heat according to the control instructions.

3. The carbon dioxide energy storage and supercritical power generation coupling optimization control system according to claim 1, characterized in that, The prediction and optimization module comprises: A data acquisition module in communication with the carbon dioxide energy storage and power generation coupling system to acquire the coupling system parameters; A system state sensing module in communication with the carbon dioxide energy storage and power generation coupling system to obtain the actual operating state data; A prediction module in communication with the system state sensing module and the data acquisition module to predict future power grid load and future heat source intensity based on the actual operating state data and the coupling system parameters to obtain prediction results; An optimization module in communication with the long short-term memory network prediction module to construct an objective function under constraints based on the prediction results, the objective function including maximizing power generation efficiency, minimizing energy loss, and heat source utilization priority.

4. A method for optimizing control of carbon dioxide energy storage and supercritical power generation coupling, characterized in that, The system comprises: A carbon dioxide energy storage and power generation coupling system based on the carbon dioxide energy storage and supercritical carbon dioxide power generation coupling optimization control system of any one of claims 1 to 3 to obtain coupling system parameters and actual operating state data; predict future load and future heat source intensity based on the coupling system parameters and the actual operation state data, to obtain a prediction result; construct a target function based on a model predictive control algorithm and the prediction result, to dynamically optimize carbon dioxide flow and heat distribution, to obtain target data; generate a control instruction based on the target data; control the carbon dioxide energy storage and power generation coupling system to adjust compressor power, carbon dioxide release valve opening degree and heat exchanger heat distribution based on the control instruction.

5. The method according to claim 4, wherein, predict future load and future heat source intensity based on the coupling system parameters and the actual operation state data, to obtain a prediction result, specifically including: construct a long short-term memory network prediction model based on the coupling system parameters and the actual operation state data; predict future load and future heat source intensity based on the long short-term memory network prediction model, to obtain a prediction result; wherein the long short-term memory network prediction model includes a forget gate, an output gate, a candidate cell state, a cell state update, an output gate, a hidden state and a prediction output; the expression of the forget gate is: ； the expression of the output gate is: ； the expression of the candidate cell state is: ； the expression of the cell state update is: ； the expression of the output gate is: ； the expression of the hidden state is: ； The expression of the predicted output is: ; represents the forecast value of the grid load and heat source intensity at the next time instant; where, is a sigmoid activation function, used to map the input to the interval [0, 1]; is a hyperbolic tangent activation function, used to map the input to the interval [-1, 1]; is a weight matrix, corresponding to the weights of the forget gate, input gate, candidate cell state, output gate, and predicted output, respectively; is a bias vector, corresponding to the biases of the forget gate, input gate, candidate cell state, output gate, and predicted output, respectively; is the hidden state at the previous time step, representing the historical information of the LSTM model; is the grid load at the current time step, with the unit of MW, representing the power demand that the system needs to respond to; is the external heat source intensity at the current time step, with the unit of kW / m², representing the available heat from solar radiation or industrial waste heat; is the CO2 storage tank pressure at the current time step, with the unit of MPa, representing the operating state of the CO2 storage subsystem; is the hot storage tank temperature at the current time step, with the unit of °C, representing the available heat from the heat storage subsystem; is the cell state at the previous and current time steps, storing the long-term memory of the LSTM model; is the candidate cell state, representing the potential update to the cell state from the current input; is the activation value of the forget gate, input gate, and output gate, controlling the information flow; is the hidden state at the current time step, representing the short-term memory of the LSTM model; is the predicted output, representing the predicted value at the next time step; is the predicted grid load at the next time step, with the unit of MW, used to guide the CO2 release and power generation scheduling; is the predicted heat source intensity at the next time step, with the unit of kW / m², used to optimize the heat distribution.

6. The method of claim 4, wherein, construct a target function based on a model predictive control algorithm and the prediction result, to dynamically optimize carbon dioxide flow and heat distribution, to obtain target data, specifically including: perform a heat source priority allocation strategy based on a priority rule, the priority rule being to preferentially utilize heat storage in a heat storage tank, the expression of heat being: ； wherein is the specific heat capacity of the heat storage medium; is the mass of the medium; ; when the heat storage tank is insufficient in heat, switch to an external heat source, and optimize carbon dioxide flow and heat source matching degree, to obtain the target data.

7. The method according to claim 6, wherein, the expression of the carbon dioxide flow and heat source matching degree is: ； is a carbon dioxide flow rate; is an available heat; is a target temperature; is a current carbon dioxide temperature; is a proportional coefficient.

8. The method of claim 4 to 7, wherein, the expression of the target function is: ； In the formula, is the power generation efficiency; is the energy loss; is the weight coefficient; is the heat source utilization rate; , is the weight coefficient; The constraints include: a pressure constraint, a temperature constraint, a turbine power constraint, a heat distribution constraint, and a flow constraint.

9. An electronic device, comprising: including: at least one processor; a memory in communication connection with the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the carbon dioxide energy storage and supercritical power generation coupling optimization control method of any one of claims 4 to 8.

10. A medium characterized by, store a computer program, the computer program being executed by a processor to implement the carbon dioxide energy storage and supercritical power generation coupling optimization control method of any one of claims 4 to 8.

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