Waste heat generation and sco2 energy storage collaborative scheduling platform based on energy level matching

Abstract

This invention discloses a waste heat generation and SCO2 energy storage collaborative scheduling platform based on energy level matching. Belonging to the field of waste heat recovery and SCO2 energy storage, it addresses the problems of poor energy matching between waste heat and energy storage systems, high reliance on manual intervention, and lack of economical scheduling in existing technologies. It achieves full-dimensional parameter acquisition and fusion through a multi-source sensing module, and constructs a dynamic energy flow scheduling system based on an intelligent decision-making central module and a production-storage tiered optimization module. This enables economical time-segmented scheduling, maintaining production load while reducing high-priced electricity consumption, balancing production and revenue. Combined with a tiered self-healing interlocking module, it achieves fault classification and self-healing control, enabling rapid fault response and self-healing. In the event of network interruption or central dispatch command failure, the edge control engine module activates a local governance mode to maintain normal platform operation, improving system stability and security, and reducing the need for manual intervention.

Description

Technical Field

[0001] This invention relates to the field of waste heat recovery and SCO2 energy storage, specifically to a waste heat generation and SCO2 energy storage collaborative scheduling platform based on energy level matching. Background Technology

[0002] The SCO2 energy storage system has attracted widespread attention due to its high round-trip efficiency and high energy density, and is considered a promising energy storage technology. Its basic principle is to use surplus electricity during off-peak hours to compress ambient-temperature, ambient-pressure carbon dioxide gas into a liquid, storing the heat energy generated during the compression process. During peak hours, the stored heat energy is used to heat the liquid carbon dioxide back to a gaseous state, driving a steam turbine to generate electricity.

[0003] The SCO2 energy storage system contains a large amount of recoverable compression heat and turbine exhaust waste heat. This waste heat can be transformed into strategically valuable resources. my country has extremely rich waste heat resources, but their utilization rate is seriously insufficient. This reflects both a huge energy waste and a significant potential for energy conservation and carbon reduction. The current measures taken address at least the following issues:

[0004] 1) Disconnection between generation and storage systems, resulting in poor energy coordination and matching: The low-temperature waste heat supply and SCO2 energy storage are usually set up with independent control systems, lacking a unified energy flow and material flow coordination and control mechanism. The low-temperature waste heat recovery grade is disconnected from the energy consumption requirements of the SCO2 energy storage system, resulting in a high direct discharge rate of waste heat in the high-temperature section. The CO2 compression process requires additional grid power consumption. Furthermore, when the industrial waste heat source load fluctuates, the CO2 energy storage charging and discharging modules cannot adapt synchronously, leading to fluctuations in heat exchange parameters on the heat source side and deviations in CO2 turbine power generation, which cannot meet the stable operation requirements of the integrated system.

[0005] 2) High dependence on human-machine interaction and lack of unmanned management and control system; the existing system relies on manual on-site inspection for parameter adjustment and operating condition judgment. Key operations such as reactor hot spot control, CO2 compressor load adjustment and energy storage charging and discharging switching all require manual intervention. The labor intensity of operators is high and it is impossible to achieve 24-hour unmanned operation. The cost of manual operation and maintenance is high.

[0006] 3) Lack of "peak-valley-waste heat-load" linkage economic dispatch: The existing system does not combine the grid peak-valley electricity price with the low-temperature waste heat production load and waste heat intensity for linkage dispatch. It lacks a quantitative model of waste heat and power generation revenue, and cannot balance production output, energy storage efficiency and economic benefits. The system investment recovery period is long. Summary of the Invention

[0007] To address the aforementioned issues, the purpose of this invention is to propose a waste heat generation and SCO2 energy storage collaborative scheduling platform based on energy level matching, thereby achieving collaborative control of the waste heat generation system and the SCO2 energy storage system, establishing a safe unmanned monitoring network to collect system parameters and operating conditions in real time at high frequency, and combining waste heat generation and energy storage data to achieve economical dynamic control of waste heat generation and SCO2 energy storage.

[0008] This was achieved through the following technical solutions:

[0009] A waste heat generation and SCO2 energy storage collaborative scheduling platform based on energy level matching includes:

[0010] Multi-source sensing module: Collects real-time multi-source data, performs validity assessment on the real-time multi-source data, and then performs weighted fusion to obtain stable fused data;

[0011] Intelligent Decision-Making Central Module: Based on stable fusion data, a comprehensive database is constructed to calculate the amount of waste heat generated in production and the energy demand of SCO2 energy storage in real time. The module makes scheduling decisions based on the comprehensive database and sends central scheduling instructions to other modules of the platform to dynamically allocate waste heat and SCO2 energy storage.

[0012] Edge control engine module: Based on the central dispatch instructions of the intelligent decision-making center, it coordinates and regulates the waste heat production system and the SCO2 energy storage system; when it receives the local governance instructions issued by the tiered self-healing interlocking module, it activates the local governance mode.

[0013] Production and storage cascade optimization module: Based on the comprehensive database, construct the energy flow cascade matching model between the waste heat production system and the SCO2 energy storage system, output energy allocation rules and optimization constraints and feed them back to the intelligent decision-making center module;

[0014] Economic Dispatch Module: Connects to the power grid dispatch platform and the production MES system, combines a comprehensive database, and uses an LSTM long short-term memory neural network to build a prediction model. Based on historical data, it predicts future production load, waste heat generation, and power grid price, and transmits the prediction results to the intelligent decision-making center module for economic time-sharing dispatch.

[0015] The tiered self-healing interlocking module is based on a comprehensive database to build a safety monitoring network, monitor core safety parameters in real time and continuously, assess the degree of fault impact, classify the degree of fault impact, and implement corresponding self-healing operations.

[0016] Power distribution hub module: Dynamically adjusts power supply priority based on photovoltaic irradiance data in the comprehensive database.

[0017] Optionally, the weighted fusion of multi-source data and the construction of a comprehensive database involve pre-setting real-time multi-source data into core process parameters and auxiliary parameters, assigning weights and fusion; among them, core process parameters include reactor internal temperature and pressure, CO2 inclined thermosphere tank pressure, turbine generator speed, and main channel CO2 circulation flow rate; auxiliary parameters include branch node flow parameters and dynamic dual-medium heat exchanger inlet and outlet temperatures; the comprehensive database includes stable fused data, grid electricity price data, photovoltaic irradiance data, ambient temperature and humidity data, and production batch plans.

[0018] Optionally, the waste heat production system includes a reactor, a heat exchanger, a waste heat emergency distribution valve group, and a circulating water pump; the SCO2 energy storage system includes a CO2 compressor unit, a storage tank, and a turbine generator set, which includes a three-stage reheat turbine and a waste heat replenishment turbine.

[0019] Optionally, the waste heat production system and the SCO2 energy storage system are coordinated and adjusted. Based on the waste heat production load, waste heat generation, and real-time multi-source data of the waste heat production system, the edge control engine module synchronously adjusts the CO2 compressor load, CO2 circulation flow, three-stage reheat turbine, and waste heat replenishment turbine of the SCO2 energy storage system to achieve dynamic coordination and matching between the waste heat production system and the SCO2 energy storage system, thereby improving the overall operating temperature and conversion efficiency of the platform.

[0020] Optionally, in the local governance mode, when network communication is interrupted or the central dispatch command fails, the tiered self-healing interlocking module issues a local governance command to the edge control engine module. The edge control engine module maintains the operation of the waste heat production system and the SCO2 energy storage system based on preset strategies and historical data. When the network is restored or the central dispatch command is restored, the edge control engine module switches from the local governance mode to the intelligent decision-making central module collaborative control mode, ensuring the stable operation and business continuity of the platform when network communication is interrupted or commands are abnormal.

[0021] Optionally, the energy flow is dynamically scheduled, and the intelligent decision-making central module calculates the amount of waste heat generated in real time and the energy demand of SCO2 energy storage, and dynamically allocates the waste heat. When the amount of waste heat generated is greater than the energy storage demand, the waste heat is stored in the CO2 thermocline storage tank and released when the demand increases. When the amount of waste heat generated is less than the energy storage demand, the grid / photovoltaic supplementary energy is activated. While ensuring the stable operation of the platform's waste heat production system and SCO2 energy storage system, waste heat waste is effectively avoided.

[0022] Optionally, time-of-use scheduling can be implemented, including: prioritizing the use of low-priced grid electricity during off-peak hours and fully activating waste heat exchange loops to store waste heat from production; prioritizing the release of SCO2 energy storage energy during peak hours to drive turbine generators to supply power to production, reducing grid power purchases, and simultaneously utilizing high-temperature waste heat to improve power generation efficiency; increasing energy storage charging when waste heat is sufficient during flat-peak hours, and purchasing electricity to supplement when waste heat is insufficient; through time-of-use scheduling, energy storage capacity can be maximized, maintaining basic production load while reducing high-priced electricity consumption, thus balancing production and revenue.

[0023] Optionally, the core safety parameters of the safety monitoring network include the temperature and pressure parameters inside the reactor, the concentration parameters of combustible gases in the environment, the internal pressure parameters of the CO2 thermocline tank, the vibration amplitude and temperature parameters of the CO2 compressor unit and turbine generator unit, the inlet and outlet flow parameters of the dynamic dual-medium heat exchanger unit, and the access voltage and real-time frequency parameters of the external power grid.

[0024] Optionally, the security monitoring network is divided into three levels based on the severity of the fault: emergency fault, general fault, and minor fault. At the same time, the security monitoring network is equipped with a self-healing mechanism, which is triggered according to the fault level. The self-healing mechanism includes: emergency shutdown, adjustment of flow input and ratio, and switching to backup sensors or backup equipment. After the fault is resolved, the load is gradually increased in stages.

[0025] Optionally, the power distribution hub module constructs a three-source power supply priority control mechanism based on photovoltaic irradiance, including: when daytime irradiance is ≥ the first irradiance threshold N1, photovoltaic power supply is used first; when irradiance is ≥ the second irradiance threshold N2 and ≤ the first irradiance threshold N1, the power difference is supplemented by grid power purchase while using photovoltaic power supply; when daytime irradiance is ≥ the second irradiance threshold N2, grid power supply is used; when waste heat from production is ≥ 200℃, waste heat is used to supplement the system's energy storage; while maintaining the basic production load, high-priced electricity consumption is reduced, balancing production and revenue.

[0026] The beneficial effects of this invention compared to the prior art are:

[0027] 1) Energy coordination and matching optimization, resulting in a double improvement in system energy efficiency and waste heat utilization: The technical solution of this invention constructs an energy flow cascade matching model between the waste heat production system and the SCO2 energy storage system, realizing energy coordination and matching between the waste heat production and SCO2 energy storage systems. Waste heat is utilized in a targeted manner according to the waste heat cascade, solving the dual problems of waste heat allocation and high energy consumption of energy storage. Compared with the existing waste heat recovery and energy storage systems that do not perform energy level matching and cascade utilization, the waste heat recovery utilization rate and the overall system energy efficiency are improved.

[0028] 2) Unmanned management and control, reducing both manual operation and maintenance costs and safety risks: Through a multi-dimensional perception, edge unmanned control, and autonomous fault self-healing system, an unmanned management and control system is built, eliminating reliance on manual operation, on-site inspection, and condition judgment, and achieving 24-hour unattended operation; through the unmanned management and control system, the safety risks of manual on-site operations are eliminated, and the integrated safety interlock and fault self-healing mechanism enables rapid response and self-healing of faults, avoiding safety accidents caused by untimely manual handling, avoiding safety hazards from the source, and ensuring the safe and stable operation of the system;

[0029] 3) Precise implementation of economic dispatch and reduced operating costs: The economic optimization dispatch module fully explores the economic value of peak and valley electricity prices. During off-peak hours, it utilizes low-priced electricity to increase energy storage, and during peak hours, it releases energy storage to generate electricity and connects excess electricity to the grid, further reducing dependence on the grid and saving electricity purchase costs. Attached Figure Description

[0030] Figure 1 An overall architecture diagram of a waste heat generation and SCO2 energy storage collaborative scheduling platform based on energy level matching is provided for an embodiment of the present invention;

[0031] Figure 2 The control logic and thermodynamic coupling topology of a waste heat generation and SCO2 energy storage collaborative scheduling platform based on energy level matching provided in this embodiment of the invention. Detailed Implementation

[0032] The technical solutions in the embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0033] like Figure 1 As shown, the self-healing control platform for waste heat production and SCO2 energy storage based on energy level matching in this embodiment has the following architecture: a multi-source sensing module, an intelligent decision-making central module, an edge control engine module, a production-storage cascade optimization module, an economic dispatch module, a tiered self-healing interlocking module, and a power distribution central module. Through comprehensive sensing of the operating environment, combined with grid electricity price data, photovoltaic irradiance data, and production batch plans, a collaborative system for waste heat production and SCO2 energy storage is constructed. This system intelligently dispatches energy flow and sets up interlocking mechanisms for faults, triggering self-healing when a fault occurs. The following provides a detailed description of each module:

[0034] Multi-source sensing module: A multi-modal sensor matrix is ​​deployed on key equipment in the waste heat production system and the SCO2 energy storage system to collect and output real-time multi-source data. Key equipment in the waste heat production system includes reactors, heat exchangers, waste heat emergency distribution valve groups, and circulating water pumps; key equipment in the SCO2 energy storage system includes CO2 compressor units, storage tanks, and turbine generator sets, including a three-stage reheat turbine and a waste heat supplementation turbine. The multi-modal sensor matrix includes temperature sensors, pressure sensors, flow sensors, and vibration sensors. The real-time multi-source data collected by the sensor matrix undergoes data validity assessment and preprocessing to eliminate environmental interference and equipment errors. Specifically, data validity is assessed as follows:

[0035] in, The result indicates the data validity; 1 represents valid data, and 0 represents invalid data. These are the actual measured parameter values ​​of the sensor; and These correspond to the upper and lower limits of the parameter design, respectively; Set the allowable deviation range for the system, such as ±2%.

[0036] The collected valid multi-source data undergoes preprocessing, including noise filtering, outlier removal, data completion, and normalization. Real-time multi-source data is then weighted and fused. This data is pre-defined as core process parameters and auxiliary parameters, assigned weights, and fused. Core process parameters include reactor internal temperature and pressure, CO2 thermocline tank pressure, turbine generator speed, and main CO2 circulation flow rate. Auxiliary parameters include branch node flow parameters and inlet / outlet temperatures of the dynamic dual-medium heat exchanger. Specifically, the weighted fusion of the multi-source data is as follows:

[0037] in, The parameter values ​​are weighted; n is the total amount of data source. The weight coefficients of the data sources collected by the sensor ranked i are 1, and the sum of all weight coefficients is 1. This refers to the measured data from the sensor corresponding to sequence number i. In terms of weight allocation strategy, for the same sensing node, core process parameters are assigned significantly higher weights than auxiliary parameters. This ensures that when calculating the final result, the more reliable and critical sensor yields the value closest to the actual operating state of the equipment. For example, the reactor temperature has a weight of 0.3, and the heat exchanger inlet and outlet temperatures have a weight of 0.08.

[0038] The intelligent decision-making central module acquires stable fused data from multi-source sensing modules, combines it with grid price data from the grid dispatch platform, photovoltaic irradiance data from distributed photovoltaic units, and production batch plans from the production MES system to construct a comprehensive database. It then calculates in real-time the amount of waste heat generated during production and the energy demand for SCO2 energy storage, sending central dispatch instructions to other modules on the platform for dynamic allocation of waste heat and SCO2 energy storage. It should be noted that the production MES system is a production management system responsible for monitoring, tracking, controlling, and optimizing the production process. Its core functions include production planning management, equipment management, quality management, and personnel management.

[0039] Specifically, the dynamic allocation of waste heat and SCO2 energy storage is reflected in the closed-loop regulation of the reactor hotspot temperature and the coordinated regulation of the CO2 compressor load. The closed-loop regulation algorithm for the reactor hotspot temperature is as follows:

[0040]

[0041] in, The value represents the opening degree (%) of the refrigerant flow regulating valve at time t, which is used to dynamically adjust the refrigerant supply. This is the PID proportional coefficient. The integral coefficient is... These are the differential coefficients; , as well as These are all control parameters pre-stored in the waste heat production system; The target temperature of the reactor hot spot (°C); The measured temperature of the reactor hot spot.

[0042] The CO2 compressor load coordination adjustment algorithm is as follows:

[0043]

[0044] in, Target load (kW) for CO2 compressor; To generate real-time load (kW); The synergy coefficient between the waste heat production system and the SCO2 energy storage system characterizes the degree of energy matching between them. Its value is obtained through fitting historical operating data or experimental calibration. When waste heat supply matches energy storage demand... The value is set to 1; when there are large fluctuations in waste heat or changes in energy storage load, Adjust within the range of 0.8 to 1.2 to compensate for system coupling deviation and improve scheduling stability; To produce real-time waste heat (kJ / h); To produce rated waste heat (kJ / h).

[0045] Edge control engine module: Receives scheduling from the intelligent decision-making central module and adopts a closed-loop regulation architecture combining edge control and local autonomy. Based on the waste heat production load, waste heat generation, and real-time multi-source data of the waste heat production system, the edge control engine module synchronously regulates the CO2 compressor load, CO2 circulation flow, and turbine power generation of the SCO2 energy storage system. Upon receiving a local governance command from the tiered self-healing interlocking module, it activates the local governance mode, maintaining the operation of the waste heat production system and the SCO2 energy storage system based on preset strategies and historical data. After network recovery, it synchronously switches to the intelligent decision-making central module control mode. Specifically, the preset strategies include: Reactor temperature and pressure maintenance strategy: calling the last historical target value before the network outage, only performing local passive fine-tuning to maintain stable operating conditions; CO2 compressor load regulation strategy: cutting off external collaborative scheduling, and scheduling the CO2 compressor production load based on local waste heat fluctuations; Heat exchanger heat exchange regulation strategy: controlling the heat exchanger valve opening to the opening before the network outage, and preventing medium overheating by adjusting the bypass pipeline flow and effective heat exchange area.

[0046] The production-storage cascade optimization module, based on the principles of temperature matching, energy cascade, and targeted utilization, and relying on the comprehensive database of the intelligent decision-making center module, constructs an energy flow cascade matching model between the waste heat production system and the SCO2 energy storage system. It outputs energy allocation rules and optimization constraints and feeds them back to the intelligent decision-making center module. Specifically, the energy allocation rules include a targeted thermodynamic mapping relationship based on waste heat levels, clarifying the optimal utilization path for waste heat at different temperature levels (e.g., high-temperature waste heat for turbine reheat, medium-temperature waste heat for compression heating, and low-temperature waste heat for insulation and heating), as well as the optimal charging and discharging power range of the energy storage system. The optimization constraints include upper limit constraints on equipment operation, waste heat temperature range constraints, and energy storage capacity constraints.

[0047] The intelligent decision-making central module calculates the waste heat generation and the energy demand of SCO2 energy storage in real time to obtain the waste heat cascade utilization matching degree. Based on the optimization results, it controls the valve opening and effective heat exchange area of ​​the waste heat converter in the waste heat production system to achieve dynamic waste heat distribution. When the waste heat generation is less than the energy storage demand, while meeting the optimization constraints, it activates the grid or photovoltaic power generation to supplement energy, ensuring the stable operation of the SCO2 energy storage system. The waste heat cascade utilization matching degree calculation scheme is as follows:

[0048] in, The matching degree of waste heat utilization is used to characterize the matching degree between waste heat and energy storage. For the first Actual utilization of stage waste heat (KJ / h); For the SCO2 energy storage system in the first Target energy storage heat demand (KJ / h) within the temperature range of the level. These correspond to three levels: high-temperature waste heat, medium-temperature waste heat, and low-temperature waste heat, respectively. For the first The heat exchange efficiency (%) of the waste heat is as follows: high temperature ≥95%, medium temperature ≥92%, and low temperature ≥85%.

[0049] Based on the energy flow cascade matching model, the overall system energy efficiency is significantly improved. The calculation method is as follows:

[0050]

[0051] in, The effective output power (kW) of the production; The total power output (kW) of the SCO2 energy storage system through turbine power generation; This represents the real-time electrical power (kW) that the system purchases from the external power grid. Total waste heat generated (kW).

[0052] Economic Dispatch Module: Connects to the power grid dispatch platform and the production MES system to obtain real-time peak and valley electricity prices, production batches, and load plans for the working environment. Combined with stable fusion data output from the multi-source sensing module, a prediction model is constructed using an LSTM long short-term memory neural network. Based on historical data, the model predicts the production load, waste heat generation, and power grid price for the next 24 hours. The prediction results are transmitted to the intelligent decision-making center module for economic time-sharing dispatch. The dispatch includes scheduling the waste heat production load of the waste heat production system and the charging and discharging status of the SCO2 energy storage.

[0053] Specifically, during off-peak electricity price periods in the platform's region, priority is given to utilizing low-priced grid electricity, and the waste heat exchange loop is fully activated to store production waste heat, maximizing energy storage capacity and maintaining basic production load while reducing high-priced electricity consumption. During peak electricity price periods, priority is given to releasing SCO2 energy storage energy to drive turbine power generation to supply electricity to production, reducing grid power purchases, while fully utilizing high-temperature waste heat to improve power generation efficiency. During flat electricity price periods, energy storage is dynamically adjusted based on real-time waste heat generation; if waste heat is sufficient, energy storage charging is increased; if waste heat is insufficient, electricity is purchased to supplement it. Through time-of-use scheduling, energy storage capacity can be maximized, maintaining basic production load while reducing high-priced electricity consumption, thus balancing production and revenue. To quantify the economic benefits of time-of-use scheduling, the system calculates daily net revenue using the following formula:

[0054]

[0055] in, The system's daily net profit (yuan / day); Revenue from electricity generated for self-use (yuan / day). , This refers to the self-consumption power generation capacity (kW). The flat real-time electricity price is (yuan / kWh); Revenue from grid connection of surplus electricity (yuan / day). , Grid-connected power generation (kW). Peak electricity price (RMB / kWh); The daily electricity purchase cost (yuan / day). , The real-time electricity price is (RMB / kWh). Daily maintenance cost (RMB / day). Let represent the power (kW) purchased from the grid at time t. To further optimize energy storage efficiency, the system determines the optimal energy storage time using the following formula, which guides the charging scheduling of the SCO2 energy storage system. This schedules energy storage operations during periods of abundant waste heat or low electricity prices, aligning the energy storage process with the supply of waste heat from production and the peak-valley electricity price of the grid, thereby improving energy utilization efficiency and optimizing system economics:

[0056] in, λ(t) represents the optimal energy storage time; λ(t) represents the real-time electricity price (yuan / kWh). Target energy storage demand (kWh); SCO2 energy storage efficiency (%) is used to characterize the ratio between input energy and available release energy during energy storage. Its value changes, affecting the operational benefits of the energy storage system at different times. In the scheduling optimization process, energy storage efficiency serves as a key parameter in the decision-making model. By comparing the energy storage benefits or costs at different times, the optimal energy storage period is determined, prioritizing energy storage operations to match electricity price changes. In this embodiment, 85% was selected as the preferred value to reflect the energy conversion level of the SCO2 compressed energy storage system under optimal operating conditions.

[0057] The tiered self-healing interlocking module, based on the comprehensive database of the intelligent decision-making central module, constructs a safety monitoring network to continuously monitor core safety parameters in real time, assess the impact of faults, classify the impact of faults, and implement corresponding self-healing operations. In the event of network communication interruption or central dispatch command failure, the tiered self-healing interlocking module issues local governance commands to the edge control engine module. Core safety parameters include the temperature and pressure parameters inside the reactor, the concentration of combustible gases in the environment, the internal pressure parameters of the CO2 thermocline tank, the vibration amplitude and temperature parameters of the CO2 compressor unit and turbine generator unit, the inlet and outlet flow parameters of the dynamic dual-medium heat exchanger unit, and the access voltage and real-time frequency parameters of the external power grid. The core safety parameters monitored by the safety monitoring network are compared with preset thresholds to assess the impact of faults, which are then applied to the reactor, CO2 thermocline tank, CO2 compressor unit, and turbine generator unit. Fault handling is achieved through interlocking control valves, shutdown devices, and backup equipment. Specifically, the assessment calculation method is as follows:

[0058]

[0059] in, The degree of impact of the fault; For the preset weighting coefficients, satisfy ; For reactor temperature deviation, The rated temperature difference for the reactor; This refers to the CO2 turbine pressure difference, which is the pressure difference between the inlet and outlet of the CO2 circulating working medium in the turbine generator set. Rated power for CO2 turbine pressure; The concentration deviation is due to the leakage of toxic media. The threshold for leakage of toxic media; For equipment vibration acceleration deviation, This is the equipment vibration threshold.

[0060] Fault classification is divided into 3 levels based on the severity of the fault. The fault is an emergency fault. The fault is a general fault. Minor faults include: large leaks, reactor overheating and overpressure, CO2 storage tank overpressure and leakage, and severe equipment vibration; general faults include reactor temperature fluctuations, CO2 turbine power deviations, waste heat exchanger efficiency reduction, and single sensor failure; minor faults include minor leaks in auxiliary equipment, occasional data transmission interruptions, and sensor drift.

[0061] The safety monitoring network is equipped with a self-healing mechanism that dynamically allocates waste heat resources linearly based on the severity of the fault. This includes: lower fault impacts result in smaller emergency allocations, maximizing the efficiency of waste heat utilization; in the event of an emergency fault, an emergency shutdown procedure is immediately triggered, cutting off raw material and energy input across systems, closing valves, and activating leak handling / firefighting / nitrogen protection systems; in the event of a general fault, the self-healing mechanism is automatically triggered, adjusting flow input and ratio; in the event of a minor fault, backup sensors or backup equipment are switched; and the load is gradually increased in stages after the fault is cleared. The basis for adjusting flow input and ratio in the self-healing mechanism is as follows:

[0062]

[0063] Waste heat emergency allocation (kJ / h); The total residual heat before the fault (kJ / h); Efficiency of emergency heat exchanger (%); To correspond to the degree of impact of the fault; The threshold value for the degree of fault impact that triggers the emergency allocation action is preset; in this embodiment, it is set to 0.5. In this embodiment, the linear adjustment range for emergency allocation actions is set to 0.3.

[0064] Distribution hub module: Dynamically adjusts power supply priority based on photovoltaic irradiance data in the comprehensive database of the intelligent decision hub module.

[0065] The power distribution hub module constructs a three-source power supply priority control mechanism based on photovoltaic irradiance, including: when daytime irradiance is ≥ the first irradiance threshold N1, photovoltaic power supply is used with priority; when irradiance is ≥ the second irradiance threshold N2 and ≤ the first irradiance threshold N1, the power difference is supplemented by grid power purchase while using photovoltaic power supply; when nighttime or daytime irradiance is ≤ the second irradiance threshold N2, grid power supply is used; when waste heat from production is ≥ 200℃, waste heat is used to supplement energy storage.

[0066] The power distribution center module features a seamless switching mechanism for its three power sources, dynamically adjusting the power from photovoltaic, grid, and waste heat replenishment. Specifically, the power allocation method is as follows:

[0067]

[0068] in, Let t be the total load power of the system at time t (kW). The real-time power supplied by the power grid at time t. Let be the real-time power generation of the photovoltaic panel at time t. This represents the real-time power generation of the waste heat turbine generator set. , and This refers to the power supply priority coefficient. Hierarchical control is implemented based on grid conditions and photovoltaic irradiance. Specifically, the power supply priority coefficient is controlled hierarchically based on grid conditions and photovoltaic irradiance.

[0069] When the power grid is in normal supply condition, power allocation is based primarily on photovoltaic irradiance. When daytime irradiance is ≥ the first irradiance threshold N1, photovoltaic power supply is prioritized. , , When the second irradiance threshold N2 ≤ irradiance ≤ first irradiance threshold N1, the proportion of electricity purchased from the grid should be increased while using photovoltaic power. In this case, take... , , When the daytime or nighttime irradiance is ≤ the second irradiance threshold N2, take , , .

[0070] When the power grid is unavailable, waste heat and photovoltaic power will be used as the main energy sources. This enables dynamic power supply adjustment under different operating conditions.

[0071] The power distribution hub module also includes: a built-in fast switch and voltage stabilization module in the switching control cabinet, which matches the voltage and frequency before switching the power supply during switching; in the event of a power grid failure, the waste heat replenishment and off-grid modes are activated within 0.3 seconds, and the waste heat emergency distribution valve group works in conjunction with the waste heat replenishment turbine to ensure the power supply to the reactor, CO2 thermocline storage tank and heat exchanger, and smoothly switches back to the original mode after the power grid is restored.

[0072] also, Figure 2 The control logic and thermodynamic coupling topology diagram of this invention illustrates the system's control architecture and the deep coupling and synergy between digital command flow and physical energy flow. Specifically, it includes: the production full parameter sensing node 1 and SCO2 energy storage sensing node 2 collect the real-time operating status of the physical execution equipment group and transmit it to the multi-source data fusion gateway 3. After validity judgment and weighted processing, it is uploaded to the intelligent decision-making central module 4. The physical execution equipment group includes: circulating water pump 11, waste heat emergency distribution valve group 12, dynamic dual-medium heat exchanger unit 13, CO2 compressor unit 14, and CO2 inclined temperature layer storage tank 15.

[0073] The intelligent decision-making central module 4 interacts bidirectionally with the production and storage cascade optimization module 5, the economic dispatch module 6, and the tiered self-healing interlocking module 7 to generate central dispatch instructions. These instructions are then sent to the edge control engine module, which includes an intelligent collaborative control cabinet 8 and an edge control terminal 9. The edge control terminal 9 performs parallel control of the physical execution device group, enabling collaborative control of the waste heat production system and the SCO2 energy storage system. The economic dispatch module 6 connects to the power grid dispatch platform, distributed photovoltaic units, and the production MES system. It acquires real-time time-of-use electricity price data from the external power grid, irradiance status and power generation forecast data of the photovoltaic units, as well as the plant's production schedule and load fluctuation data. It performs economic calculations on the acquired data, outputs an economic dispatch strategy that minimizes system operating costs or maximizes power generation revenue, and feeds it back to the intelligent decision-making central module 4. The intelligent decision-making central module 4 then sends instructions to the three-source power supply switching module 10 in the power distribution central module to achieve dynamic switching of the power supply link.

[0074] Specifically, during the waste heat production process, the circulating water pump 11 is connected to the heat source inlet of the waste heat emergency distribution valve group 12, and the normally open outlet end of the waste heat emergency distribution valve group 12 is connected to the hot side inlet of the dynamic dual-medium heat exchanger unit 13, forming the main channel for transporting waste heat medium. During the SCO2 energy storage process, the pressurized outlet end of the CO2 compressor unit 14 is connected to the cold side inlet of the dynamic dual-medium heat exchanger unit 13, and the heated outlet end of the dynamic dual-medium heat exchanger unit 13 is connected to the CO2 inclined temperature layer storage tank 15 to store the high-temperature and high-pressure SCO2 after absorbing waste heat with high fidelity. The release end of the CO2 inclined temperature layer storage tank 15 is split into two paths and connected to the air inlets of the three-stage reheat turbine 16 and the waste heat replenishment turbine 17 respectively to release energy. After the exhaust ends of the three-stage reheat turbine 16 and the waste heat replenishment turbine 17 merge, they flow back to the inlet end of the CO2 compressor unit 14, thus forming a complete physical closed loop of SCO2 energy storage and release.

[0075] The platform also deploys a tiered self-healing interlocking module 7, which is bidirectionally connected to the intelligent decision-making central module 4, for conducting security assessments of the platform and implementing a graded response self-healing protection strategy based on the severity level of the fault. Specifically, the tiered self-healing interlocking module 7 extracts stable fusion data from the intelligent decision-making central module 4, performs real-time judgment of over-temperature, over-pressure, and system coupling anomalies, and sends the safety assessment results back to the intelligent decision-making central module 4 to dynamically correct the central dispatch instructions. At the same time, it triggers tiered self-healing actions for different risk levels: when network communication is interrupted or the central dispatch instructions of the central dispatch module fail, the tiered self-healing interlocking module 7 issues local governance instructions to the edge control engine module. The edge control engine module then cuts off its dependence on cloud instructions, takes over the real-time dispatch authority of the underlying layer, and independently completes the autonomous collaborative control of the physical execution device group based on preset local security policies and historical operating parameters to ensure the stable operation of the system in a degraded state. When a serious over-limit fault that endangers the underlying physical equipment (such as pipeline rupture or severe over-temperature of heat exchanger) is judged to occur, the waste heat emergency distribution valve group 12 is directly driven to act, instantly cutting off the heat source entering the dynamic dual-medium heat exchanger unit 13 and guiding it to bypass energy dissipation, achieving millisecond-level physical isolation, thereby building a highly reliable underlying safety defense line in the platform.

[0076] In summary, this invention addresses the problems of poor energy matching, high reliance on manual intervention, and lack of economic scheduling in existing technologies by constructing a waste heat generation and SCO2 energy storage collaborative scheduling platform based on energy level matching. It achieves real-time acquisition and fusion of multi-source parameters through a multi-source sensing module, constructs a dynamic energy flow scheduling system based on an intelligent decision-making central module and a production-storage tiered optimization module, and utilizes an economic scheduling module and a power distribution central module for economical time-sharing scheduling to maximize energy storage capacity. This balances production and revenue by maintaining basic production load while reducing high-priced electricity consumption. Furthermore, it incorporates a tiered self-healing interlocking module to achieve fault classification and self-healing control, enabling rapid fault response and self-healing, effectively improving system stability and safety, and reducing the need for manual intervention.

[0077] The above embodiments are merely illustrative of 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 to the technical solutions based on the technical concept proposed in this invention shall fall within the scope of protection of this invention.

Claims

1. A waste heat generation and SCO2 energy storage collaborative scheduling platform based on energy level matching, characterized in that, include: Multi-source sensing module: Collects real-time multi-source data, performs validity assessment on the real-time multi-source data, and then performs weighted fusion to obtain stable fused data; Intelligent Decision-Making Central Module: Based on stable fusion data, a comprehensive database is constructed to calculate the amount of waste heat generated in production and the energy demand of SCO2 energy storage in real time. The module makes scheduling decisions based on the comprehensive database and sends central scheduling instructions to other modules of the platform to dynamically allocate waste heat and SCO2 energy storage. Edge control engine module: Based on the central dispatch instructions of the intelligent decision-making center, it coordinates and regulates the waste heat production system and the SCO2 energy storage system; when it receives the local governance instructions issued by the tiered self-healing interlocking module, it activates the local governance mode. Production and storage cascade optimization module: Based on the comprehensive database, construct the energy flow cascade matching model between the waste heat production system and the SCO2 energy storage system, output energy allocation rules and optimization constraints and feed them back to the intelligent decision-making center module; Economic Dispatch Module: Connects to the power grid dispatch platform and the production MES system, combines a comprehensive database, and uses an LSTM long short-term memory neural network to build a prediction model. Based on historical data, it predicts future production load, waste heat generation, and power grid price, and transmits the prediction results to the intelligent decision-making center module for economic time-sharing dispatch. The tiered self-healing interlocking module is based on a comprehensive database to build a safety monitoring network, monitor core safety parameters in real time and continuously, assess the degree of fault impact, classify the degree of fault impact, and implement corresponding self-healing operations. Power distribution hub module: Dynamically adjusts power supply priority based on photovoltaic irradiance data in the comprehensive database.

2. The waste heat generation and SCO2 energy storage collaborative scheduling platform based on energy level matching according to claim 1, characterized in that, The weighted fusion of multi-source data and the construction of a comprehensive database include: real-time multi-source data are pre-defined into core process parameters and auxiliary parameters, weighted, and fused; among them, core process parameters include reactor internal temperature and pressure, CO2 inclined thermosphere tank pressure, turbine generator speed, and main channel CO2 circulation flow rate; auxiliary parameters include branch node flow parameters and dynamic dual-medium heat exchanger inlet and outlet temperatures; the comprehensive database includes stable fused data, grid electricity price data, photovoltaic irradiance data, environmental temperature and humidity data, and production batch plans.

3. The waste heat generation and SCO2 energy storage collaborative scheduling platform based on energy level matching according to claim 1, characterized in that, The waste heat production system includes a reactor, heat exchanger, waste heat emergency distribution valve group and circulating water pump; the SCO2 energy storage system includes a CO2 compressor unit, storage tank and turbine generator set, which includes a three-stage reheat turbine and a waste heat supplementation turbine.

4. The waste heat generation and SCO2 energy storage collaborative scheduling platform based on energy level matching according to claim 1, characterized in that, The coordinated regulation of the waste heat production system and the SCO2 energy storage system includes: based on the waste heat production load, waste heat generation, and real-time multi-source data of the waste heat production system, the edge control engine module synchronously regulates the CO2 compressor load, CO2 circulation flow, three-stage reheat turbine, and waste heat replenishment turbine of the SCO2 energy storage system.

5. The waste heat generation and SCO2 energy storage collaborative scheduling platform based on energy level matching according to claim 1, characterized in that, The local governance mode includes the following: when network communication is interrupted or the central dispatch command fails, the tiered self-healing interlocking module issues a local governance command to the edge control engine module. The edge control engine module maintains the operation of the waste heat production system and the SCO2 energy storage system based on preset strategies and historical data. When the network is restored or the central dispatch command is restored, the edge control engine module switches from the local governance mode to the intelligent decision-making central module collaborative control mode.

6. The waste heat generation and SCO2 energy storage collaborative scheduling platform based on energy level matching according to claim 1, characterized in that, Dynamic energy flow scheduling includes a smart decision-making central module that calculates the amount of waste heat generated in real time and the energy demand for SCO2 energy storage, and dynamically allocates the waste heat. When the amount of waste heat generated is greater than the energy storage demand, the waste heat is stored in a CO2 thermocline storage tank and released when the demand increases. When the amount of waste heat generated is less than the energy storage demand, grid / photovoltaic supplementary energy is activated.

7. The waste heat generation and SCO2 energy storage coordinated scheduling platform based on energy level matching according to claim 1, characterized in that, Economic time-of-use dispatching includes: during off-peak electricity price periods, priority is given to utilizing low-priced grid electricity and fully opening waste heat exchange circuits to store production waste heat; during peak electricity price periods, priority is given to releasing SCO2 energy storage energy to drive turbine generator sets to supply power for production, reducing grid power purchases, while utilizing high-temperature waste heat to improve power generation efficiency; during flat electricity price periods, when waste heat is sufficient, energy storage charging is increased, and when waste heat is insufficient, electricity is purchased to supplement it.

8. The waste heat generation and SCO2 energy storage collaborative scheduling platform based on energy level matching according to claim 1, characterized in that, The core safety parameters include the temperature and pressure parameters inside the reactor, the concentration parameters of combustible gases in the environment, the internal pressure parameters of the CO2 thermocline tank, the vibration amplitude and temperature parameters of the CO2 compressor unit and turbine generator unit, the inlet and outlet flow parameters of the dynamic dual-medium heat exchanger unit, and the access voltage and real-time frequency parameters of the external power grid.

9. The waste heat generation and SCO2 energy storage coordinated scheduling platform based on energy level matching according to claim 1, characterized in that, The safety monitoring network is divided into three levels based on the severity of the fault: emergency fault, general fault, and minor fault. At the same time, the safety monitoring network is equipped with a self-healing mechanism, which is triggered according to the fault level. The self-healing mechanism includes: emergency shutdown, adjustment of traffic input and allocation, and switching to backup sensors or backup equipment. After the fault is resolved, the load is gradually increased in stages.

10. A waste heat generation and SCO2 energy storage collaborative scheduling platform based on energy level matching according to claim 1, characterized in that, The power distribution hub module constructs a three-source power supply priority control mechanism based on photovoltaic irradiance, including: when daytime irradiance is ≥ the first irradiance threshold N1, photovoltaic power supply is used first; when irradiance is ≥ the second irradiance threshold N2 and ≤ the first irradiance threshold N1, the power difference is supplemented by grid power purchase while using photovoltaic power supply; when the daytime irradiance is ≥ the second irradiance threshold N2, grid power supply is used; when the waste heat from production is ≥ 200℃, the waste heat is used to supplement the system's energy storage.

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