Solar photo-thermal power generation and supply system

Abstract

The invention belongs to the technical field of solar power generation, and particularly relates to a solar photo-thermal power generation and supply system, which comprises a self-adaptive condensation tracking module for outputting stable high-temperature heat flow through multi-stage reflector array cooperative tracking and focus decentralized control; the layered cascade heat storage module receives the high-temperature heat flow and stores the high-temperature heat flow to different areas according to temperature grades; the multi-source cooperative power generation module receives heat energy and cooperatively generates power through multiple systems; the intelligent load prediction scheduling module generates a load prediction result and formulates a power generation scheduling plan; the system self-optimization evolution module monitors operation parameters, parameter self-adaption adjustment and strategy improvement are achieved, the solar energy utilization efficiency is remarkably improved, and the direct solar energy utilization rate is increased to 25% from 20%.

Description

Technical Field

[0001] The present invention belongs to the technical field of solar power generation, and particularly relates to a solar thermal power generation and power supply system based on intelligent thermal energy coordinated control. The system realizes the efficient collection, storage, and power generation of solar energy through the organic combination of technologies such as adaptive light concentration tracking, hierarchical cascade thermal energy storage, multi-source collaborative power generation, intelligent load prediction and scheduling, and system self-optimization and evolution. Background Art

[0002] With the advancement of the global energy structure transformation goal, solar thermal power generation, as an important clean energy technology, has received extensive attention. The traditional solar thermal power generation system mainly has the following technical limitations: First, the light concentration tracking accuracy is limited. Usually, fixed or simple two-axis tracking is adopted, and the solar positioning accuracy is only ±0.5°, resulting in low light concentration efficiency; Second, the thermal energy storage system mostly uses a single temperature level and cannot make full use of thermal energy of different qualities, with low thermal energy utilization efficiency; Third, the power generation method is relatively single, lacking a multi-source collaborative mechanism, and serious waste of waste heat resources; In addition, the system operation mostly relies on manual experience, lacking intelligent load prediction and scheduling optimization, and it is difficult to adapt to the rapid changes in grid demand; Finally, the system operation parameters are fixed, lacking self-learning and self-optimization capabilities, and cannot adapt to environmental changes and equipment aging during long-term operation.

[0003] In the prior art, a Chinese invention patent with the patent number 202510096387.3 discloses a solar thermal power generation system and its operation method, but its light concentration tracking accuracy is limited and the thermal energy storage temperature is single. There is still much room for improvement in terms of light concentration accuracy, thermal energy storage efficiency, power generation stability, and system intelligence level for these technical solutions. Summary of the Invention

[0004] The purpose of the present invention is to overcome the deficiencies of the prior art and provide a solar thermal power generation and power supply system based on intelligent thermal energy coordinated control, which realizes the efficient utilization and stable power generation of solar energy through the coordinated cooperation of five core innovative modules.

[0005] The present invention proposes a solar thermal power generation and power supply system, including:

[0006] An adaptive light concentration tracking module, which is used to control the multi-stage mirror array for coordinated tracking according to the solar position prediction data and cloud dynamic perception data, and adjust the light concentration heat flux density distribution through a focus dispersion control mechanism, and output a stable high-temperature heat flux to the subsequent module;

[0007] The hierarchical cascade thermal energy storage module is thermally connected to the adaptive concentrating and tracking module, and is used to receive the high-temperature heat flow and store thermal energy in the high-temperature thermal energy storage area, the medium-temperature thermal energy storage area, and the low-temperature preheating area respectively based on the temperature level. The working temperature of the high-temperature thermal energy storage area is 800 - 1000 °C, the working temperature of the medium-temperature thermal energy storage area is 400 - 600 °C, and the working temperature of the low-temperature preheating area is 150 - 300 °C;

[0008] The multi-source collaborative power generation module is thermally connected to the hierarchical cascade thermal energy storage module, and is used to receive the thermal energy from the high-temperature thermal energy storage area, the medium-temperature thermal energy storage area, and the low-temperature preheating area, and perform collaborative power generation through the main steam turbine power generation system, the organic Rankine cycle power generation system, and the biomass supplementary combustion power generation system to output stable power;

[0009] The intelligent load prediction and scheduling module is electrically connected to the multi-source collaborative power generation module, and is used to collect multi-dimensional data and generate a load prediction result, formulate a power generation scheduling plan based on the load prediction result, and send a scheduling instruction to the multi-source collaborative power generation module;

[0010] The system self-optimization and evolution module is respectively data-connected to the adaptive concentrating and tracking module, the hierarchical cascade thermal energy storage module, the multi-source collaborative power generation module, and the intelligent load prediction and scheduling module, and is used to monitor the system operation parameters, generate parameter optimization instructions based on the performance evaluation result, and realize the adaptive adjustment of system parameters and the continuous improvement of operation strategies.

[0011] Preferably, the adaptive concentrating and tracking module includes:

[0012] The solar position prediction unit is used to calculate the solar altitude angle and azimuth angle based on the geographical coordinates and time parameters, and perform atmospheric refraction compensation according to the atmospheric pressure, temperature, and humidity parameters, and output solar position data with an accuracy of ±0.1°;

[0013] The cloud dynamic perception unit is used to scan the cloud state within a radius of 50 kilometers through a multi-spectral detection device, identify the cloud thickness, moving speed, and shielding probability, and predict the future 30-minute solar radiation change trend;

[0014] The multi-stage mirror collaborative control unit is used to receive the solar position data and cloud prediction data, control the main mirror array with a mirror area of 25 square meters for two-axis precise tracking, and concentrate the solar radiation by more than 1000 times through the secondary concentrator.

[0015] Preferably, the adaptive concentrating and tracking module further includes:

[0016] The focus dispersion control unit is used to monitor the heat flux density distribution on the surface of the heat absorber through 100 temperature sensors. When the detected local temperature gradient exceeds 50 °C / cm, it automatically adjusts the reflection angle within 10 seconds to disperse the focus to the surrounding area, ensuring uniform distribution of the heat flux density and the focus position adjustment accuracy reaching ±1 cm.

[0017] Preferably, the hierarchical cascade thermal energy storage module includes:

[0018] A three-stage temperature stratified thermal energy storage unit, where the high-temperature thermal energy storage area uses a mixture of molten sodium nitrate and potassium nitrate as the thermal energy storage medium and the thermal energy storage capacity meets the demand for 8-hour full-load power generation. The medium-temperature thermal energy storage area uses a mixture of diphenyl-diphenyl ether heat transfer oil as the thermal energy storage medium and the thermal energy storage capacity meets the demand for 4-hour medium-load power generation. The low-temperature preheating area uses a pressurized water vapor system for sensible heat and latent heat storage;

[0019] The phase change thermal energy storage enhancement unit is used to configure a carbonate eutectic phase change material with a melting point of 760 °C in the high-temperature thermal energy storage area and a nitrate hydrate phase change material with a melting point of 320 °C in the medium-temperature thermal energy storage area, and enhance the heat transfer efficiency through finned tube bundles and a graphite heat conduction network.

[0020] Preferably, the hierarchical cascade thermal energy storage module further includes:

[0021] The intelligent thermal energy scheduling unit is used to achieve the thermal energy balance scheduling of each temperature level based on the load prediction data and solar energy collection prediction data, according to the thermal energy storage priority of preferentially storing heat in the high-temperature thermal energy storage area and storing heat step by step downward, and the heat release priority of preferentially using the thermal energy in the high-temperature thermal energy storage area to ensure the highest power generation efficiency;

[0022] The heat loss minimization unit is used to automatically adjust the thickness of the thermal insulation layer according to the ambient temperature, control the thermal conductivity of the key pipelines below 0.01 W / m·K through a vacuum interlayer structure, and collect waste heat resources such as the exhaust heat of the steam turbine and the heat dissipation of equipment and input them into the corresponding thermal energy storage areas according to the temperature levels.

[0023] Preferably, the multi-source collaborative power generation module includes:

[0024] The main steam turbine power generation unit is used to receive the high-temperature molten salt from the high-temperature thermal energy storage area, generate main steam with a pressure of 10 MPa and a temperature of 540 °C through a spiral coil heat exchanger, drive a condensing steam turbine with a rated power of 50 MW to generate electricity, and achieve a thermoelectric conversion efficiency ≥42%;

[0025] The organic Rankine cycle power generation unit is used to utilize the medium-low temperature heat sources from the medium-temperature thermal energy storage area and the low-temperature preheating area, and drive an organic Rankine cycle system with a rated power of 5 MW to generate electricity through the R245fa working fluid;

[0026] The biomass co-firing power generation unit is used to provide a supplementary heat source with a thermal power of 10 MW when solar energy is insufficient. It burns agricultural and forestry waste through fluidized bed combustion, with a combustion efficiency > 95% and soot emissions < 30 mg / m³.

[0027] Preferably, the multi-source collaborative power generation module further includes:

[0028] The multi-pressure level steam coordination unit is used to establish a high-pressure steam circuit, a medium-pressure steam circuit, and a low-pressure steam circuit. Among them, the parameters of the high-pressure steam circuit are 10 MPa and 540 °C for main steam turbine power generation, the parameters of the medium-pressure steam circuit are 2.5 MPa and 350 °C for process steam, and the parameters of the low-pressure steam circuit are 0.5 MPa and 150 °C for heating and deaeration;

[0029] The intelligent switching control unit is used to manage the heat source priority strategy with solar thermal power generation as the first priority, waste heat power generation as the second priority, and biomass co-firing as the third priority. Through power prediction and smoothing algorithms, it ensures the frequency and voltage stability of the output power.

[0030] Preferably, the intelligent load prediction and dispatching module includes:

[0031] The multi-dimensional data acquisition unit is used to collect solar radiation intensity, cloud cover, wind speed and direction, temperature and humidity data, as well as real-time power, voltage, and current data of each substation in the power grid, and identify the electricity consumption patterns of industrial users, commercial users, and residential users, as well as adjustable load characteristics such as air-conditioning load and electric vehicle charging load;

[0032] The load prediction algorithm unit is used to generate short-term load prediction results for 1 to 24 hours and medium- and long-term load prediction results for 1 to 7 days based on the multi-dimensional data through a multi-factor analysis method considering weather factors, historical load patterns, and holiday impacts. Among them, the daily load prediction error is controlled within ±3%.

[0033] Preferably, the intelligent load prediction and dispatching module further includes:

[0034] The power generation dispatching optimization unit is used to generate a power generation dispatching plan every 15 minutes based on the short-term load prediction results and the medium- and long-term load prediction results, with a multi-objective optimization strategy of minimizing the economic objective of power generation cost, ensuring the reliability objective of power supply reliability, and giving priority to using clean energy for environmental protection;

[0035] The emergency response processing unit is used to identify abnormal situations such as sudden cloud cover, equipment failure, load mutation, and fuel shortage, start a preset emergency dispatching plan, and ensure power supply continuity through measures such as load transfer, power purchase from the grid, and orderly power rationing.

[0036] Preferably, the system self-optimizing and evolving module includes:

[0037] A full-system performance monitoring unit, which is used to collect system operation data through more than 1000 temperature, pressure, flow rate, and vibration sensors, monitor key performance indicators such as solar energy utilization rate, thermoelectric conversion efficiency, equipment availability rate, and unit power generation cost, and establish a hierarchical storage system that can store important data for more than 5 years;

[0038] An intelligent diagnosis and analysis unit, which is used to diagnose the health status of equipment and evaluate the system performance based on the system operation data, predict equipment performance degradation through the analysis of parameter change trends, send early warning signals 2-4 weeks before a fault occurs, and identify the key bottleneck links that restrict the system performance;

[0039] A parameter adaptive optimization unit, which is used to dynamically adjust operation parameters such as temperature set values, flow distribution ratios, pressure levels, and equipment start-stop times according to the analysis results of the intelligent diagnosis and analysis unit, improve the control logic based on historical data and expert experience, and realize the continuous optimization of the system operation strategy and the self-evolution of performance.

[0040] The present invention has the following beneficial effects:

[0041] First, it significantly improves the solar energy utilization efficiency. By improving the sun positioning accuracy from the traditional ±0.5° to ±0.1°, the concentration ratio is increased by 15% - 20%; by adopting three-stage temperature stratified heat storage, the utilization rate of heat energy quality is increased from the traditional 60% to 85%; multi-source collaborative power generation makes the overall thermal efficiency of the system 8 - 12 percentage points higher than that of a single heat source system, and the direct solar energy utilization rate is increased from 20% to 25%.

[0042] Second, it fundamentally improves the power generation stability. The theoretical energy storage time of the three-stage heat storage system reaches 14 hours, and it can actually support 10 - 12 hours of stable power generation; multi-source power generation forms multiple guarantees to ensure continuous power generation at a minimum of 30% of the rated power; intelligent prediction realizes 72-hour forward-looking regulation, the system availability rate is increased from 65% to 85%, and the voltage fluctuation is controlled within ±2% from ±5%.

[0043] Third, it achieves a breakthrough in operation and maintenance efficiency. More than 1000 sensors enable full-system state perception, predicting equipment failures 2 - 4 weeks in advance; parameter adaptive optimization reduces human operation errors; the learning effect of knowledge accumulation makes the system optimization effect 15% - 20% higher than that in the initial stage after 3 years of operation. The demand for manual operation and maintenance is reduced from 24 people to 8 people, the maintenance cost is reduced from 8 million yuan / year to 4.8 million yuan / year, and the fault shutdown time is reduced from 360 hours / year to 120 hours / year.

[0044] Fourth, create significant economic and environmental benefits. The annual power generation of the 50MW power station increases by 45%, with an annual income increase of about 36 million yuan; the annual fuel cost is saved by 8 million yuan, and the operation and maintenance cost is 3.2 million yuan; the annual CO2 emissions are reduced by about 120,000 tons, realizing the resource utilization of waste and having good social benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a schematic diagram of the overall structure of the solar thermal power generation and power supply system of the present invention;

[0046] Figure 2 It is a schematic diagram of the structure of the adaptive concentrating and tracking module of the present invention;

[0047] Figure 3 It is a schematic diagram of the structure of the hierarchical cascade energy storage module of the present invention;

[0048] Figure 4 It is a schematic diagram of the structure of the multi-source collaborative power generation module of the present invention;

[0049] Figure 5 It is a schematic diagram of the structure of the intelligent load prediction and scheduling module of the present invention;

[0050] Figure 6 It is a schematic diagram of the structure of the system self-optimization and evolution module of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0051] Please refer to the attached Figure 1 , and the technical solutions of the present invention will be described in detail below in conjunction with the drawings and specific embodiments. It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other.

[0052] As Figure 1 shown, the solar thermal power generation and power supply system provided by the present invention mainly includes five core modules, namely, an adaptive concentrating and tracking module 1, a hierarchical cascade energy storage module 2, a multi-source collaborative power generation module 3, an intelligent load prediction and scheduling module 4, and a system self-optimization and evolution module 5. These five modules form an organic whole through heat flow connection, electrical connection, and data connection, realizing the efficient collection, storage, power generation, and intelligent scheduling of solar energy.

[0053] The adaptive concentrating and tracking module 1 is the energy input end of the entire solar thermal power generation system, responsible for maximizing the collection of solar radiation energy and converting it into high-quality heat flow. This module controls the multi-stage mirror array for collaborative tracking according to the solar position prediction data and cloud dynamic perception data, and adjusts the concentrating heat flow density distribution through the focus dispersion control mechanism.

[0054] Preferably, the adaptive light - concentrating tracking module 1 includes a solar position prediction unit 11, a cloud - layer dynamic perception unit 12, and a multi - level mirror collaborative control unit 13. The solar position prediction unit 11 establishes a solar position prediction model based on astronomical calculation theory. In the practical application of the solar thermal power generation system, this unit first obtains the geographical coordinate information of the power station, including the latitude φ, longitude λ, and altitude h, and then calculates astronomical parameters such as the declination angle δ and hour angle ω of the sun according to the current time information.

[0055] Solar altitude angle The calculation formula is:

[0056] ,

[0057] Wherein, is the solar altitude angle, in degrees, representing the angle between the sun's rays and the ground plane; is the latitude of the observation point, in degrees, representing the latitude of the location where the solar photovoltaic power station is located; is the solar declination angle, in degrees, representing the latitude of the sun's direct - point; is the hour angle, in degrees, representing the deviation angle between the local time and noon time.

[0058] Solar azimuth angle The calculation formula is:

[0059] ,

[0060] Wherein, A is the solar azimuth angle, in degrees, representing the angular deviation of the sun's position relative to the due - south direction.

[0061] In an embodiment of the solar thermal power generation system of the present invention, when the power station is located at a certain place in the northwest region of China, at 40°N latitude and 116°E longitude, at noon on the spring equinox (12:00), the solar declination angle , the solar altitude angle can be calculated, and the solar azimuth angle A = 180° (due - south direction). At this time, the mirror should be adjusted to the optimal elevation angle to obtain the maximum light - concentrating efficiency.

[0062] To further improve the light - concentrating positioning accuracy of the solar thermal power generation system, the solar position prediction unit 11 also needs to perform atmospheric refraction compensation. The calculation formula for the atmospheric refraction correction amount R is:

[0063] ,

[0064] Among them, R is the atmospheric refraction correction amount, with the unit of arc minute, representing the influence of atmospheric refraction on the observation of the sun's position; P is the atmospheric pressure, with the unit of millibar, which is measured in real time at the solar thermal power generation station site through a weather station; T is the atmospheric temperature, with the unit of degree Celsius, which is also obtained through on-site meteorological monitoring equipment.

[0065] The cloud dynamic perception unit 12 adopts multi-spectral detection technology in the solar thermal power generation system, and identifies cloud characteristics through comprehensive analysis of three bands of visible light, near-infrared and far-infrared. This unit establishes a cloud movement prediction model, and the prediction formula is:

[0066] ,

[0067] Among them, is the cloud cover probability at time, with a value range of 0 to 1, representing the possibility that solar energy is blocked by clouds; is the weight of the i-th prediction factor, ; is the i-th prediction function, established based on meteorological principles; is the wind speed, with the unit of meters per second, which affects the cloud movement speed; is the cloud height, with the unit of meters, which affects the shielding effect; is the cloud density, with the unit of grams per cubic meter, which affects the shielding intensity; t is the time variable, with the unit of hour; n is the number of prediction factors, usually taking values of 5 - 8.

[0068] The multi-stage mirror collaborative control unit 13 receives the sun position data and cloud prediction data, and controls the tracking actions of the mirror array in the solar thermal power generation system. The tracking angle calculation formula for each mirror is:

[0069] ,

[0070] ,

[0071] Among them, is the pitch angle of the mirror, with the unit of degree, representing the inclination angle of the mirror relative to the horizontal plane; is the azimuth angle of the mirror, with the unit of degree, representing the rotation angle of the mirror relative to the due south direction; is the solar altitude angle, with the unit of degree; is the solar azimuth angle, with the unit of degree; is the altitude angle of the target point, with the unit of degree, that is, the position angle of the heat absorber of the solar photovoltaic and thermal power generation system; is the azimuth angle of the target point, with the unit of degree.

[0072] In addition, the adaptive concentrating and tracking module 1 further includes a focus dispersion control unit 14, which monitors the heat flux density distribution in the solar thermal power generation system in real time through a temperature sensor network. When it detects that the local heat flux density exceeds the set threshold, the focus dispersion control algorithm is started. The calculation formula for the heat flux density is:

[0073] ,

[0074] where, is the heat flux density, with the unit of kilowatt per square meter, representing the heat flux intensity per unit area; is the direct solar radiation intensity, with the unit of kilowatt per square meter, measured by a pyranometer; is the reflectivity of the mirror, dimensionless, representing the reflection efficiency of the mirror for sunlight; is the area of a single mirror, with the unit of square meter; is the absorber receiving area, with the unit of square meter.

[0075] In the practical application of the solar thermal power generation system, when kilowatt per square meter, , square meter, square meter, the heat flux density is kilowatt per square meter. When the heat flux density exceeds the safety threshold of 50 kilowatt per square meter, the focus dispersion control unit 14 automatically adjusts the mirror angle to disperse the heat flux to a larger area, protecting the absorber of the solar thermal power generation system from being damaged by overheating.

[0076] The hierarchical cascade thermal energy storage module 2 is thermally connected to the adaptive concentrating and tracking module 1 through a high-temperature heat transfer pipeline, receives the high-temperature heat flux from the concentrating system, and stores the thermal energy in layers based on the principle of cascade utilization of thermodynamics. This module is the key to realizing continuous and stable power generation of the solar thermal power generation system, and maximizes the thermal energy storage efficiency and storage capacity through the three-stage temperature stratification and phase change thermal energy storage enhancement technology.

[0077] Preferably, the hierarchical cascade thermal energy storage module 2 includes a three-stage temperature stratification thermal energy storage unit 21 and a phase change thermal energy storage enhancement unit 22. The three-stage temperature stratification thermal energy storage unit 21 stores the thermal energy of different temperature levels separately according to the second law of thermodynamics, avoiding exergy loss. In the solar thermal power generation system, the working temperature of the high-temperature thermal energy storage area is 800 - 1000 °C, and a mixed molten salt of 60% sodium nitrate and 40% potassium nitrate is used as the thermal energy storage medium.

[0078] The calculation formula for the specific heat capacity of the molten salt is:

[0079] ,

[0080] where, is the specific heat capacity, with the unit of joule per kilogram per kelvin, representing the heat required for a unit mass of molten salt to increase by a unit temperature; is the temperature, with the unit of kelvin, representing the absolute temperature of the molten salt; the constant 1443 represents the base specific heat capacity, with the unit of joule per kilogram per kelvin; the coefficient 0.172 represents the rate of change of specific heat capacity with temperature, with the unit of joule per kilogram per kelvin.

[0081] The calculation formula for the heat storage capacity is:

[0082] ,

[0083] where, is the heat storage capacity, with the unit of joule, representing the total heat that the heat storage system can store; is the mass of the heat storage medium, with the unit of kilogram, representing the total mass of the molten salt in the solar thermal power generation system; is the temperature difference, with the unit of kelvin, representing the temperature change of the molten salt during the heat storage process.

[0084] In an embodiment of the solar thermal power generation system of the present invention, the high-temperature heat storage area stores 1000 tons of molten salt, and the temperature is heated from 850 °C (1123 K) to 950 °C (1223 K). The heat storage capacity is:

[0085] ,

[0086] This is equivalent to 44000 kWh of thermal energy, which can support a 50 MW solar thermal power generation unit to operate at full load for about 8 hours.

[0087] The working temperature of the medium-temperature heat storage area is 400 - 600 °C, and a biphenyl - diphenyl ether mixed heat transfer oil is used as the heat storage medium. The formula for the density of the heat transfer oil changing with temperature is:

[0088] ,

[0089] where, is the density at temperature , with the unit of kilogram per cubic meter, representing the density of the heat transfer oil at a specific temperature; is the reference temperature at which the density is, with the unit of kilogram per cubic meter, usually taking the density value at 20 °C; is the volume expansion coefficient, with the unit of per kelvin, representing the relative change in density when the temperature increases by 1 K; is the reference temperature, with the unit of kelvin, usually taking 293 K (20 °C).

[0090] The working temperature of the low-temperature preheating zone is 150 - 300 °C, and a pressurized water vapor system is used for sensible heat and latent heat storage. In a solar thermal power generation system, the latent heat of vaporization of water is 2260 kJ / kg, and the phase change heat storage density is much higher than that of sensible heat storage.

[0091] The phase change heat storage enhancement unit 22 improves the heat storage density by selecting a suitable phase change material. The calculation formula for the heat storage density of the phase change material is:

[0092] ,

[0093] where, is the heat storage density of the phase change material, with the unit of joules per cubic meter, representing the heat that can be stored per unit volume of the phase change material; is the density of the phase change material, with the unit of kilograms per cubic meter; is the specific heat capacity in the solid state, with the unit of joules per kilogram per kelvin; is the specific heat capacity in the liquid state, with the unit of joules per kilogram per kelvin; is the temperature range in the solid state, with the unit of kelvin; is the temperature range in the liquid state, with the unit of kelvin; is the latent heat of phase change, with the unit of joules per kilogram.

[0094] In addition, the stratified cascade heat storage module 2 also includes an intelligent thermal energy scheduling unit 23 and a heat loss minimization unit 24. The intelligent thermal energy scheduling unit 23 conducts heat storage and heat release scheduling according to the thermodynamic priority. In a solar thermal power generation system, the heat storage priority algorithm is:

[0095] ,

[0096] where, is the heat storage priority, dimensionless, with a value range of 0 to 1, and the larger the value, the higher the priority; is the available heat source temperature, with the unit of kelvin; is the lowest working temperature of the heat storage area, with the unit of kelvin; is the highest working temperature of the heat storage area, with the unit of kelvin; is the efficiency weight, dimensionless, with a value range of 0 to 1; is the capacity weight, dimensionless, ; is the remaining heat storage amount, with the unit of joules; is the total heat storage capacity, with the unit of joules.

[0097] The heat loss minimization unit 24 reduces the heat loss of the solar thermal power generation system through dynamic insulation control. The optimization formula for the insulation layer thickness is:

[0098] ,

[0099] Among them, is the optimal insulation layer thickness, in meters; is the thermal conductivity, in watts per meter per kelvin; is the heat conductivity, in watts per meter per kelvin; is the insulation time, in hours, representing the duration for which the solar thermal power generation system needs to be insulated; is the insulation material cost, in yuan per cubic meter; is the energy cost, in yuan per joule.

[0100] The multi-source collaborative power generation module 3 is thermally connected to the hierarchical cascade energy storage module 2 through a steam pipeline system, receives the thermal energy from each energy storage layer, and realizes collaborative power generation through a unified steam turbine unit and multiple power generation methods. This module is the energy output end of the solar thermal power generation system, responsible for efficiently converting the stored thermal energy into electrical energy.

[0101] In an embodiment of the solar thermal power generation system of the present invention, the multi-source collaborative power generation module 3 includes a main steam turbine power generation unit 31, an organic Rankine cycle power generation unit 32, and a biomass supplementary combustion power generation unit 33. The main steam turbine power generation unit 31 uses a condensing steam turbine with a rated power of 50 MW and main steam parameters of 10 MPa and 540 °C. The calculation formula for the thermal efficiency of the steam turbine is:

[0102] ,

[0103] Among them, is the thermal efficiency of the steam turbine, dimensionless, representing the efficiency of the steam turbine in converting thermal energy into mechanical energy; is the cold end temperature, in kelvin, representing the exhaust steam temperature of the steam turbine in the solar thermal power generation system; is the hot end temperature, in kelvin, representing the inlet steam temperature of the steam turbine.

[0104] The heat transfer calculation formula of the steam generator is:

[0105] ,

[0106] Among them, is the heat transfer amount, in watts, representing the heat transfer power of the steam generator in the solar thermal power generation system; is the total heat transfer coefficient, in watts per square meter per kelvin, representing the comprehensive heat transfer ability of the heat transfer equipment; is the heat transfer area, in square meters, representing the effective heat exchange area of the steam generator; is the logarithmic mean temperature difference, in kelvin.

[0107] The calculation formula for the logarithmic mean temperature difference is:

[0108] ,

[0109] where, is the temperature difference at the hot end of the heat exchanger, in Kelvin, representing the difference between the inlet temperature of the hot fluid and the inlet temperature of the cold fluid; is the temperature difference at the cold end of the heat exchanger, in Kelvin, representing the difference between the outlet temperature of the hot fluid and the outlet temperature of the cold fluid; is the natural logarithm function.

[0110] The organic Rankine cycle power generation unit 32 generates electricity using the medium and low temperature heat sources of the solar thermal power generation system. The working fluid is R245fa, with a boiling point of 15.14 °C, suitable for medium and low temperature waste heat recovery. The calculation formula for the efficiency of the ORC system is:

[0111] ,

[0112] where, is the efficiency of the ORC system, dimensionless, representing the thermoelectric conversion efficiency of the organic Rankine cycle system; is the net output work, in Watts; is the input heat, in Watts; is the work of the steam turbine, in Watts, representing the mechanical work generated by the organic working fluid to drive the steam turbine; is the work of the working fluid pump, in Watts, representing the mechanical work consumed by the circulating working fluid pump; is the heat absorption of the evaporator, in Watts.

[0113] The biomass supplementary combustion power generation unit 33 provides a supplementary heat source when the solar energy of the solar thermal power generation system is insufficient. The calculation formula for the calorific value of biomass combustion is:

[0114] ,

[0115] where, is the calorific value of biomass combustion, in Joules, representing the heat released by the complete combustion of biomass fuel; is the mass of biomass, in kilograms; is the lower calorific value of biomass, in Joules per kilogram, representing the calorific value of unit mass of biomass fuel; is the combustion efficiency, dimensionless, representing the ratio of the actual heat released by biomass fuel combustion to the theoretical heat of combustion.

[0116] In addition, the multi-source collaborative power generation module 3 further includes a multi-pressure-level steam coordination unit 34 and an intelligent switching control unit 35. The multi-pressure-level steam coordination unit 34 establishes high, medium, and low pressure steam systems to achieve the cascaded utilization of steam in the solar thermal power generation system. The optimization algorithm for steam distribution is as follows:

[0117] ,

[0118] Among them, is the high-pressure steam output, with the unit of kilograms per hour; is the medium-pressure steam output, with the unit of kilograms per hour; is the low-pressure steam output, with the unit of kilograms per hour; is the distribution matrix coefficient, dimensionless, indicating the distribution ratio of the th demand for the th level of steam; is the steam demand for the steam turbine, with the unit of kilograms per hour; is the steam demand for the process, with the unit of kilograms per hour; is the steam demand for heating, with the unit of kilograms per hour. The above matrix is a 3×3 dimension, indicating the distribution relationship between the three steam pressure levels and the three steam demand types.

[0119] The intelligent switching control unit 35 performs switching control according to the heat source priority management strategy. In the solar thermal power generation system, the decision matrix of the switching logic is:

[0120] ,

[0121] Among them, is the selected power generation mode, with values of 1, 2, or 3, representing the solar mode, waste heat mode, and biomass mode respectively; represents finding the value of the variable that maximizes the objective function; is the available solar power, with the unit of megawatts; is the available waste heat power, with the unit of megawatts; is the available biomass power, with the unit of megawatts; is the cleanliness weight, dimensionless, with a value range of 0 to 1; is the efficiency weight, dimensionless, with a value range of 0 to 1; is the reliability weight, dimensionless, with a value range of 0 to 1, and 1.

[0122] The intelligent load prediction and scheduling module 4 is electrically connected to the multi-source collaborative power generation module 3, collects power grid and load data, generates accurate load prediction results, and formulates an optimal power generation scheduling plan. This module is the core of realizing the intelligent operation of the solar thermal power generation system, ensuring the balance between supply and demand and economic operation through multi-dimensional data fusion and advanced prediction algorithms.

[0123] In an embodiment of the solar thermal power generation system of the present invention, the intelligent load prediction and scheduling module 4 includes a multi-dimensional data acquisition unit 41 and a load prediction algorithm unit 42. The multi-dimensional data acquisition unit 41 establishes a comprehensive monitoring network to collect meteorological data, power grid data, and user behavior data. The data acquisition frequency is once per minute to ensure the real-time and accuracy of the scheduling data of the solar thermal power generation system.

[0124] The load prediction algorithm unit 42 adopts a multi-factor time series prediction model, and the prediction formula is:

[0125] ,

[0126] where, is the load prediction value at time, in megawatts, representing the power that the solar thermal power generation system needs to provide at a certain future time; is the weight of the th prediction factor, dimensionless, is the th prediction function, established based on statistical and machine learning methods; is the th prediction function, established based on statistical and machine learning methods; is the weather temperature, in degrees Celsius; is the historical load data, in megawatts; is the calendar information, including factors such as week and holiday; is the economic activity index, representing the economic activity level of the region; is the number of prediction factors, usually taking values from 8 to 12; is the time variable, in hours.

[0127] The function of temperature on load is:

[0128] ,

[0129] where, is the temperature influence function, in megawatts, representing the influence of temperature change on the power supply load of the solar photovoltaic power generation system; is the low temperature sensitivity coefficient, in megawatts per degree Celsius, representing the sensitivity of load change with temperature below the reference temperature; is the high temperature sensitivity coefficient, in megawatts per degree Celsius, representing the sensitivity of load change with temperature above the reference temperature; is the actual temperature in degrees Celsius; is the reference temperature in degrees Celsius, usually taken as 18°C, representing the critical temperature at which the load is not sensitive to temperature changes.

[0130] The periodic modeling formula for historical load is:

[0131] ,

[0132] where, is the periodic load component at time, in megawatts, representing the periodic change law of the load of the solar photovoltaic power generation system; is the amplitude, in megawatts, representing the amplitude of the periodic change of the load; is the period, in hours, usually taken as 24 hours (daily cycle) or 168 hours (weekly cycle); is the phase, in radians, representing the initial phase of the periodic function; is the DC component, in megawatts, representing the basic level of the load; is the pi, approximately equal to 3.14159.

[0133] In addition, the intelligent load prediction and scheduling module 4 also includes a power generation scheduling optimization unit 43 and an emergency response processing unit 44. The power generation scheduling optimization unit 43 uses a multi-objective optimization algorithm. In the solar thermal power generation system, the objective function is:

[0134] ,

[0135] The constraint conditions include:

[0136] Power balance constraint: ;

[0137] Capacity constraint: ;

[0138] Ramp constraint: ;

[0139] where, is the total cost function, in yuan, representing the total operating cost of the solar thermal power generation system; is the fuel cost of the th unit, in yuan per megawatt-hour; is the start-up cost of the th unit, in yuan; is the environmental cost of the th unit, in yuan per megawatt-hour; is the output of the th unit, in megawatts; It is the start-up and shutdown state of the unit, with a value of 0 or 1. 0 indicates shutdown, and 1 indicates startup; It is the pollutant emissions of the th unit, in kilograms per megawatt-hour; It is the minimum output of the th unit, in megawatts; It is the th unit's maximum output, in megawatts; It is the th unit's ramping rate, in megawatts per minute;

[0140] The emergency response processing unit 44 establishes an abnormal situation identification and handling mechanism. In the solar thermal power generation system, the anomaly detection algorithm adopts statistical control theory, and the control limit calculation formula is:

[0141] UCL = μ + 3σ,

[0142] LCL = μ - 3σ,

[0143] Among them, UCL is the upper control limit, with the same unit as the monitoring parameter, representing the upper limit of the normal operating parameter; LCL is the lower control limit, with the same unit as the monitoring parameter, representing the lower limit of the normal operating parameter; μ is the mean value, with the same unit as the monitoring parameter, representing the average value of the monitoring parameter during the normal operation of the solar thermal power generation system; σ is the standard deviation, with the same unit as the monitoring parameter, representing the dispersion degree of the monitoring parameter; the coefficient 3 represents the 3-fold standard deviation principle, corresponding to a confidence level of 99.7%.

[0144] When the monitoring parameter exceeds the control limit, the emergency response process is started. The optimization algorithm for load transfer is:

[0145] ,

[0146] Constraint conditions: , ,

[0147] Among them, is the transfer cost from the demand point to the supply point , in yuan per megawatt; is the transfer volume from the demand point to the supply point , in megawatts; is the demand at the demand point , in megawatts, representing the power demand at the th load point in the solar thermal power generation system; For the supply point The supply capacity, in megawatts, represents the power supply capacity of the th power generation unit; Is the number of demand points; Is the number of supply points.

[0148] The system self-optimizing and evolving module 5 is respectively connected to the data of the foregoing four modules, responsible for monitoring the operating status of the entire solar thermal power generation system, generating optimization instructions based on the performance evaluation results, and realizing the continuous improvement and self-evolution of the system. This module is the highest level of the intelligence of the solar thermal power generation system, and continuously improves the system performance through big data analysis and machine learning technology.

[0149] In an embodiment of the solar thermal power generation system of the present invention, the system self-optimizing and evolving module 5 includes a full-system performance monitoring unit 51, an intelligent diagnosis and analysis unit 52, and a parameter adaptive optimization unit 53. The full-system performance monitoring unit 51 establishes a KPI monitoring system, and the calculation formula of the key performance indicators is:

[0150] Solar energy utilization rate:

[0151] ,

[0152] Thermoelectric conversion efficiency:

[0153] ,

[0154] System comprehensive efficiency:

[0155] ,

[0156] Among them, Is the solar energy utilization rate, in percentage, representing the proportion of the solar energy actually collected by the solar thermal power generation system in the available solar energy; Is the collected heat, in joules, representing the solar heat actually collected by the concentrating system; Is the available solar energy, in joules, representing the total energy provided by solar radiation; Is the thermoelectric conversion efficiency, in percentage, representing the efficiency of converting thermal energy into electrical energy; Is the power generation power, in watts; Is the input heat, in watts; Is the system comprehensive efficiency, in percentage, representing the overall energy conversion efficiency of the solar thermal power generation system; Is the net power generation power, in watts, representing the net electrical power output by the system; Is the solar radiation intensity, in watts per square meter; Is the total area of the reflector, in square meters.

[0157] The intelligent diagnosis and analysis unit 52 adopts trend analysis and anomaly detection algorithms. In the solar thermal power generation system, the calculation formula for the equipment health index is as follows:

[0158] ,

[0159] where is the health index, dimensionless, with a value range of 0 to 1. The closer the value is to 1, the better the equipment health condition; is the weight of the i-th parameter, dimensionless, ; is the value of the i-th parameter during normal operation, and the unit is determined according to the parameter type; is the current value of the i-th parameter, and the unit is the same as that of ; is the number of monitored parameters.

[0160] Fault prediction adopts the exponential smoothing method, and the prediction formula is:

[0161] ,

[0162] ,

[0163] where is the smoothed value at time , with the same unit as the monitored parameter, representing the parameter value after smoothing; is the smoothing coefficient, dimensionless, with a value range of 0 to 1, usually taking 0.1 to 0.3; is the observed value at time , with the same unit as the monitored parameter; is the smoothed value at time , with the same unit as ; is the remaining useful life, in hours or days, representing the expected time until the failure of the solar thermal power generation system equipment; is the fault threshold, with the same unit as the monitored parameter; is the degradation slope, in units of the monitored parameter per unit time.

[0164] The parameter adaptive optimization unit 53 uses the genetic algorithm for parameter optimization. In the solar thermal power generation system, the fitness function of the genetic algorithm is:

[0165] ,

[0166] where is the fitness value, dimensionless, with a value range of 0 to 1. The larger the value, the better the individual fitness; is the weight of the i-th target, dimensionless, ; is the target value of the i-th target, and the unit is determined according to the target type; is for the th target, the actual value, and the unit is the same as that of ; is the number of optimization targets.

[0167] The selection operation adopts the roulette method, and the selection probability is:

[0168] ,

[0169] where is the probability that the individual is selected, dimensionless, and the value range is from 0 to 1; is the fitness value of the individual ; is the population size, representing the total number of individuals in the genetic algorithm. The crossover operation adopts single-point crossover, and the crossover position is randomly selected. The mutation probability of the mutation operation is:

[0170] ,

[0171] where is the mutation probability, dimensionless, and the value range is from 0 to 1; is the basic mutation probability, dimensionless, usually taking values from 0.01 to 0.1; is the current generation number, dimensionless; is the maximum generation number, dimensionless; is the natural constant, approximately equal to 2.71828.

[0172] Through the coordinated cooperation of the above five core modules, the solar thermal power generation and power supply system of the present invention realizes the full-process intelligent control from solar energy collection to power output. When the system operates, the adaptive concentrator tracking module 1 converts solar radiation into high-temperature heat flow, the hierarchical cascade thermal energy storage module 2 stores thermal energy in layers, the multi-source collaborative power generation module 3 converts thermal energy into electrical energy, the intelligent load prediction and scheduling module 4 optimizes the power generation plan, and the system self-optimization and evolution module 5 continuously improves the system performance, forming a complete closed-loop control system.

[0173] Actual operation data shows that the solar energy utilization rate of the solar thermal power generation system of the present invention reaches more than 25%, the system availability exceeds 85%, and the power generation cost is reduced by more than 20% compared with the traditional solar thermal power generation system, fully verifying the practicability of the technical solution.

[0174] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, various modifications and variations can be made to the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A solar thermal power generation and power supply system, characterized in that, Including: An adaptive concentrating tracking module, which is used to control the cooperative tracking of a multi-stage mirror array according to the solar position prediction data and cloud dynamic perception data, and adjust the concentrating heat flux density distribution through a focus dispersion control mechanism, and output a stable high-temperature heat flux to the subsequent module; A hierarchical cascade thermal energy storage module, which is thermally connected to the adaptive concentrating tracking module, and is used to receive the high-temperature heat flux, and store thermal energy in the high-temperature thermal energy storage area, medium-temperature thermal energy storage area and low-temperature preheating area respectively based on the temperature level, wherein the working temperature of the high-temperature thermal energy storage area is 800-1000 °C, the working temperature of the medium-temperature thermal energy storage area is 400-600 °C, and the working temperature of the low-temperature preheating area is 150-300 °C; A multi-source cooperative power generation module, which is thermally connected to the hierarchical cascade thermal energy storage module, and is used to receive the thermal energy from the high-temperature thermal energy storage area, the medium-temperature thermal energy storage area and the low-temperature preheating area, and perform cooperative power generation through a main steam turbine power generation system, an organic Rankine cycle power generation system and a biomass supplementary combustion power generation system, and output stable power; An intelligent load prediction and scheduling module, which is electrically connected to the multi-source cooperative power generation module, and is used to collect multi-dimensional data and generate a load prediction result, formulate a power generation scheduling plan based on the load prediction result, and send a scheduling instruction to the multi-source cooperative power generation module; A system self-optimization and evolution module, which is respectively data-connected to the adaptive concentrating tracking module, the hierarchical cascade thermal energy storage module, the multi-source cooperative power generation module and the intelligent load prediction and scheduling module, and is used to monitor the system operation parameters, generate parameter optimization instructions based on the performance evaluation result, and realize the adaptive adjustment of system parameters and the continuous improvement of operation strategies.

2. The solar thermal power generation power supply system according to claim 1, wherein The adaptive concentrating tracking module includes: A solar position prediction unit, which is used to calculate the solar altitude angle and azimuth angle based on geographical coordinates and time parameters, and perform atmospheric refraction compensation according to atmospheric pressure, temperature and humidity parameters, and output solar position data with an accuracy of ±0.1°; A cloud dynamic perception unit, which is used to scan the cloud state within a radius of 50 kilometers through a multi-spectral detection device, identify the cloud thickness, moving speed and shielding probability, and predict the future solar radiation change trend within 30 minutes; A multi-stage mirror cooperative control unit, which is used to receive the solar position data and cloud prediction data, control the main mirror array with a mirror area of 25 square meters to perform two-axis precise tracking, and concentrate the solar radiation by more than 1000 times through a secondary concentrator.

3. The solar thermal power generation power supply system according to claim 2, characterized in that, The adaptive concentrating tracking module further includes: A focus dispersion control unit, which is used to monitor the heat flux density distribution on the surface of the heat absorber through 100 temperature sensors. When the detected local temperature gradient exceeds 50 °C / cm, the reflection angle is automatically adjusted within 10 seconds to disperse the focus to the surrounding area, ensuring uniform distribution of the heat flux density and the focus position adjustment accuracy reaching ±1 cm.

4. The solar thermal power generation and power supply system according to claim 1, wherein The hierarchical cascade thermal energy storage module includes: Three - level temperature - stratified thermal energy storage unit, where the high - temperature thermal energy storage area uses a mixture of sodium nitrate and potassium nitrate molten salts as the thermal energy storage medium, and the thermal energy storage capacity meets the demand of 8 - hour full - load power generation. The medium - temperature thermal energy storage area uses a mixture of diphenyl - diphenyl ether heat - conducting oil as the thermal energy storage medium, and the thermal energy storage capacity meets the demand of 4 - hour medium - load power generation. The low - temperature pre - heating area uses a pressurized steam system for sensible - heat and latent - heat storage; Phase - change thermal - energy - storage enhancement unit, which is used to configure a carbonate eutectic phase - change material with a melting point of 760 °C in the high - temperature thermal energy storage area and a nitrate hydrate phase - change material with a melting point of 320 °C in the medium - temperature thermal energy storage area, and enhances the heat - transfer efficiency through finned tube bundles and a graphite heat - conducting network.

5. The solar thermal power generation power supply system according to claim 4, wherein, The stratified cascade thermal - energy - storage module further includes: Intelligent thermal - energy scheduling unit, which is used to achieve the thermal - energy balance scheduling of each temperature level based on the load - prediction data and solar - energy - collection - quantity prediction data, according to the thermal - energy - storage priority of preferentially storing heat in the high - temperature thermal energy storage area and storing heat step by step downward, and the heat - release priority of preferentially using the thermal energy in the high - temperature thermal energy storage area to ensure the highest power - generation efficiency; Thermal - loss minimization unit, which is used to automatically adjust the thickness of the thermal insulation layer according to the ambient temperature, control the thermal conductivity of key pipelines below 0.01 W / m·K through a vacuum - interlayer structure, and collect waste - heat resources such as the exhaust heat of the steam turbine and equipment heat dissipation and input them into the corresponding thermal - energy storage areas according to the temperature levels.

6. The solar thermal power generation power supply system according to claim 1, wherein The multi - source collaborative power - generation module includes: Main steam - turbine power - generation unit, which is used to receive the high - temperature molten salt from the high - temperature thermal energy storage area, generate main steam with a pressure of 10 MPa and a temperature of 540 °C through a spiral - coil heat exchanger, drive a condensing steam turbine with a rated power of 50 MW to generate electricity, and achieve a thermoelectric conversion efficiency of ≥42%; Organic Rankine cycle power - generation unit, which is used to utilize the medium - and low - temperature heat sources from the medium - temperature thermal energy storage area and the low - temperature pre - heating area, and drive an organic Rankine cycle system with a rated power of 5 MW to generate electricity through the R245fa working fluid; Biomass supplementary - combustion power - generation unit, which is used to provide a supplementary heat source with a thermal power of 10 MW when solar energy is insufficient, burn agricultural and forestry waste through fluidized - bed combustion, with a combustion efficiency > 95% and a soot emission < 30 mg / m³.

7. The solar thermal power generation power supply system according to claim 6, characterized in that, The multi - source collaborative power - generation module further includes: Multi - pressure - level steam coordination unit, which is used to establish a high - pressure steam circuit, a medium - pressure steam circuit, and a low - pressure steam circuit. The parameters of the high - pressure steam circuit are 10 MPa and 540 °C for main - steam - turbine power generation, the parameters of the medium - pressure steam circuit are 2.5 MPa and 350 °C for process steam, and the parameters of the low - pressure steam circuit are 0.5 MPa and 150 °C for heating and deaeration; Intelligent switching control unit, which is used to manage the heat - source priority strategy with solar thermal power generation as the first priority, waste - heat power generation as the second priority, and biomass supplementary combustion as the third priority, and ensure the frequency and voltage stability of the output power through power prediction and smoothing algorithms.

8. The solar thermal power generation power supply system according to claim 1, characterized in that, The intelligent load - prediction and scheduling module includes: Multi-dimensional data acquisition unit, which is used to collect solar radiation intensity, cloud cover, wind speed and direction, temperature and humidity data, as well as real-time power, voltage and current data of each substation of the power grid, and identify the electricity consumption patterns of industrial users, commercial users and residential users, and adjustable load characteristics such as air-conditioning load and electric vehicle charging load; Load forecasting algorithm unit, which is used to generate short-term load forecasting results for 1 to 24 hours and medium- and long-term load forecasting results for 1 to 7 days based on the multi-dimensional data through a multi-factor analysis method considering weather factors, historical load patterns and holiday impacts, and the error of the day-ahead load forecasting is controlled within ±3%.

9. The solar thermal power generation power supply system according to claim 8, characterized in that, The intelligent load forecasting and scheduling module further includes: Power generation scheduling optimization unit, which is used to generate a power generation scheduling plan every 15 minutes based on the short-term load forecasting results and the medium- and long-term load forecasting results, with the economic objective of minimizing power generation costs, the reliability objective of ensuring power supply reliability, and the environmental protection objective of preferentially using clean energy as the multi-objective optimization strategy; Emergency response processing unit, which is used to identify abnormal situations such as sudden cloud cover, equipment failures, load mutations, fuel shortages, etc., start the preset emergency scheduling plan, and ensure power supply continuity through measures such as load transfer, power purchase from outside, and orderly power rationing.

10. The solar thermal power generation power supply system according to claim 1, wherein The system self-optimization and evolution module includes: Full-system performance monitoring unit, which is used to collect system operation data through more than 1000 temperature, pressure, flow and vibration sensors, monitor key performance indicators such as solar energy utilization rate, thermoelectric conversion efficiency, equipment availability rate, and unit power generation cost, and establish a hierarchical storage system covering important data stored for more than 5 years; Intelligent diagnosis and analysis unit, which is used to diagnose the health status of equipment and evaluate the system performance based on the system operation data, predict the degradation of equipment performance through the analysis of the parameter change trend, send a warning signal 2-4 weeks before the failure occurs, and identify the key bottleneck links restricting the system performance; Parameter adaptive optimization unit, which is used to dynamically adjust operation parameters such as temperature set value, flow distribution ratio, pressure level, and equipment start-stop time according to the analysis results of the intelligent diagnosis and analysis unit, improve the control logic based on historical data and expert experience, and realize the continuous optimization of the system operation strategy and the self-evolution of the performance.

Images