A method for optimizing operation of a tower solar thermal power system adapted to harsh environments

Abstract

This invention discloses an optimized operation method for a tower solar thermal power system adapted to harsh environments, comprising: establishing a heliostat energy flow prediction model and an optical efficiency prediction model for the reflection of the heliostat field onto the absorber surface under different time periods and environmental conditions; analyzing the optimal steam parameters of the steam power generation device under different time periods based on the digital twin model of the tower solar thermal power system, under the input conditions of grid dispatch command demand and dynamic electricity price signal; establishing a dynamic energy flow allocation model for the molten salt thermal storage device and the absorber based on the predicted energy flow, predicted optical efficiency, and optimal steam parameters under different time periods and environmental conditions, and in combination with the heat storage, heat release, and operating parameters of the molten salt thermal storage device and the absorber, to satisfy the optimal steam parameter values; and establishing a control model for the steam power generation device based on the dynamic energy flow allocation results of the molten salt thermal storage device and the absorber, and in combination with the optimal steam parameter values.

Description

Technical Field

[0001] This invention belongs to the field of tower solar thermal power system operation technology, specifically relating to an optimized operation method for tower solar thermal power systems adapted to harsh environments. Background Technology

[0002] Tower solar thermal power systems use large-scale heliostat fields to concentrate sunlight onto heat collection devices, which then convert the heat energy into high-temperature heat energy and transfer it to the heat carrier. The high-temperature heat energy heats water to generate steam, which in turn drives a steam turbine to rotate and generate electricity. Excess heat energy is stored in a thermal storage device to meet the peak-shaving needs of the power grid. Through the above process, the energy conversion between light, heat and electricity is completed.

[0003] However, tower solar thermal power systems face severe environmental challenges such as strong winds, sandstorms, and low temperatures. The energy flow received and reflected by the heliostat field varies drastically due to cloud cover and wind loads, causing a mismatch between the energy supply and demand of large-scale heliostat fields and steam power generation devices. How to enable tower solar thermal power systems to adapt to harsh environments and predict the energy supply and demand under different environmental conditions in advance, and realize the dynamic allocation and control of energy flow between devices, is an urgent problem to be solved.

[0004] Based on the aforementioned technical issues, a new optimized operation method for tower solar thermal power systems adapted to harsh environments needs to be designed. Summary of the Invention

[0005] The technical problem to be solved by this invention is to overcome the shortcomings of the prior art and provide an optimized operation method for tower solar thermal power systems that are adapted to harsh environments. By using a digital twin model as the core, the method achieves the following: advance prediction of mirror field energy flow, active optimization of steam parameters, dynamic allocation of energy flow, and precise control of the steam power generation device. This improves the system's adaptability to harsh environments. The entire process replaces the passive response and manual adjustment mode of traditional systems, realizing the active optimization, advance adjustment, and precise control of the entire system, and significantly improving the automation and intelligent operation level of the system.

[0006] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows: This invention provides an optimized operation method for a tower solar thermal power system adapted to harsh environments, comprising: S1. Establish a digital twin model of a tower solar thermal power system that includes a heliostat field, receiver, molten salt thermal storage device, steam power generation device and power grid; S2. Based on the digital twin model of the tower solar thermal power system, the influence of normal environment and different harsh environment on the optical performance of the heliostat field is simulated and quantitatively analyzed. A multi-environmental condition influence dataset is formed. After analyzing the mirror deformation compensation coefficient and spot offset error of the heliostat field, a mirror field energy flow prediction model and an optical efficiency prediction model are established for the heliostat field reflected to the absorber surface under different environmental conditions at different times. S3. Based on the digital twin model of the tower solar thermal power system, analyze the optimal values ​​of steam parameters of the steam power generation unit under different time periods under the input conditions of grid dispatch command demand and dynamic electricity price signal; S4. Based on the predicted values ​​of mirror field energy flow, optical efficiency, and steam parameters under different time periods and environmental conditions, and combined with the heat storage, heat release, and operating parameters of the molten salt thermal storage device and the absorber, and with the condition of satisfying the steam parameter optimization value, establish a dynamic energy flow distribution model for the molten salt thermal storage device and the absorber, and obtain the dynamic energy flow distribution results for the molten salt thermal storage device and the absorber. S5. Based on the dynamic energy flow distribution results of the molten salt thermal storage device and the absorber, and combined with the optimal steam parameter values, a control model for the steam power generation device is established to obtain the regulation and control parameters of the steam power generation device under different environmental conditions at different times.

[0007] Furthermore, S1 specifically includes: Based on the actual deployment of the heliostat field, receiver, molten salt thermal storage device, steam power generation device and power grid, each subsystem is modeled separately. Three-dimensional geometric models of each physical entity, physical law models of equipment operation, and energy flow transfer behavior models between devices are established and fused to form a mechanism model of the tower solar thermal power system. The energy flow transfer behavior model between devices is based on light, heat and electrical energy flow as the main line, coupling of each physical entity, and clarifying the dynamic characteristics and constraints of energy flow coupling. Historical operating data, real-time operating data, and environmental data of each device in the tower solar thermal power system are collected, and a digital twin model of the tower solar thermal power system is established after the mechanism model of the tower solar thermal power system is mapped to the real world. A coupling verification mechanism and an online self-calibration mechanism are added to the digital twin model of the tower solar thermal power system. The accuracy of energy flow coupling between physical entities in the model is verified by historical operation data, so that the energy flow coupling error is less than the preset value. When the deviation between the model prediction value and the measured value exceeds the threshold, the model parameters are automatically corrected.

[0008] Furthermore, in S2, the influence of normal and different harsh environments on the optical performance of the heliostat field is quantitatively analyzed based on the digital twin model of the tower solar thermal power system, forming a multi-environmental condition influence dataset, including: Define the core characteristic parameters of normal environmental conditions, including the range of solar irradiance, the range of ambient temperature, wind speed, and air cleanliness. The core characteristic parameters for different harsh environmental conditions are defined, including core characteristic parameters for solar radiation intensity, single sandstorm environment, strong wind environment, high temperature or low temperature environment, rainy or hazy environment, and core characteristic parameters for various combinations of environments; the core characteristic parameters for sandstorm environment include sandstorm concentration, sandstorm particle size, and sandstorm adhesion amount; the core characteristic parameters for strong wind environment include average wind speed, gust coefficient, and wind direction; the core characteristic parameters for high temperature or low temperature environment include extreme ambient temperature and temperature change rate; the core characteristic parameters for rainy or hazy environment include relative humidity, visibility, and raindrop or fog droplet size. An environmental condition simulation submodule is embedded into the existing digital twin model of a tower solar thermal power system to perform spatial and temporal simulations of environmental parameters. Historical environmental monitoring data of the location is input into the environmental condition simulation submodule to adjust the environmental parameter coefficients within the module and clarify the physical coupling relationship between the heliostat field and each core environmental characteristic parameter. Simulation boundary conditions are set so that the digital twin model can simulate and reproduce real environmental characteristics. The simulation boundary conditions include the coupling of dust adhesion to the mirror surface, the mechanical coupling of wind speed and mirror deformation, and the coupling of temperature change and thermal expansion and contraction of mirror deformation. In the digital twin model of the tower solar thermal power system, the normal environment is used as the baseline operating condition. The harsh environment simulation is carried out by superimposing single environmental parameters and multi-environment combination parameters in turn through the control variable method. The simulation results are then quantitatively analyzed for the core optical performance indicators to obtain the influence law of different environmental conditions on the optical performance of the heliostat field and form a multi-environment influence dataset.

[0009] Furthermore, the single-environment parameter simulation involves changing only one type of harsh environmental parameter while keeping other parameters at normal environmental values, quantifying the impact of a single environmental parameter on core optical performance indicators; the multi-environment combined parameter superposition simulation combines harsh environmental parameters to quantify the synergistic effect of multiple environmental combined parameters on optical performance; and during the simulation process, hourly or time-period-level simulations are performed for each type of environmental condition to clarify the differences in optical performance changes under different environmental conditions at different times. The core optical performance indicators include specular reflectivity, heliostat normal deviation angle, absorber surface energy flux density distribution characteristics, overall optical efficiency of the heliostat field, and absorber spot offset. Specular reflectivity includes reflectivity attenuation caused by dust adhesion, high-temperature oxidation, and humidity condensation. The heliostat normal deviation angle includes the conversion of specular deformation caused by strong winds and temperature deformation into normal tracking deviation. The absorber surface energy flux density distribution characteristics include the mean, extreme values, and non-uniformity of the absorber surface energy flux density. The overall optical efficiency of the heliostat field includes the conversion efficiency of the incident energy flux to the received energy flux in the absorber. The absorber spot offset includes the spatial offset distance between the actual spot center and the theoretical spot center on the absorber surface.

[0010] Furthermore, in S2, after analyzing the heliostat field mirror deformation compensation coefficient and spot offset error, a prediction model for the mirror field energy flow and optical efficiency of the heliostat field reflected onto the absorber surface under different time periods and environmental conditions is established, including: After feature extraction of the dataset affected by multiple environmental conditions, it is divided into environmental condition dimension features, time dimension features, and optical performance dimension features. By performing regression analysis on the features of each dimension, the correlation between the deformation compensation coefficient and the features of each dimension is extracted. The deformation compensation coefficient under different environmental conditions is then calculated to offset the influence of deformation on optical performance, as expressed in the following form: ; , , , , These are the fitting coefficients; Wind speed; This represents the difference between the ambient temperature and the normal temperature. This refers to the amount of sand and dust adhering to the surface. For other environmental operating conditions; Correlation analysis was used to analyze the features of each dimension, extracting features strongly correlated with the spot offset error. Spot offset correction factors were calculated for the spatial offset distance along the x and y axes, and expressed as follows: ; ; This represents the offset of the actual spot center on the absorber surface from the theoretical spot center along the x-axis. This is the theoretical effective receiving radius of the light spot; This represents the offset of the actual spot center and the theoretical spot center on the absorber surface along the y-axis. The mirror deformation compensation coefficient and the spot offset correction factor are mapped with time dimension features and environmental condition dimension features to form a multi-environment-time period optical correction parameter library; Based on the physical behavior of optical propagation in the heliostat field, a basic model for energy flow prediction based on physical mechanisms is constructed. A multi-environment-time period optical correction parameter library is introduced to obtain a corrected heliostat field energy flow computational model. The output value of the heliostat field energy flow computational model and the features of each dimension are used as input features. After training the model using machine learning algorithms, a heliostat field energy flow prediction model is established. Based on the predicted mirror field energy flow and the physical laws of various losses affecting optical efficiency, a physical mechanism model of optical efficiency is constructed. Then, the output value of the physical mechanism model of optical efficiency and the features of each dimension are used as input features, and a machine learning algorithm is used to train the model to establish an optical efficiency prediction model.

[0011] Furthermore, S3 includes: The power grid dispatch instructions, power upper and lower limit constraints, and ramp rate are transformed into power target values, regulation rates, and time period constraint parameters that can be identified by the digital twin model of the tower solar thermal power system. At the same time, it connects to the real-time electricity price interface of the electricity market to obtain dynamic electricity price signals for each time period. Based on the digital twin model of a tower solar thermal power system, the correlation between different steam parameters and power generation revenue and safety is analyzed. With the optimal power generation revenue and safety as the objective function, an intelligent optimization algorithm is used to solve for the optimal values ​​of steam parameters at different time periods.

[0012] Furthermore, the objective function of maximizing power generation revenue is expressed as: ; Total runtime; The duration of the time period; The power target value required by the scheduling instruction; Let be the power generation efficiency corresponding to the steam parameters during time period t. , , These are steam temperature, pressure, and flow rate, respectively. The dynamic electricity price for time period t; The operating cost of the steam power generation unit during time period t is related to the temperature of the produced steam. ,pressure ,flow Related; Taking optimal security as the objective function, it can be expressed as: ; The equipment safety index corresponding to the steam parameters during time period t; For safety indicator thresholds; For safety weights.

[0013] Furthermore, S4 includes: The system obtains predicted values ​​of mirror field energy flow and optical efficiency for different time periods and environmental conditions. Combined with the optimal values ​​of steam parameters, the heat storage and release of the molten salt thermal storage device, and the operating parameters of the absorber, it determines whether the optimal values ​​of steam parameters are met after converting light energy into heat energy and then heat energy into electrical energy. If they are met, the energy flow is supplied by the heliostat, absorber, and molten salt thermal storage device in coordination. Using the predicted values ​​of mirror field energy flow and optical efficiency, the optimal values ​​of steam parameters, the heat storage and heat release of the molten salt thermal storage device, and the operating parameters of the absorber under different time periods and environmental conditions as input variables, and with the objective functions of the optimal steam energy flow demand satisfaction and the optimal mirror field energy flow utilization rate, a dynamic energy flow allocation model for the molten salt thermal storage device and the absorber is established. The intelligent optimization algorithm is used to solve for the absorber heat power and the heat storage and heat release power of the molten salt thermal storage device under different time periods and environmental conditions.

[0014] Furthermore, the objective function, which is to optimize the satisfaction of steam energy flow demand, is expressed as: ; The heat absorption power of the absorber during time period t; The absorption efficiency of the receiver for the energy flow of the heliostat field; The value represents the heat stored and released by the molten salt thermal storage device during time period t. If the value is greater than 0, it indicates that the molten salt thermal storage device releases heat to supplement the energy flow. If the value is less than 0, it indicates that the energy flow of the heliostat field is excessive and the molten salt thermal storage device stores heat. If the value is 0, it indicates that the molten salt thermal storage device does not store or release heat. The heat storage and release efficiency of molten salt thermal storage devices; The steam energy flow demand of the steam power generation unit during period t; The objective function, which is to optimize the energy flow utilization rate of the mirror field, is expressed as: ; The total mirror field energy flow at the receiver inlet during time period t; Let be the field optical efficiency of the mirror during time period t.

[0015] Furthermore, S5 includes: The dynamic energy flow distribution results of the molten salt thermal storage device and the absorber are obtained and combined with the historical optimal values ​​of steam parameters and the historical regulation and control parameters of the steam power generation device as model variables; the historical regulation and control parameters include the superheater desuperheating water flow rate, the turbine inlet valve opening degree, the feedwater pump speed, and the condenser cooling water flow rate. To meet the target values ​​of main steam temperature, pressure, flow rate and power generation, the superheater temperature dynamic model, turbine power dynamic model and feedwater pump-steam flow dynamic model are integrated based on the digital twin model of the tower solar thermal power system to form an overall dynamic coupling model of the steam power generation device. The model variables are input into the overall dynamic coupling model of the steam power generation unit. A closed-loop control strategy combining cascade PID control, model predictive control and energy flow disturbance feedforward compensation is adopted to establish the control model of the steam power generation unit. The adjustment and control parameters of the steam power generation unit under different time periods and environmental conditions are obtained by solving the model, so that the steam parameters of the steam power generation unit can stably track the set target value under different time periods and environmental conditions. The cascaded PID control includes: setting an inner loop PID control, with the superheater inlet steam temperature as the controlled variable and the opening of the desuperheating water regulating valve as the regulating control variable, to suppress local disturbances in the desuperheating water pressure and flow rate; and setting an outer loop PID control, with the main steam temperature as the controlled variable, and the set value of the inner loop PID control being the regulating control variable to track the main steam temperature. The model predictive control includes: acquiring steam parameters and power generation under different environmental conditions at different future time periods, aiming to track the set target value and minimize the adjustment amount, while satisfying the operating constraints of the steam power generation unit, constructing a rolling optimization control model for the steam power generation unit, and outputting optimized adjustment control variables; The energy flow disturbance feedforward compensation includes: calculating the energy flow fluctuation coefficient in advance using the dynamic energy flow allocation results, and correcting and adjusting the control variables to offset the energy flow disturbance.

[0016] The beneficial effects of this invention are: (1) This invention constructs a full-link digital mapping model of heliostat field, receiver, molten salt thermal storage, steam power generation and power grid, realizes dynamic coupling simulation of multiple devices and multiple links, can reproduce the full operating state of the system under normal and harsh environments, realize simulation analysis of harsh environments, and make up for the defects of physical experiments that cannot simulate extreme working conditions. (2) This invention transforms the qualitative description of the impact of harsh environments on the optical performance of the mirror field into quantitative quantification. Through simulation, a dataset of multiple environmental conditions is generated, clarifying the impact of harsh environments such as sandstorms, strong winds, and high temperatures on the energy flow and optical efficiency of the mirror field. This solves the problem that traditional methods cannot accurately assess the environmental impact. The established time-segmented and environmental condition-segmented prediction models for the energy flow and optical efficiency of the mirror field can predict the energy flow output state of the mirror field in advance, providing a data foundation for the active optimization and advance adjustment of the subsequent dynamic allocation of energy flow, and improving the system's adaptability to environmental fluctuations. (3) This invention combines power grid dispatch instructions and dynamic electricity price signals to optimize steam parameters, realizing time-segmented and multi-objective optimization of steam parameters. It satisfies power grid dispatch instructions and takes into account the operational economy of different electricity price periods, improving the power grid adaptability and power generation revenue of the system. At the same time, it ensures that the optimized steam parameters are adapted to the energy flow output characteristics of the mirror field and the power grid dispatch requirements. (4) The present invention establishes a dynamic energy flow allocation model, realizes the dynamic coordinated allocation of mirror field energy flow and thermal storage device energy flow, solves the problem of fixed energy flow allocation in traditional systems and inability to cope with mirror field energy flow fluctuations, and ensures the stable energy flow input of steam power generation device; and gives full play to the energy flow buffering role of molten salt thermal storage device. When the mirror field energy flow drops sharply in harsh environment, the energy flow is supplemented by the thermal storage device releasing heat. When the mirror field energy flow is excessive, the energy flow is stored by the thermal storage device storing heat. This not only avoids the waste of mirror field energy flow, but also ensures the continuous and stable energy flow input of steam power generation device, and greatly improves the system's operational stability and energy flow utilization rate in harsh environment. (5) The present invention establishes a control model for a steam power generation device, realizes precise matching between energy flow distribution input and steam parameter control, ensures that steam parameters and power generation are stably tracked to the optimal value under different time periods and different environmental conditions, improves the operational stability and parameter control accuracy of the steam power generation device, realizes precise and automatic adjustment of the steam power generation device, and greatly improves the automation level of the system.

[0017] Other features and advantages will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.

[0018] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0019] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0020] Figure 1 This is a flowchart of an optimized operation method for a tower solar thermal power system adapted to harsh environments according to the present invention. Figure 2 This is a flowchart illustrating the method for generating a dataset of multiple environmental conditions according to the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] like Figure 1 As shown in the figure, this embodiment provides an optimized operation method for a tower solar thermal power system adapted to harsh environments, which includes: S1. Establish a digital twin model of a tower solar thermal power system that includes a heliostat field, receiver, molten salt thermal storage device, steam power generation device and power grid; S2. Based on the digital twin model of the tower solar thermal power system, the influence of normal environment and different harsh environment on the optical performance of the heliostat field is simulated and quantitatively analyzed. A multi-environmental condition influence dataset is formed. After analyzing the mirror deformation compensation coefficient and spot offset error of the heliostat field, a mirror field energy flow prediction model and an optical efficiency prediction model are established for the heliostat field reflected to the absorber surface under different environmental conditions at different times. S3. Based on the digital twin model of the tower solar thermal power system, analyze the optimal values ​​of steam parameters of the steam power generation unit under different time periods under the input conditions of grid dispatch command demand and dynamic electricity price signal; S4. Based on the predicted values ​​of mirror field energy flow, optical efficiency, and steam parameters under different time periods and environmental conditions, and combined with the heat storage, heat release, and operating parameters of the molten salt thermal storage device and the absorber, and with the condition of satisfying the steam parameter optimization value, establish a dynamic energy flow distribution model for the molten salt thermal storage device and the absorber, and obtain the dynamic energy flow distribution results for the molten salt thermal storage device and the absorber. S5. Based on the dynamic energy flow distribution results of the molten salt thermal storage device and the absorber, and combined with the optimal steam parameter values, a control model for the steam power generation device is established to obtain the regulation and control parameters of the steam power generation device under different environmental conditions at different times.

[0023] In this embodiment, S1 specifically includes: Based on the actual deployment of the heliostat field, receiver, molten salt thermal storage device, steam power generation device and power grid, each subsystem is modeled separately. Three-dimensional geometric models of each physical entity, physical law models of equipment operation, and energy flow transfer behavior models between devices are established and fused to form a mechanism model of the tower solar thermal power system. The energy flow transfer behavior model between devices is based on light, heat and electrical energy flow as the main line, coupling of each physical entity, and clarifying the dynamic characteristics and constraints of energy flow coupling. Historical operating data, real-time operating data, and environmental data of each device in the tower solar thermal power system are collected, and a digital twin model of the tower solar thermal power system is established after the mechanism model of the tower solar thermal power system is mapped to the real world. A coupling verification mechanism and an online self-calibration mechanism are added to the digital twin model of the tower solar thermal power system. The accuracy of energy flow coupling between physical entities in the model is verified by historical operation data, so that the energy flow coupling error is less than the preset value. When the deviation between the model prediction value and the measured value exceeds the threshold, the model parameters are automatically corrected.

[0024] In practical applications, tower solar thermal power systems also incorporate multi-energy complementarity logic. For example, when the energy flow at the heliostat field is insufficient, gas-fired supplementary combustion units, electric heating units, or other renewable energy generators can be linked as complementary energy flow devices to supplement heat or electricity, further enhancing the system's resilience to fluctuations. If the complementary energy flow devices are activated, subsequent dynamic energy flow allocation must also consider their operation and scheduling. If the environment at the heliostat field is extremely harsh, the energy flow supply of the tower solar thermal power system will be relatively low. In this case, the system will not participate in grid dispatch transactions (power generation costs and revenues must be considered). The system needs to determine whether the current environment is suitable for the equipment to operate. Only when the environment is suitable for the heliostat field equipment will the equipment start and operate normally. When energy flow is low, it can be stored and not currently participate in grid dispatch, but used for peak shaving and frequency regulation needs of the grid in subsequent periods. When energy flow is high, it can participate in grid dispatch in real time.

[0025] like Figure 2 As shown, in this embodiment, in step S2, the influence of normal and different harsh environments on the optical performance of the heliostat field is quantitatively analyzed based on the digital twin model of the tower solar thermal power system, forming a multi-environmental condition influence dataset, including: Define the core characteristic parameters of normal environmental conditions, including the range of solar irradiance, the range of ambient temperature, wind speed, and air cleanliness. The core characteristic parameters for different harsh environmental conditions are defined, including core characteristic parameters for solar radiation intensity, single sandstorm environment, strong wind environment, high temperature or low temperature environment, rainy or hazy environment, and core characteristic parameters for various combinations of environments; the core characteristic parameters for sandstorm environment include sandstorm concentration, sandstorm particle size, and sandstorm adhesion amount; the core characteristic parameters for strong wind environment include average wind speed, gust coefficient, and wind direction; the core characteristic parameters for high temperature or low temperature environment include extreme ambient temperature and temperature change rate; the core characteristic parameters for rainy or hazy environment include relative humidity, visibility, and raindrop or fog droplet size. An environmental condition simulation submodule is embedded into the existing digital twin model of a tower solar thermal power system to perform spatial and temporal simulations of environmental parameters. Historical environmental monitoring data of the location is input into the environmental condition simulation submodule to adjust the environmental parameter coefficients within the module and clarify the physical coupling relationship between the heliostat field and each core environmental characteristic parameter. Simulation boundary conditions are set so that the digital twin model can simulate and reproduce real environmental characteristics. The simulation boundary conditions include the coupling of dust adhesion to the mirror surface, the mechanical coupling of wind speed and mirror deformation, and the coupling of temperature change and thermal expansion and contraction of mirror deformation. In the digital twin model of the tower solar thermal power system, the normal environment is used as the baseline operating condition. The harsh environment simulation is carried out by superimposing single environmental parameters and multi-environment combination parameters in turn through the control variable method. The simulation results are then quantitatively analyzed for the core optical performance indicators to obtain the influence law of different environmental conditions on the optical performance of the heliostat field and form a multi-environment influence dataset.

[0026] In this embodiment, the single-environment parameter simulation involves changing only one type of harsh environmental parameter while keeping other parameters at normal environmental values, quantifying the impact of a single environmental parameter on the core indicators of optical performance; the multi-environment combined parameter superposition simulation combines harsh environmental parameters to quantify the synergistic effect of multiple environmental combined parameters on optical performance; and during the simulation process, hourly or time-period-level simulations are performed for each type of environmental condition to clarify the differences in optical performance changes under different environmental conditions at different times. The core optical performance indicators include specular reflectivity, heliostat normal deviation angle, absorber surface energy flux density distribution characteristics, overall optical efficiency of the heliostat field, and absorber spot offset. Specular reflectivity includes reflectivity attenuation caused by dust adhesion, high-temperature oxidation, and humidity condensation. The heliostat normal deviation angle includes the conversion of specular deformation caused by strong winds and temperature deformation into normal tracking deviation. The absorber surface energy flux density distribution characteristics include the mean, extreme values, and non-uniformity of the absorber surface energy flux density. The overall optical efficiency of the heliostat field includes the conversion efficiency of the incident energy flux to the received energy flux in the absorber. The absorber spot offset includes the spatial offset distance between the actual spot center and the theoretical spot center on the absorber surface.

[0027] In this embodiment, in step S2, after analyzing the heliostat field mirror deformation compensation coefficient and the spot offset error, a prediction model for the mirror field energy flow and optical efficiency of the heliostat field reflected onto the absorber surface under different environmental conditions at different times is established, including: After feature extraction of the dataset affected by multiple environmental conditions, it is divided into environmental condition dimension features, time dimension features, and optical performance dimension features. By performing regression analysis on the features of each dimension, the correlation between the deformation compensation coefficient and the features of each dimension is extracted. The deformation compensation coefficient under different environmental conditions is then calculated to offset the influence of deformation on optical performance, as expressed in the following form: ; , , , , These are the fitting coefficients; Wind speed; This represents the difference between the ambient temperature and the normal temperature. This refers to the amount of sand and dust adhering to the surface. For other environmental operating conditions; Correlation analysis was used to analyze the features of each dimension, extracting features strongly correlated with the spot offset error. Spot offset correction factors were calculated for the spatial offset distance along the x and y axes, and expressed as follows: ; ; This represents the offset of the actual spot center on the absorber surface from the theoretical spot center along the x-axis. This is the theoretical effective receiving radius of the light spot; This represents the offset of the actual spot center and the theoretical spot center on the absorber surface along the y-axis. The mirror deformation compensation coefficient and the spot offset correction factor are mapped with time dimension features and environmental condition dimension features to form a multi-environment-time period optical correction parameter library; Based on the physical behavior of optical propagation in the heliostat field, a basic model for energy flow prediction based on physical mechanisms is constructed. A multi-environment-time period optical correction parameter library is introduced to obtain a corrected heliostat field energy flow computational model. The output value of the heliostat field energy flow computational model and the features of each dimension are used as input features. After training the model using machine learning algorithms, a heliostat field energy flow prediction model is established. Based on the predicted mirror field energy flow and the physical laws of various losses affecting optical efficiency, a physical mechanism model of optical efficiency is constructed. Then, the output value of the physical mechanism model of optical efficiency and the features of each dimension are used as input features, and a machine learning algorithm is used to train the model to establish an optical efficiency prediction model.

[0028] It should be noted that the computational model of the mirror field energy flow is expressed as follows: ; Let t be the actual energy flux density at position (x,y) indicated by the receiver under environmental conditions during time period t; Let t be the theoretical energy flux density at position (x,y) of the receiver under environmental conditions during time period t; This is the mirror deformation compensation coefficient; , This is the offset correction factor for the light spot at the x and y positions; Add an environmental loss factor; This refers to the environmental operating condition type.

[0029] The physical mechanism model of optical efficiency is expressed as follows: ; The reflectivity of the specular surface under normal conditions; Atmospheric transmittance: , These represent the changes in solar altitude angle and azimuth angle, respectively. This refers to stray light reflected from the mirror and stray loss caused by the blockage of the heat absorber.

[0030] The machine learning algorithms used in establishing the mirror field energy flow prediction model and the optical efficiency prediction model include XGBoost, LightGBM, and BP neural network.

[0031] In this embodiment, S3 includes: The power grid dispatch instructions, power upper and lower limit constraints, and ramp rate are transformed into power target values, regulation rates, and time period constraint parameters that can be identified by the digital twin model of the tower solar thermal power system. At the same time, it connects to the real-time electricity price interface of the electricity market to obtain dynamic electricity price signals for each time period. Based on a digital twin model of a tower solar thermal power system, the correlation between different steam parameters and power generation revenue and safety is analyzed. Using optimal power generation revenue and safety as the objective function, intelligent optimization algorithms are employed to obtain the optimal values ​​of steam parameters for different time periods. These intelligent optimization algorithms include particle swarm optimization, genetic algorithms, and gray wolf optimization algorithms.

[0032] In this embodiment, the objective function of maximizing power generation revenue is expressed as: ; Total runtime; The duration of the time period; The power target value required by the scheduling instruction; Let be the power generation efficiency corresponding to the steam parameters during time period t. , , These are steam temperature, pressure, and flow rate, respectively. The dynamic electricity price for time period t; The operating cost of the steam power generation unit during time period t is related to the temperature of the produced steam. ,pressure ,flow Related; Taking optimal security as the objective function, it can be expressed as: ; The equipment safety index corresponding to the steam parameters during time period t; For safety indicator thresholds; For safety weights.

[0033] In this embodiment, S4 includes: The system obtains predicted values ​​of mirror field energy flow and optical efficiency for different time periods and environmental conditions. Combined with the optimal values ​​of steam parameters, the heat storage and release of the molten salt thermal storage device, and the operating parameters of the absorber, it determines whether the optimal values ​​of steam parameters are met after converting light energy into heat energy and then heat energy into electrical energy. If they are met, the energy flow is supplied by the heliostat, absorber, and molten salt thermal storage device in coordination. Using the predicted values ​​of mirror field energy flow and optical efficiency, the optimal values ​​of steam parameters, the heat storage and heat release of the molten salt thermal storage device, and the operating parameters of the absorber under different time periods and environmental conditions as input variables, and with the objective functions of the optimal steam energy flow demand satisfaction and the optimal mirror field energy flow utilization rate, a dynamic energy flow allocation model for the molten salt thermal storage device and the absorber is established. The intelligent optimization algorithm is used to solve for the absorber heat power and the heat storage and heat release power of the molten salt thermal storage device under different time periods and environmental conditions.

[0034] In this embodiment, the objective function of maximizing the satisfaction of steam energy flow demand is expressed as: ; The heat absorption power of the absorber during time period t; The absorption efficiency of the receiver for the energy flow of the heliostat field; The value represents the heat stored and released by the molten salt thermal storage device during time period t. If the value is greater than 0, it indicates that the molten salt thermal storage device releases heat to supplement the energy flow. If the value is less than 0, it indicates that the energy flow of the heliostat field is excessive and the molten salt thermal storage device stores heat. If the value is 0, it indicates that the molten salt thermal storage device does not store or release heat. The heat storage and release efficiency of molten salt thermal storage devices; The steam energy flow demand of the steam power generation unit during period t; The objective function, which is to optimize the energy flow utilization rate of the mirror field, is expressed as: ; The total mirror field energy flow at the receiver inlet during time period t; Let be the field optical efficiency of the mirror during time period t.

[0035] It should be noted that the constraints of the dynamic energy flow allocation model include: energy flow balance constraints, receiver energy flow constraints, receiver power constraints, molten salt thermal storage device heat storage and release constraints, heat storage capacity constraints, and molten salt temperature and level constraints in the molten salt thermal storage device. The solution algorithms for the dynamic energy flow allocation model include particle swarm optimization algorithms and genetic algorithms.

[0036] In this embodiment, S5 includes: The dynamic energy flow distribution results of the molten salt thermal storage device and the absorber are obtained and combined with the historical optimal values ​​of steam parameters and the historical regulation and control parameters of the steam power generation device as model variables; the historical regulation and control parameters include the superheater desuperheating water flow rate, the turbine inlet valve opening degree, the feedwater pump speed, and the condenser cooling water flow rate. To meet the target values ​​of main steam temperature, pressure, flow rate and power generation, the superheater temperature dynamic model, turbine power dynamic model and feedwater pump-steam flow dynamic model are integrated based on the digital twin model of the tower solar thermal power system to form an overall dynamic coupling model of the steam power generation device. The model variables are input into the overall dynamic coupling model of the steam power generation unit. A closed-loop control strategy combining cascade PID control, model predictive control and energy flow disturbance feedforward compensation is adopted to establish the control model of the steam power generation unit. The adjustment and control parameters of the steam power generation unit under different time periods and environmental conditions are obtained by solving the model, so that the steam parameters of the steam power generation unit can stably track the set target value under different time periods and environmental conditions. The cascaded PID control includes: setting an inner loop PID control, with the superheater inlet steam temperature as the controlled variable and the opening of the desuperheating water regulating valve as the regulating control variable, to suppress local disturbances in the desuperheating water pressure and flow rate; and setting an outer loop PID control, with the main steam temperature as the controlled variable, and the set value of the inner loop PID control being the regulating control variable to track the main steam temperature. The model predictive control includes: acquiring steam parameters and power generation under different environmental conditions at different future time periods, aiming to track the set target value and minimize the adjustment amount, while satisfying the operating constraints of the steam power generation unit, constructing a rolling optimization control model for the steam power generation unit, and outputting optimized adjustment control variables; The energy flow disturbance feedforward compensation includes: calculating the energy flow fluctuation coefficient in advance using the dynamic energy flow allocation results, and correcting and adjusting the control variables to offset the energy flow disturbance.

[0037] It should be noted that the superheater temperature dynamic model characterizes the relationship between the desuperheating water flow and the main steam temperature, expressed as: ; The steam temperature; This refers to the steam temperature without desuperheating water regulation. Gain adjustment for cooling water volume; This is the temperature regulation time constant; For the Laplace operator; Reduce the amount of water used to cool the superheater; To adjust the pure time delay; The turbine power dynamic model characterizes the relationship between the steam inlet valve opening and the power generation, and is expressed as: ; This refers to the turbine power. This is the gain for the steam inlet valve opening. This refers to the opening degree of the steam turbine inlet valve; Main steam pressure; Set the target value for the main steam pressure; Main steam temperature; Set the target value for the main steam temperature; This refers to the rated power of the steam turbine. The feedwater pump-steam flow dynamic model characterizes the relationship between feedwater pump speed and steam flow rate, and is expressed as: ; Steam flow rate; For the speed gain of the water pump; This refers to the speed of the water pump. This is the rated steam flow rate.

[0038] The objective is to minimize the target value and the adjustment amount, expressed as: ; N is the number of future time periods; For predicted steam parameters and power generation; To set a target value; To adjust control variables; To adjust the weights of the control variables.

[0039] The energy flow fluctuation coefficient is calculated in advance using the dynamic energy flow allocation results, and the adjustment control variables are corrected accordingly, expressed as follows: ; This is the feedforward adjustment amount; For feedforward gain; The energy flow fluctuation coefficient is expressed as the ratio of the deviation between the current energy flow and the reference energy flow. Adjust control variables based on the basic principles.

[0040] The solution process for the control model of a steam power generation unit includes: 1) Initialize parameters: Read the energy flow input parameters and steam parameters for time period t, set the target values, and initialize the parameters of the controlled object model; 2) Feedforward compensation calculation: Calculate the feedforward adjustment amount based on the energy flow fluctuation coefficient; 3) MPC Rolling Optimization: Optimizes the adjustment control variables under different step lengths in the future and outputs the optimal value of the current step length; 4) PID correction: Substitute the temperature and pressure setpoints output by MPC into the cascade PID control to correct the desuperheating water volume and the speed of the water supply pump; 5) Constraint verification: Verify whether the solution results meet the equipment operation constraints; 6) Time series iteration: Using the result of time period t as the initial value, solve for the parameters of time period t+1, ensuring that the change in adjustment amount between adjacent time periods is less than 10%.

[0041] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can also be implemented in other ways. The system embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0042] Furthermore, the functional modules in the various embodiments of this invention can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part. If the function is implemented as a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory, random access memory, magnetic disks, or optical disks.

[0043] Based on the above-described preferred embodiments of the present invention, and through the foregoing description, those skilled in the art can make various changes and modifications without departing from the inventive concept. The technical scope of this invention is not limited to the contents of the specification, but must be determined according to the scope of the claims.

Claims

1. A method for optimizing the operation of a tower solar thermal power system adapted to harsh environments, characterized in that, It includes: S1. Establish a digital twin model of a tower solar thermal power system that includes a heliostat field, receiver, molten salt thermal storage device, steam power generation device and power grid; S2. Based on the digital twin model of the tower solar thermal power system, the influence of normal environment and different harsh environment on the optical performance of the heliostat field is simulated and quantitatively analyzed. A multi-environmental condition influence dataset is formed. After analyzing the mirror deformation compensation coefficient and spot offset error of the heliostat field, a mirror field energy flow prediction model and an optical efficiency prediction model are established for the heliostat field reflected to the absorber surface under different environmental conditions at different times. S3. Based on the digital twin model of the tower solar thermal power system, analyze the optimal values ​​of steam parameters of the steam power generation unit under different time periods under the input conditions of grid dispatch command demand and dynamic electricity price signal; S4. Based on the predicted values ​​of mirror field energy flow, optical efficiency, and steam parameters under different time periods and environmental conditions, and combined with the heat storage, heat release, and operating parameters of the molten salt thermal storage device and the absorber, and with the condition of satisfying the steam parameter optimization value, establish a dynamic energy flow distribution model for the molten salt thermal storage device and the absorber, and obtain the dynamic energy flow distribution results for the molten salt thermal storage device and the absorber. S5. Based on the dynamic energy flow distribution results of the molten salt thermal storage device and the absorber, and combined with the optimal steam parameter values, a control model for the steam power generation device is established to obtain the regulation and control parameters of the steam power generation device under different environmental conditions at different times.

2. The optimized operation method for a tower solar thermal power system according to claim 1, characterized in that, S1 specifically includes: Based on the actual deployment of the heliostat field, receiver, molten salt thermal storage device, steam power generation device and power grid, each subsystem is modeled separately. Three-dimensional geometric models of each physical entity, physical law models of equipment operation, and energy flow transfer behavior models between devices are established and fused to form a mechanism model of the tower solar thermal power system. The energy flow transfer behavior model between devices is based on light, heat and electrical energy flow as the main line, coupling of each physical entity, and clarifying the dynamic characteristics and constraints of energy flow coupling. Historical operating data, real-time operating data, and environmental data of each device in the tower solar thermal power system are collected, and a digital twin model of the tower solar thermal power system is established after the mechanism model of the tower solar thermal power system is mapped to the real world. A coupling verification mechanism and an online self-calibration mechanism are added to the digital twin model of the tower solar thermal power system. The accuracy of energy flow coupling between physical entities in the model is verified by historical operation data, so that the energy flow coupling error is less than the preset value. When the deviation between the model prediction value and the measured value exceeds the threshold, the model parameters are automatically corrected.

3. The optimized operation method for a tower solar thermal power system according to claim 1, characterized in that, In S2, the influence of normal and different harsh environments on the optical performance of the heliostat field is quantitatively analyzed based on the digital twin model of the tower solar thermal power system, forming a multi-environmental condition influence dataset, including: Define the core characteristic parameters of normal environmental conditions, including the range of solar irradiance, the range of ambient temperature, wind speed, and air cleanliness. The core characteristic parameters for different harsh environmental conditions are defined, including core characteristic parameters for solar radiation intensity, single sandstorm environment, strong wind environment, high temperature or low temperature environment, rainy or hazy environment, and core characteristic parameters for various combinations of environments; the core characteristic parameters for sandstorm environment include sandstorm concentration, sandstorm particle size, and sandstorm adhesion amount; the core characteristic parameters for strong wind environment include average wind speed, gust coefficient, and wind direction; the core characteristic parameters for high temperature or low temperature environment include extreme ambient temperature and temperature change rate; the core characteristic parameters for rainy or hazy environment include relative humidity, visibility, and raindrop or fog droplet size. An environmental condition simulation submodule is embedded into the existing digital twin model of a tower solar thermal power system to perform spatial and temporal simulations of environmental parameters. Historical environmental monitoring data of the location is input into the environmental condition simulation submodule to adjust the environmental parameter coefficients within the module and clarify the physical coupling relationship between the heliostat field and each core environmental characteristic parameter. Simulation boundary conditions are set so that the digital twin model can simulate and reproduce real environmental characteristics. The simulation boundary conditions include the coupling of dust adhesion to the mirror surface, the mechanical coupling of wind speed and mirror deformation, and the coupling of temperature change and thermal expansion and contraction of mirror deformation. In the digital twin model of the tower solar thermal power system, the normal environment is used as the baseline operating condition. The harsh environment simulation is carried out by superimposing single environmental parameters and multi-environment combination parameters in turn through the control variable method. The simulation results are then quantitatively analyzed for the core optical performance indicators to obtain the influence law of different environmental conditions on the optical performance of the heliostat field and form a multi-environment influence dataset.

4. The optimized operation method for a tower solar thermal power system according to claim 3, characterized in that, The single-environment parameter simulation involves changing only one type of harsh environmental parameter while keeping other parameters at normal environmental values, quantifying the impact of a single environmental parameter on core optical performance indicators. The multi-environment combined parameter superposition simulation combines harsh environmental parameters, quantifying the synergistic effect of multiple combined environmental parameters on optical performance. Furthermore, during the simulation process, hourly or time-period-level simulations are conducted for each type of environmental condition to clarify the differences in optical performance under different environmental conditions at different times. The core optical performance indicators include specular reflectivity, heliostat normal deviation angle, absorber surface energy flux density distribution characteristics, overall optical efficiency of the heliostat field, and absorber spot offset. Specular reflectivity includes reflectivity attenuation caused by dust adhesion, high-temperature oxidation, and humidity condensation. The heliostat normal deviation angle includes the conversion of specular deformation caused by strong winds and temperature deformation into normal tracking deviation. The absorber surface energy flux density distribution characteristics include the mean, extreme values, and non-uniformity of the absorber surface energy flux density. The overall optical efficiency of the heliostat field includes the conversion efficiency of the incident energy flux to the received energy flux in the absorber. The absorber spot offset includes the spatial offset distance between the actual spot center and the theoretical spot center on the absorber surface.

5. The optimized operation method for a tower solar thermal power system according to claim 1, characterized in that, In step S2, after analyzing the heliostat field mirror deformation compensation coefficient and spot offset error, a prediction model for the mirror field energy flow and optical efficiency of the heliostat field reflected onto the absorber surface under different time periods and environmental conditions is established, including: After feature extraction of the dataset affected by multiple environmental conditions, it is divided into environmental condition dimension features, time dimension features, and optical performance dimension features. By performing regression analysis on the features of each dimension, the correlation between the deformation compensation coefficient and the features of each dimension is extracted. The deformation compensation coefficient under different environmental conditions is then calculated to offset the influence of deformation on optical performance, as expressed in the following form: ； , , , , These are the fitting coefficients; Wind speed; This represents the difference between the ambient temperature and the normal temperature. This refers to the amount of sand and dust adhering to the surface. For other environmental operating conditions; Correlation analysis was used to analyze the features of each dimension, extracting features strongly correlated with the spot offset error. Spot offset correction factors were calculated for the spatial offset distance along the x and y axes, and expressed as follows: ； ； This represents the offset of the actual spot center on the absorber surface from the theoretical spot center along the x-axis. This is the theoretical effective receiving radius of the light spot; This represents the offset of the actual spot center and the theoretical spot center on the absorber surface along the y-axis. The mirror deformation compensation coefficient and the spot offset correction factor are mapped with time dimension features and environmental condition dimension features to form a multi-environment-time period optical correction parameter library; Based on the physical behavior of optical propagation in the heliostat field, a basic model for energy flow prediction based on physical mechanisms is constructed. A multi-environment-time period optical correction parameter library is introduced to obtain a corrected heliostat field energy flow computational model. The output value of the heliostat field energy flow computational model and the features of each dimension are used as input features. After training the model using machine learning algorithms, a heliostat field energy flow prediction model is established. Based on the predicted mirror field energy flow and the physical laws of various losses affecting optical efficiency, a physical mechanism model of optical efficiency is constructed. Then, the output value of the physical mechanism model of optical efficiency and the features of each dimension are used as input features, and a machine learning algorithm is used to train the model to establish an optical efficiency prediction model.

6. The optimized operation method for a tower solar thermal power system according to claim 1, characterized in that, S3 includes: The power grid dispatch instructions, power upper and lower limit constraints, and ramp rate are transformed into power target values, regulation rates, and time period constraint parameters that can be identified by the digital twin model of the tower solar thermal power system. At the same time, it connects to the real-time electricity price interface of the electricity market to obtain dynamic electricity price signals for each time period. Based on the digital twin model of a tower solar thermal power system, the correlation between different steam parameters and power generation revenue and safety is analyzed. With the optimal power generation revenue and safety as the objective function, an intelligent optimization algorithm is used to solve for the optimal values ​​of steam parameters at different time periods.

7. The optimized operation method for a tower solar thermal power system according to claim 6, characterized in that, The objective function, which is to optimize power generation revenue, is expressed as: ； Total runtime; The duration of the time period; The power target value required by the scheduling instruction; Let be the power generation efficiency corresponding to the steam parameters during time period t. , , These are steam temperature, pressure, and flow rate, respectively. The dynamic electricity price for time period t; The operating cost of the steam power generation unit during time period t is related to the temperature of the produced steam. ,pressure ,flow Related; Taking optimal security as the objective function, it can be expressed as: ； The equipment safety index corresponding to the steam parameters during time period t; For safety indicator thresholds; For safety weights.

8. The optimized operation method for a tower solar thermal power system according to claim 1, characterized in that, S4 includes: The system obtains predicted values ​​of mirror field energy flow and optical efficiency for different time periods and environmental conditions. Combined with the optimal values ​​of steam parameters, the heat storage and release of the molten salt thermal storage device, and the operating parameters of the absorber, it determines whether the optimal values ​​of steam parameters are met after converting light energy into heat energy and then heat energy into electrical energy. If they are met, the energy flow is supplied by the heliostat, absorber, and molten salt thermal storage device in coordination. Using the predicted values ​​of mirror field energy flow and optical efficiency, the optimal values ​​of steam parameters, the heat storage and heat release of the molten salt thermal storage device, and the operating parameters of the absorber under different time periods and environmental conditions as input variables, and with the objective functions of the optimal steam energy flow demand satisfaction and the optimal mirror field energy flow utilization rate, a dynamic energy flow allocation model for the molten salt thermal storage device and the absorber is established. The intelligent optimization algorithm is used to solve for the absorber heat power and the heat storage and heat release power of the molten salt thermal storage device under different time periods and environmental conditions.

9. The optimized operation method for a tower solar thermal power system according to claim 8, characterized in that, The objective function, which is to optimize the satisfaction of steam energy flow demand, is expressed as: ； The heat absorption power of the absorber during time period t; The absorption efficiency of the receiver for the energy flow of the heliostat field; The value represents the heat stored and released by the molten salt thermal storage device during time period t. If the value is greater than 0, it indicates that the molten salt thermal storage device releases heat to supplement the energy flow. If the value is less than 0, it indicates that the energy flow of the heliostat field is excessive and the molten salt thermal storage device stores heat. If the value is 0, it indicates that the molten salt thermal storage device does not store or release heat. The heat storage and release efficiency of molten salt thermal storage devices; The steam energy flow demand of the steam power generation unit during period t; The objective function, which is to optimize the energy flow utilization rate of the mirror field, is expressed as: ； The total mirror field energy flow at the receiver inlet during time period t; Let be the field optical efficiency of the mirror during time period t.

10. The optimized operation method for a tower solar thermal power system according to claim 1, characterized in that, S5 includes: The dynamic energy flow distribution results of the molten salt thermal storage device and the absorber are obtained and combined with the historical optimal values ​​of steam parameters and the historical regulation and control parameters of the steam power generation device as model variables; the historical regulation and control parameters include the superheater desuperheating water flow rate, the turbine inlet valve opening degree, the feedwater pump speed, and the condenser cooling water flow rate. To meet the target values ​​of main steam temperature, pressure, flow rate and power generation, the superheater temperature dynamic model, turbine power dynamic model and feedwater pump-steam flow dynamic model are integrated based on the digital twin model of the tower solar thermal power system to form an overall dynamic coupling model of the steam power generation device. The model variables are input into the overall dynamic coupling model of the steam power generation unit. A closed-loop control strategy combining cascade PID control, model predictive control and energy flow disturbance feedforward compensation is adopted to establish the control model of the steam power generation unit. The adjustment and control parameters of the steam power generation unit under different time periods and environmental conditions are obtained by solving the model, so that the steam parameters of the steam power generation unit can stably track the set target value under different time periods and environmental conditions. The cascaded PID control includes: setting an inner loop PID control, with the superheater inlet steam temperature as the controlled variable and the opening of the desuperheating water regulating valve as the regulating control variable, to suppress local disturbances in the desuperheating water pressure and flow rate; and setting an outer loop PID control, with the main steam temperature as the controlled variable, and the set value of the inner loop PID control being the regulating control variable to track the main steam temperature. The model predictive control includes: acquiring steam parameters and power generation under different environmental conditions at different future time periods, aiming to track the set target value and minimize the adjustment amount, while satisfying the operating constraints of the steam power generation unit, constructing a rolling optimization control model for the steam power generation unit, and outputting optimized adjustment control variables; The energy flow disturbance feedforward compensation includes: calculating the energy flow fluctuation coefficient in advance using the dynamic energy flow allocation results, and correcting and adjusting the control variables to offset the energy flow disturbance.

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