Steam extraction energy storage regulation optimization method and system using high-temperature molten salt heat storage system

Abstract

The application provides a steam extraction energy storage regulation optimization method and system using a high-temperature molten salt heat storage system, relates to the technical field of energy storage, and comprises the following steps: a thermal coupling mathematical model of a high-temperature molten salt heat storage system and a steam turbine unit is established, key parameters affecting the performance of steam extraction energy storage are determined, a dynamic optimization regulation model of steam extraction energy storage is established with the coal consumption rate of power generation as an optimization target, the dynamic optimization regulation model is solved by using an improved non-dominated sorting genetic algorithm, an optimal solution set of operation parameters of steam extraction energy storage under different working conditions is obtained, and a dynamic optimization regulation strategy library of steam extraction energy storage is formed; according to the prediction result of the power grid load, an optimal regulation strategy is selected from the obtained dynamic optimization regulation strategy library, the steam extraction process of the steam turbine and the molten salt heat storage process are cooperatively controlled, and flexible operation and efficient peak shaving of the thermal power generating unit under the condition of dynamic fluctuation of the power grid load are realized.

Description

TECHNICAL FIELD

[0001] The present application relates to energy storage technology, and particularly to a steam extraction energy storage regulation optimization method and system using a high-temperature molten salt heat storage system. BACKGROUND

[0002] With the increasing proportion of renewable energy in the power system, the peak-valley difference and load fluctuation of the power grid also increase, which puts higher requirements on the regulation ability of traditional thermal power generating units. In order to improve the stability and reliability of the power grid, and enhance the economy and flexibility of thermal power generating units, the steam extraction energy storage regulation technology of high-temperature molten salt heat storage system has emerged as the times require.

[0003] Traditional thermal power generating units face the following problems during operation:

[0004] Limited peak regulation capacity: The output regulation range of thermal power generating units is narrow, making it difficult to adapt to large fluctuations in grid load. Frequent start-stop increases equipment wear and tear: In order to respond to load changes, units need to be started and stopped frequently, leading to increased equipment wear and tear and shortened lifespan. Low energy utilization efficiency: During low-load operation, unit efficiency is significantly reduced, resulting in energy waste. Poor economy: Frequent regulation and off-design operation can increase coal consumption, reducing the economy of the unit. High environmental pressure: Load fluctuations lead to unstable combustion, increasing pollutant emissions.

[0005] In order to solve the above problems, researchers have proposed a scheme that combines high-temperature molten salt heat storage systems with thermal power generating units. The basic principle of this scheme is to extract part of the steam from the steam turbine during the low-load valley of the power grid and use it to heat molten salt, storing the heat energy; during the peak load, the stored heat energy is used to generate steam to drive the steam turbine to generate electricity. This method can improve the peak regulation capacity of the unit, reduce the number of starts and stops, and improve energy utilization efficiency.

[0006] However, existing steam extraction energy storage regulation technology still has the following shortcomings:

[0007] Insufficient optimization of steam extraction parameters: The selection of steam extraction pressure, temperature and flow rate often relies on experience and lacks scientific optimization methods. Poor control of heat storage process: The control strategy for molten salt temperature and flow rate is not precise enough, affecting the heat storage efficiency. Poor coordination with the unit: The coordinated control of the steam extraction system and the main unit is not perfect enough, which may affect the safe and stable operation of the unit. Insufficient consideration of economy: The existing regulation strategy does not fully consider economic factors such as electricity price changes and equipment wear and tear. Limited prediction capability: Lack of accurate prediction of grid load and renewable energy output affects the development of regulation strategies. Insufficient real-time optimization capability: The existing system is difficult to dynamically adjust the strategy according to real-time operating conditions and market conditions.

[0008] Therefore, it is urgent to develop an optimization method which can comprehensively consider various factors, realize the coordinated control of steam extraction parameters, heat storage process and unit, so as to fully exert the potential of high-temperature molten salt heat storage system and improve the regulation ability and economic benefits of thermal power generating unit. SUMMARY

[0009] The embodiment of the present application provides a steam extraction energy storage regulation optimization method and system using a high-temperature molten salt heat storage system, which can solve the problems in the prior art.

[0010] The first aspect of the embodiment of the present application,

[0011] The embodiment of the present application provides a steam extraction energy storage regulation optimization method using a high-temperature molten salt heat storage system, which comprises the following steps:

[0012] A thermodynamic coupling mathematical model of the high-temperature molten salt heat storage system and the steam turbine unit is established, wherein the thermodynamic coupling mathematical model comprises thermodynamic models of high-pressure cylinders, medium-pressure cylinders and low-pressure cylinders of the steam turbine, a heat transfer and flow model of the high-temperature molten salt heat storage device, and mass and energy balance equations of the steam turbine steam extraction and heat storage system; key parameters affecting the steam extraction energy storage performance are determined, wherein the key parameters comprise high-pressure cylinder steam extraction pressure, steam extraction temperature, steam extraction flow rate, high-temperature molten salt heat storage temperature, molten salt flow rate, and reheater outlet temperature;

[0013] A dynamic optimization regulation model of the steam extraction energy storage is established with the power generation coal consumption rate as an optimization target, wherein the objective function of the dynamic optimization regulation model is to minimize the power generation coal consumption rate, and the constraint conditions comprise upper and lower limit ranges of the steam extraction flow rate, the molten salt flow rate, the steam extraction temperature, the molten salt heat storage temperature and the reheater outlet temperature; the established dynamic optimization regulation model is solved by using an improved non-dominated sorting genetic algorithm to obtain an optimal solution set of the steam extraction energy storage operating parameters under different working conditions, thereby forming a dynamic optimization regulation strategy library of the steam extraction energy storage;

[0014] According to the power grid load prediction result, an optimal regulation strategy is selected from the obtained dynamic optimization regulation strategy library, the set values of the high-pressure cylinder steam extraction pressure, the steam extraction temperature and the steam extraction flow rate are delivered to a steam turbine steam extraction control system, and the set values of the molten salt heat storage temperature and the molten salt flow rate are delivered to a high-temperature molten salt heat storage control system; the steam turbine steam extraction process and the molten salt heat storage process are controlled in coordination, thereby realizing the flexible operation and efficient peak shaving of the thermal power generating unit under the dynamic fluctuation of the power grid load.

[0015] In an alternative embodiment,

[0016] A thermodynamic coupling mathematical model of the high-temperature molten salt heat storage system and the steam turbine unit is established, wherein the thermodynamic coupling mathematical model comprises thermodynamic models of high-pressure cylinders, medium-pressure cylinders and low-pressure cylinders of the steam turbine, a heat transfer and flow model of the high-temperature molten salt heat storage device, and mass and energy balance equations of the steam turbine steam extraction and heat storage system, which comprise:

[0017] establishing a steam turbine high-pressure cylinder thermodynamic model, the steam turbine high-pressure cylinder thermodynamic model comprising a steam flow calculation formula based on Stodola's elliptic law and an isentropic efficiency correction formula considering performance changes at partial load; calculating steam flow and corrected isentropic efficiency of the steam turbine high-pressure cylinder according to the steam turbine high-pressure cylinder thermodynamic model;

[0018] establishing a high-temperature molten salt heat storage device heat transfer and flow model, the high-temperature molten salt heat storage device heat transfer and flow model comprising a heat exchanger heat transfer equation based on the logarithmic mean temperature difference method and a modified Dittus-Boelter formula considering molten salt flow characteristics; calculating heat transfer power and Nusselt number of molten salt flow of the high-temperature molten salt heat storage device using the high-temperature molten salt heat storage device heat transfer and flow model;

[0019] establishing a mass and energy balance equation of a steam extraction and heat storage system, the mass and energy balance equation comprising a mass balance equation, an energy balance equation at the extraction point, and an energy accumulation rate equation of the high-temperature molten salt heat storage system; calculating steam flow, specific enthalpy at each point of the system, and energy change rate of the high-temperature molten salt heat storage system based on the mass and energy balance equation;

[0020] combining the steam turbine high-pressure cylinder thermodynamic model, the high-temperature molten salt heat storage device heat transfer and flow model, and the mass and energy balance equation of the steam extraction and heat storage system to construct a complete thermodynamic coupling mathematical model.

[0021] In an alternative embodiment,

[0022] establishing a dynamic optimization and regulation model of steam extraction and energy storage with the power generation coal consumption rate as the optimization target, the objective function of the dynamic optimization and regulation model being minimization of the power generation coal consumption rate, the constraint conditions including upper and lower limit ranges of the extraction flow, the molten salt flow, the extraction temperature, the high-temperature molten salt heat storage temperature, and the reheater outlet temperature; solving the established dynamic optimization and regulation model by using an improved non-dominated sorting genetic algorithm to obtain an optimal solution set of the steam extraction and energy storage operating parameters under different working conditions, thereby constructing a dynamic optimization and regulation strategy library of steam extraction and energy storage, which includes:

[0023] initializing a population to generate an initial population containing decision variables, the decision variables including the high-pressure cylinder extraction pressure, the extraction temperature, the extraction flow, the high-temperature molten salt heat storage temperature, the molten salt flow, and the reheater outlet temperature;

[0024] non-dominantly sorting the initial population to divide individuals into different non-dominant layers; generating uniformly distributed reference points in a standardized target space; calculating the distance of each individual to the nearest reference point, and selecting individuals into the next generation population according to the distance; calculating a population diversity index, which is the average value of the Euclidean distance of all individuals to the population center;

[0025] Adaptively adjust the crossover rate and the mutation rate according to the population diversity index, wherein the crossover rate decreases with the increase of the population diversity index, the mutation rate increases with the increase of the population diversity index, and the adjustment of the crossover rate and the mutation rate both adopt an exponential function form; perform crossover and mutation operations on the selected individuals to generate a child population;

[0026] Select part of the individuals with a preset probability to perform simulated annealing optimization, generate new solutions in the neighborhood of the selected individuals, calculate the energy difference, calculate the probability of accepting the new solution according to the energy difference and the current temperature, and update the temperature according to a preset cooling coefficient;

[0027] Calculate the improved crowding degree, including calculating the target space crowding degree and the decision space crowding degree, and obtaining the comprehensive crowding degree through weighted average, wherein the target space crowding degree and the decision space crowding degree consider the distance of adjacent individuals in the target space and the decision space respectively;

[0028] Divide the population into multiple sub-populations, the number of sub-populations being equal to the number of available processor cores, perform target function evaluation, constraint processing, non-dominated sorting and crowding calculation on each sub-population in a separate processor core in parallel; aggregate the evaluation results of each sub-population, perform global non-dominated sorting and selection operation; merge the parent population and the child population, and perform non-dominated sorting on the merged population to select the first N individuals to form the next generation population;

[0029] Determine whether the maximum number of iterations is reached or the convergence condition is met, if yes, output the optimization result, and if no, return to the step of calculating the distance of each individual to the nearest reference point; according to the output optimization result, determine the optimal operation parameter of the steam extraction energy storage system, and realize dynamic optimization control of the system.

[0030] In an alternative embodiment,

[0031] According to the power grid load prediction result, select the optimal adjustment strategy from the obtained dynamic optimization adjustment strategy library, and deliver the set values of the high-pressure cylinder steam extraction pressure, steam extraction temperature and steam extraction flow to the steam turbine extraction control system, and deliver the set values of the molten salt heat storage temperature and molten salt flow to the high-temperature molten salt heat storage control system, comprising:

[0032] A deep learning model adopting a long short-term memory network and an attention mechanism is used for power grid load prediction, the deep learning model comprising an input layer, a long short-term memory network layer, an attention layer, a fully connected layer and an output layer, wherein the long short-term memory network layer is provided with 3 layers, each layer containing 128 neurons, and the fully connected layer comprises 2 layers, containing 64 and 32 neurons respectively;

[0033] A double deep Q network algorithm is used to construct a dynamic optimization adjustment strategy library, the double deep Q network algorithm includes an action value network and a target network, the action value network and the target network are both composed of 3 full connection layers, the number of neurons are 256, 128 and 64 respectively, and the activation function is a ReLU function; the reward function of the dynamic optimization adjustment strategy library considers the power generation efficiency improvement, the peak regulation capacity improvement, the equipment wear cost and the operation cost;

[0034] Based on the power grid load prediction result, an optimal adjustment strategy is selected from the dynamic optimization adjustment strategy library by using a fuzzy comprehensive evaluation method, the fuzzy comprehensive evaluation method includes establishing a fuzzy relationship matrix and determining an index weight, wherein the index weight is determined by an analytic hierarchy process;

[0035] The high-pressure cylinder extraction pressure set value, the extraction temperature set value and the extraction flow set value in the optimal adjustment strategy are transmitted to the steam turbine extraction control system through an OPC UA protocol, and the molten salt storage temperature set value and the molten salt flow set value are transmitted to the high-temperature molten salt storage control system through the OPC UA protocol, the OPC UA protocol uses an X.509 certificate for identity authentication, and uses an AES-256 encryption algorithm to encrypt communication data;

[0036] In an optional implementation,

[0037] The steam turbine extraction process and the molten salt storage process are cooperatively controlled to realize flexible operation and efficient peak regulation of the thermal power generating unit under dynamic fluctuation of the power grid load, including:

[0038] A model predictive control method is used to realize cooperative control of the steam turbine extraction process and the molten salt storage process, a target function of the model predictive control method includes an output tracking error term and a control increment penalty term, and constraint conditions include an extraction pressure range, an extraction temperature range, an extraction flow range, a molten salt temperature range, a molten salt flow range and a control increment limit;

[0039] An improved fast gradient method is used to solve the model predictive control optimization problem, the improved fast gradient method includes converting the original problem into a quadratic programming problem, using a Nesterov accelerated gradient method for iterative solution, and using a hot start strategy; a recursive least squares algorithm is introduced to update system model parameters online, a forgetting factor of the recursive least squares algorithm is set to 0.98, and adaptive updating of the system model is realized.

[0040] In an optional implementation,

[0041] An improved fast gradient method is used to solve the model predictive control optimization problem, the improved fast gradient method includes converting the original problem into a quadratic programming problem, using a Nesterov accelerated gradient method for iterative solution, and using a hot start strategy includes:

[0042] The Nesterov accelerated gradient method comprises the following sub-steps:

[0043] initializing a decision variable vector, an auxiliary variable vector, a step length and a momentum parameter; calculating the gradient of the objective function; updating the decision variable vector and the auxiliary variable vector; projecting the updated decision variable vector into the feasible region; dynamically adjusting the step length by using the Barzilai-Borwein method; and dynamically adjusting the momentum parameter according to the iteration number;

[0044] implementing a hot start strategy, taking the optimal solution of the previous control period as the initial value of the current control period, and specifically comprising:

[0045] saving the optimal solution of the previous control period; constructing a new initial decision variable vector, wherein the first N-1 control increments come from the optimal solution of the previous period, and the last control increment is set to zero; and performing preheating iteration using a large step length to quickly approach the optimal solution.

[0046] A second aspect of the embodiment of the application,

[0047] The application provides a steam extraction energy storage regulation optimization system using a high-temperature molten salt heat storage system, comprising:

[0048] A first unit is configured to establish a thermal coupling mathematical model of the high-temperature molten salt heat storage system and the steam turbine unit, wherein the thermal coupling mathematical model comprises thermodynamic models of high-pressure cylinders, medium-pressure cylinders and low-pressure cylinders of the steam turbine, a heat transfer and flow model of the high-temperature molten salt heat storage device, and mass and energy balance equations of the steam extraction and heat storage system; and key parameters affecting the performance of the steam extraction energy storage are determined, wherein the key parameters include the high-pressure cylinder extraction pressure, the extraction temperature, the extraction flow rate, the high-temperature molten salt heat storage temperature, the molten salt flow rate, and the reheater outlet temperature.

[0049] A second unit is configured to establish a dynamic optimization regulation model of the steam extraction energy storage with the power generation coal consumption rate as an optimization target, wherein the objective function of the dynamic optimization regulation model is to minimize the power generation coal consumption rate, and the constraint conditions include upper and lower limit ranges of the extraction flow rate, the molten salt flow rate, the extraction temperature, the molten salt heat storage temperature and the reheater outlet temperature; the established dynamic optimization regulation model is solved by using an improved non-dominated sorting genetic algorithm to obtain an optimal solution set of the steam extraction energy storage operating parameters under different working conditions, thereby forming a dynamic optimization regulation strategy library of the steam extraction energy storage.

[0050] The third unit is configured to select an optimal adjustment strategy from the obtained dynamic optimization adjustment strategy library according to the power grid load prediction result, and deliver the set values of the high-pressure cylinder extraction steam pressure, extraction steam temperature and extraction steam flow to a steam turbine extraction control system and deliver the set values of the molten salt heat storage temperature and molten salt flow to a high-temperature molten salt heat storage control system; the steam turbine extraction process and the molten salt heat storage process are cooperatively controlled to realize flexible operation and efficient peak shaving of the thermal power generating unit under dynamic fluctuation of the power grid load.

[0051] A third aspect of the embodiments of the present application,

[0052] An electronic device is provided, comprising:

[0053] A processor;

[0054] A memory for storing processor-executable instructions;

[0055] The processor is configured to invoke the instructions stored in the memory to execute the method described above.

[0056] A fourth aspect of the embodiments of the present application,

[0057] A computer-readable storage medium is provided, which stores computer program instructions, and the computer program instructions are executed by a processor to implement the method described above.

[0058] By constructing a dynamic optimization adjustment strategy library, the system can quickly select the most suitable operating parameters according to the current operating conditions and the predicted future load changes. This predictive control strategy can respond to load changes in advance, further improving the system's adjustment ability and economy. In summary, this steam extraction energy storage dynamic optimization adjustment method based on the improved non-dominated sorting genetic algorithm provides strong technical support for the efficient operation of the high-temperature molten salt heat storage system, helps to improve the flexibility and economy of the thermal power generating unit, and contributes to the stable operation of the power grid and the large-scale access of renewable energy.

[0059] The steam extraction energy storage system can dynamically adjust the operating parameters according to the power grid load prediction results, ensuring the safe and stable operation of the system while maximizing the power generation efficiency and peak shaving capacity. For example, during the predicted load low period, the system may increase the extraction amount, increase the molten salt heat storage temperature, and prepare for subsequent peak shaving. During the load peak period, the system will reduce the extraction amount and increase the molten salt heat release, thereby increasing the power generation capacity of the unit. This intelligent control strategy not only improves the flexibility and economy of the thermal power generating unit, but also provides strong support for the stable operation of the power grid and the large-scale access of renewable energy. BRIEF DESCRIPTION OF DRAWINGS

[0060] Figure 1A flowchart of the steam extraction energy storage regulation optimization method using the high-temperature molten salt heat storage system according to an embodiment of the present application is shown in FIG. 1.

[0061] Figure 2 A structure diagram of the steam extraction energy storage regulation optimization system using the high-temperature molten salt heat storage system according to an embodiment of the present application is shown in FIG. 2. DETAILED DESCRIPTION

[0062] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described below in connection with the drawings of the embodiments of the present application. Obviously, the described embodiments are only a part but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0063] The technical solutions of the present application will be described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and some embodiments may not be described again for the same or similar concepts or processes.

[0064] Figure 1 A flowchart of the steam extraction energy storage regulation optimization method using the high-temperature molten salt heat storage system according to an embodiment of the present application is shown in FIG. 1. Figure 1 The method comprises the following steps:

[0065] S101. A thermodynamic coupling mathematical model of the high-temperature molten salt heat storage system and the steam turbine unit is established, the thermodynamic coupling mathematical model comprises thermodynamic models of high-pressure cylinders, medium-pressure cylinders and low-pressure cylinders of the steam turbine, a heat transfer and flow model of the high-temperature molten salt heat storage device, and mass and energy balance equations of the steam extraction and heat storage system; key parameters affecting the performance of the steam extraction energy storage are determined, the key parameters include steam extraction pressure, steam extraction temperature, steam extraction flow rate, high-temperature molten salt heat storage temperature, molten salt flow rate, and reheater outlet temperature;

[0066] S102. A dynamic optimization regulation model of the steam extraction energy storage is established with the power generation coal consumption rate as the optimization target, the objective function of the dynamic optimization regulation model is to minimize the power generation coal consumption rate, the constraint conditions include upper and lower limit ranges of the steam extraction flow rate, the molten salt flow rate, the steam extraction temperature, the molten salt heat storage temperature and the reheater outlet temperature; the established dynamic optimization regulation model is solved by using an improved non-dominated sorting genetic algorithm to obtain an optimal solution set of the steam extraction energy storage operating parameters under different working conditions, thereby forming a dynamic optimization regulation strategy library of the steam extraction energy storage;

[0067] S103. According to the power grid load prediction result, an optimal adjustment strategy is selected from the obtained dynamic optimization adjustment strategy library, and the set values of the high-pressure cylinder extraction steam pressure, extraction steam temperature and extraction steam flow are transmitted to the steam turbine extraction control system, and the set values of the molten salt heat storage temperature and molten salt flow are transmitted to the high-temperature molten salt heat storage control system; the steam turbine extraction process and the molten salt heat storage process are cooperatively controlled to realize flexible operation and efficient peak shaving of the thermal power generating unit under the dynamic fluctuation of the power grid load.

[0068] In an alternative embodiment,

[0069] A thermodynamic coupling mathematical model of the high-temperature molten salt heat storage system and the steam turbine unit is established, and the thermodynamic coupling mathematical model includes a thermodynamic model of a high-pressure cylinder, a medium-pressure cylinder and a low-pressure cylinder of the steam turbine, a heat transfer and flow model of the high-temperature molten salt heat storage device, and a mass and energy balance equation of the steam turbine extraction and heat storage system, and the mass and energy balance equation includes:

[0070] A thermodynamic model of the high-pressure cylinder of the steam turbine is established, and the thermodynamic model of the high-pressure cylinder of the steam turbine includes a steam flow calculation formula based on the Stodola elliptic law and a isentropic efficiency correction formula considering the performance change under partial load; according to the thermodynamic model of the high-pressure cylinder of the steam turbine, the steam flow and the corrected isentropic efficiency of the high-pressure cylinder of the steam turbine are calculated;

[0071] A heat transfer and flow model of the high-temperature molten salt heat storage device is established, and the heat transfer and flow model of the high-temperature molten salt heat storage device includes a heat transfer equation of a heat exchanger based on the logarithmic mean temperature difference method and a modified Dittus-Boelter formula considering the flow characteristics of the molten salt; the heat transfer power of the high-temperature molten salt heat storage device and the Nusselt number of the molten salt flow are calculated by using the heat transfer and flow model of the high-temperature molten salt heat storage device;

[0072] A mass and energy balance equation of the steam turbine extraction and heat storage system is established, and the mass and energy balance equation includes a mass balance equation, an energy balance equation of an extraction point and an energy accumulation rate equation of the high-temperature molten salt heat storage system; based on the mass and energy balance equation, the steam flow, specific enthalpy of each point in the system and the energy change rate of the high-temperature molten salt heat storage system are calculated;

[0073] The thermodynamic model of the high-pressure cylinder of the steam turbine, the heat transfer and flow model of the high-temperature molten salt heat storage device and the mass and energy balance equation of the steam turbine extraction and heat storage system are combined to construct a complete thermodynamic coupling mathematical model.

[0074] Exemplarily, first, a thermodynamic model of the high-pressure cylinder of a steam turbine is established. This model includes a steam flow calculation formula based on Stodola's elliptic law and an isentropic efficiency correction formula considering the performance change at partial load. Stodola's elliptic law is used to describe the relationship between the inlet and outlet pressures of the high-pressure cylinder of the steam turbine and the steam flow. The isentropic efficiency correction formula takes into account the change in efficiency of the steam turbine when operating at partial load. Through these two formulas, the steam flow and the corrected isentropic efficiency of the high-pressure cylinder of the steam turbine can be calculated.

[0075] For example, for a steam turbine with a rated power of 300 MW, the inlet steam parameters of its high-pressure cylinder are 16.7 MPa and 538°C, and the outlet pressure is 3.8 MPa. Using Stodola's elliptic law, the steam flow at rated load is calculated to be 250 kg / s. Through the isentropic efficiency correction formula, the isentropic efficiency at 75% load is obtained to be 87%, which is lower than the 89% at rated load.

[0076] Next, a heat transfer and flow model of the high-temperature molten salt thermal storage device is established. This model includes a heat transfer equation of the heat exchanger based on the logarithmic mean temperature difference method and a modified Dittus-Boelter formula considering the flow characteristics of the molten salt. The logarithmic mean temperature difference method is used to calculate the heat transfer power of the heat exchanger, and the modified Dittus-Boelter formula is used to calculate the Nusselt number of the molten salt flow, thereby obtaining the convective heat transfer coefficient.

[0077] Taking a high-temperature molten salt thermal storage device with a capacity of 1000 MWh as an example, a mixture of 60% NaNO3 and 40% KNO3 is used as the thermal storage medium. Through the logarithmic mean temperature difference method, the heat transfer power at the design condition is calculated to be 50 MW. Using the modified Dittus-Boelter formula, the Nusselt number is calculated to be 120 when the molten salt flow rate is 2 m / s, and the corresponding convective heat transfer coefficient is 6000 W / (m 2 ·K).

[0078] Then, the mass and energy balance equations of the steam turbine extraction and thermal storage system are established. This includes the mass balance equation, the energy balance equation of the extraction point, and the energy accumulation rate equation of the high-temperature molten salt thermal storage system. The mass balance equation ensures the balance of steam flow at each extraction point, the energy balance equation ensures the energy conservation of each part of the system, and the energy accumulation rate equation describes the change of energy in the thermal storage system over time.

[0079] Taking a system with three levels of extraction as an example, the first level of extraction pressure is 3.8 MPa, and the flow rate is 20 kg / s; the second level of extraction pressure is 1.7 MPa, and the flow rate is 15 kg / s; the third level of extraction pressure is 0.5 MPa, and the flow rate is 10 kg / s. The steam flow rate between each level can be calculated through the mass balance equation. The energy balance equation shows that the total extraction energy is 100 MW under the rated operating condition. The energy accumulation rate equation shows that the energy of the thermal storage system increases at a rate of 45 MW during the energy storage process.

[0080] Finally, the thermodynamic model of the high-pressure cylinder of the steam turbine, the heat transfer and flow model of the high-temperature molten salt thermal storage device, and the mass and energy balance equations of the steam extraction and thermal storage system are combined to establish a complete thermodynamic coupling mathematical model. This comprehensive model can describe the thermodynamic behavior of the entire system, including the performance of each cylinder of the steam turbine, the heat transfer characteristics of the thermal storage device, and the mass and energy balance of the entire system.

[0081] Through this thermodynamic coupling mathematical model, the system performance under different operating conditions can be simulated. For example, during the low load period of the power grid, the extraction amount can be increased to 25 kg / s, and at this time the thermal storage power can reach 55 MW. During the peak load period, the extraction amount can be reduced to 5 kg / s, and at the same time, the stored thermal energy can be used to generate an additional 15 MW of power output.

[0082] This thermodynamic coupling mathematical model lays the foundation for subsequent optimization control. By adjusting variables such as extraction parameters and molten salt flow, the system performance can be optimized, the energy utilization efficiency can be improved, and the power grid peak shaving capability can be enhanced. For example, based on the predicted load curve of the power grid, an optimal extraction strategy can be developed in advance to maximize economic benefits while ensuring the safe and stable operation of the unit.

[0083] In addition, the model can also be used for dynamic response analysis of the system. By introducing the time factor, the transient characteristics of the system under rapid load changes can be studied to provide a basis for the development of real-time control strategies. For example, the analysis of how the thermal storage system cooperates with the steam turbine to respond quickly and maintain system stability when the load jumps from 50% to 100% in 5 minutes can be performed.

[0084] In an alternative embodiment,

[0085] A dynamic optimization and regulation model for extraction and energy storage is established with the objective of minimizing the coal consumption rate of power generation. The objective function of the dynamic optimization and regulation model is to minimize the coal consumption rate of power generation, and the constraint conditions include the upper and lower limit ranges of extraction flow rate, molten salt flow rate, extraction temperature, molten salt storage temperature, and reheater outlet temperature. An improved non-dominated sorting genetic algorithm is applied to solve the established dynamic optimization and regulation model to obtain an optimal solution set of extraction and energy storage operating parameters under different operating conditions, thereby forming a dynamic optimization and regulation strategy library for extraction and energy storage.

[0086] initializing a population, generating an initial population containing decision variables including high-pressure cylinder extraction steam pressure, extraction steam temperature, extraction steam flow, high-temperature molten salt heat storage temperature, molten salt flow and reheater outlet temperature;

[0087] non-dominant sorting is performed on the initial population, individuals are divided into different non-dominant layers; uniformly distributed reference points are generated in the normalized objective space; the distance of each individual to the nearest reference point is calculated, and the individual is selected into the next generation population according to the distance; a population diversity index is calculated, which is the average of the Euclidean distance of all individuals to the population center;

[0088] According to the population diversity index, the crossover rate and the mutation rate are adaptively adjusted, wherein the crossover rate decreases with the increase of the population diversity index, the mutation rate increases with the increase of the population diversity index, and the adjustment of the crossover rate and the mutation rate adopts an exponential function form; the selected individuals are subjected to crossover and mutation operations to generate a child population;

[0089] A part of individuals are selected for simulated annealing optimization with a preset probability, new solutions are generated in the neighborhood of the selected individuals, the energy difference is calculated, the probability of accepting new solutions is calculated according to the energy difference and the current temperature, and the temperature is updated according to a preset cooling coefficient;

[0090] An improved crowding degree is calculated, including calculating the objective space crowding degree, the decision space crowding degree, and obtaining the comprehensive crowding degree by weighted average, wherein the objective space crowding degree and the decision space crowding degree consider the distance of adjacent individuals in the objective space and the decision space respectively;

[0091] The population is divided into multiple sub-populations, the number of sub-populations is equal to the number of available processor cores, and the objective function evaluation, constraint processing, non-dominant sorting and crowding degree calculation are performed on each sub-population in a separate processor core in parallel; the evaluation results of each sub-population are summarized, global non-dominant sorting and selection operations are performed; the parent population and the child population are merged, and the merged population is subjected to non-dominant sorting, and the first N individuals are selected to form the next generation population;

[0092] It is judged whether the maximum iteration number is reached or the convergence condition is met, if yes, the optimization result is output, if not, the step of calculating the distance of each individual to the nearest reference point is returned; according to the output optimization result, the optimal operation parameters of the extraction energy storage system are determined, and the dynamic optimization control of the system is realized.

[0093] An exemplary dynamic optimization model is established. The objective function of the model is to minimize the coal consumption rate of power generation, and the constraint conditions include the upper and lower limit ranges of the extraction steam flow, the molten salt flow, the extraction steam temperature, the molten salt storage temperature, and the reheater outlet temperature. For example, for a 300 MW unit, the extraction steam flow range can be set to 0-50 kg / s, the molten salt flow range to 100-500 kg / s, the extraction steam temperature range to 300-500 °C, the molten salt storage temperature range to 290-565 °C, and the reheater outlet temperature range to 535-565 °C. These constraints ensure that the system operates within a safe and stable range.

[0094] Next, the established dynamic optimization model is solved by using an improved non-dominated sorting genetic algorithm. The specific implementation steps of the algorithm are as follows:

[0095] Initialize the population: generate an initial population containing decision variables. The decision variables include the high-pressure cylinder extraction steam pressure, the extraction steam temperature, the extraction steam flow, the high-temperature molten salt storage temperature, the molten salt flow, and the reheater outlet temperature. Assuming that the population size is 100, each individual contains 6 decision variables. For example, the possible decision variable values of an individual are: extraction steam pressure 3.5 MPa, extraction steam temperature 450 °C, extraction steam flow 30 kg / s, molten salt storage temperature 520 °C, molten salt flow 300 kg / s, and reheater outlet temperature 550 °C.

[0096] Non-dominated sorting of the initial population: divide the individuals into different non-dominated layers. Generate uniformly distributed reference points in the standardized objective space, and assume that 100 reference points are generated. Calculate the distance of each individual to the nearest reference point, and select the individual to enter the next generation population according to the distance. Calculate the population diversity index, which is the average of the Euclidean distances of all individuals to the population center. For example, the calculated population diversity index is 0.8.

[0097] Adaptively adjust the crossover rate and the mutation rate according to the population diversity index: the crossover rate decreases and the mutation rate increases with the increase of the population diversity index, both of which are adjusted in the form of an exponential function. For example, when the population diversity index is 0.8, the calculated crossover rate is 0.7 and the mutation rate is 0.1. Perform crossover and mutation operations on the selected individuals to generate a child population.

[0098] Select a part of the individuals to perform simulated annealing optimization with a preset probability: assume that the preset probability is 0.2, i.e., randomly select 20 individuals from the 100 individuals to perform simulated annealing optimization. Generate new solutions within the neighborhood of the selected individuals, calculate the energy difference, and calculate the probability of accepting the new solution according to the energy difference and the current temperature. For example, the initial temperature is set to 100 and the cooling coefficient is 0.95. After each iteration, the temperature is updated to the current temperature multiplied by the cooling coefficient.

[0099] Calculate the improved crowding distance: including calculating the target space crowding distance, decision space crowding distance, and getting the comprehensive crowding distance by weighted average. The target space crowding distance and decision space crowding distance consider the distance of adjacent individuals in the target space and decision space respectively. Assume that the target space crowding distance weight is 0.6 and the decision space crowding distance weight is 0.4.

[0100] Divide the population into multiple sub-populations to realize parallel computing: assuming that the number of available processor cores is 4, then 100 individuals are evenly distributed into 4 sub-populations, each containing 25 individuals. For each sub-population, perform target function evaluation, constraint processing, non-dominated sorting and crowding distance calculation on a separate processor core. After completion, aggregate the evaluation results of each sub-population, perform global non-dominated sorting and selection operations.

[0101] Merge the parent population and the child population, and perform non-dominated sorting on the merged population to select the top N individuals to form the next generation population. For example, if the parent and child populations each have 100 individuals, after merging there are 200 individuals, then select the top 100 individuals in the order of sorting as the next generation population.

[0102] Determine whether the maximum number of iterations is reached or the convergence condition is met: assuming that the maximum number of iterations is set to 1000 and the convergence condition is that the variation amplitude of the optimal solution for 50 consecutive generations is less than 1%. If the condition is met, output the optimization result, otherwise return to the step of calculating the distance of each individual to the nearest reference point and continue iterative optimization.

[0103] According to the output optimization result, determine the optimal operating parameters of the steam extraction energy storage system to realize dynamic optimization control of the system. For example, the optimization result may show that under certain operating conditions, the optimal operating parameters are: extraction pressure 3.2 MPa, extraction temperature 460℃, extraction flow rate 35 kg / s, molten salt storage temperature 540℃, molten salt flow rate 350 kg / s, reheater outlet temperature 555℃. This set of parameters can achieve the lowest coal consumption rate for power generation while meeting all the constraints.

[0104] Through this dynamic optimization adjustment method, the optimal operating parameters of the steam extraction energy storage system can be quickly obtained for different load conditions and grid demands. For example, during the low load valley period of the grid, the extraction flow rate can be increased to 40 kg / s and the molten salt storage temperature can be increased to 560℃ to maximize the energy storage effect. While during the load peak period, the extraction flow rate can be reduced to 10 kg / s and the molten salt storage temperature can be reduced to 500℃ to improve the power generation efficiency.

[0105] An important feature of this optimization method is the ability to consider multiple objectives and constraints simultaneously. While the primary optimization objective is the coal consumption rate of power generation, other performance indicators such as system response speed and equipment life are also taken into account through non-dominated sorting and improved crowding calculation. This makes the optimization results more comprehensive and practical.

[0106] In addition, the parallel computing characteristics of this method enable it to quickly handle large-scale optimization problems. For complex power systems, it may be necessary to consider tens of decision variables and constraints, and traditional optimization methods may take several hours or even days to obtain results. Through parallel computing, the calculation time can be greatly shortened, achieving near real-time optimization control.

[0107] Finally, by constructing a dynamic optimization adjustment strategy library, the system can quickly select the most suitable operating parameters according to the current operating conditions and predicted future load changes. This predictive control strategy can anticipate load changes and further improve the system's adjustment ability and economy.

[0108] In summary, this dynamic optimization adjustment method for extraction storage based on improved non-dominated sorting genetic algorithm provides strong technical support for the efficient operation of high-temperature molten salt thermal storage systems, helping to improve the flexibility and economy of thermal power generating units, and contributing to the stable operation of the power grid and the large-scale integration of renewable energy.

[0109] In an alternative embodiment,

[0110] According to the power grid load prediction result, the optimal adjustment strategy is selected from the obtained dynamic optimization adjustment strategy library, and the set values of the high-pressure cylinder extraction steam pressure, extraction steam temperature, and extraction steam flow are delivered to the steam turbine extraction control system, and the set values of the molten salt storage temperature and molten salt flow are delivered to the high-temperature molten salt storage control system, including:

[0111] A deep learning model using a long short-term memory network and an attention mechanism is used for power grid load prediction, the deep learning model including an input layer, a long short-term memory network layer, an attention layer, a fully connected layer, and an output layer, wherein the long short-term memory network layer is set to 3 layers, each containing 128 neurons, and the fully connected layer includes 2 layers, containing 64 and 32 neurons respectively;

[0112] A double deep Q network algorithm is used to construct a dynamic optimization adjustment strategy library, the double deep Q network algorithm including an action value network and a target network, both of which are composed of 3 fully connected layers with neuron counts of 256, 128, and 64 respectively, and the activation function is ReLU function; the reward function of the dynamic optimization adjustment strategy library considers the improvement of power generation efficiency, the improvement of peak shaving capacity, the cost of equipment wear and tear, and the operating cost;

[0113] Based on the power grid load prediction result, the optimal adjustment strategy is selected from the dynamic optimization adjustment strategy library by using a fuzzy comprehensive evaluation method, which includes establishing a fuzzy relationship matrix and determining index weights, wherein the index weights are determined by an analytic hierarchy process;

[0114] The high-pressure cylinder extraction steam pressure set value, extraction steam temperature set value, and extraction steam flow set value in the optimal adjustment strategy are transmitted to the steam turbine extraction control system through the OPC UA protocol, and the molten salt heat storage temperature set value and molten salt flow set value are transmitted to the high-temperature molten salt heat storage control system through the OPC UA protocol, wherein the OPC UA protocol uses an X.509 certificate for identity authentication and uses an AES-256 encryption algorithm to encrypt communication data.

[0115] Illustratively, a deep learning model using a long short-term memory network and an attention mechanism is used for power grid load prediction. The structure of the model includes an input layer, a long short-term memory network layer, an attention layer, a fully connected layer, and an output layer. The input layer receives historical load data, weather information, date characteristics, and other related factors. The long short-term memory network layer is set to 3 layers, each containing 128 neurons, to capture the long-term dependence of time series data. The attention layer is used to weight the features at different time steps, improving the model's focus on key information. The fully connected layer includes 2 layers, containing 64 and 32 neurons, respectively, to further extract features. The output layer gives the load prediction results for the next 24 hours.

[0116] For example, for power grid load prediction in a certain city, the input data includes 7 days of hourly load data (168 data points), temperature, humidity, and wind speed forecasts for the current and next 24 hours, and date types (weekday, weekend, holiday). Model training uses 3 years of historical data, uses an Adam optimizer, sets the learning rate to 0.001, and the batch size to 64. After 1000 rounds of training, the model's mean absolute percentage error (MAPE) on the test set reaches 2.5%, and the root mean square error (RMSE) is 15 MW.

[0117] Next, a double deep Q network algorithm is used to build a dynamic optimization adjustment strategy library. The algorithm includes an action value network and a target network, both of which have the same structure and are composed of 3 fully connected layers with 256, 128, and 64 neurons, respectively, and a ReLU function as the activation function. The action value network is used to evaluate the value of each action in the current state, and the target network is used to generate target Q values to improve the stability of training.

[0118] The state space of the dynamic optimization adjustment strategy library includes the current load level, load change trend, current values of various parameters of the steam extraction energy storage system, etc. The action space includes the adjustment amount of the high-pressure cylinder steam extraction pressure, steam extraction temperature, steam extraction flow, molten salt heat storage temperature, and molten salt flow. The reward function comprehensively considers the power generation efficiency improvement, peak regulation capacity improvement, equipment wear cost, and operation cost. For example, the weight of the power generation efficiency improvement can be set to 0.4, the weight of the peak regulation capacity improvement can be set to 0.3, the weight of the equipment wear cost can be set to 0.2, and the weight of the operation cost can be set to 0.1.

[0119] During the training process, an ε-greedy strategy is used for exploration, and the initial ε value is set to 1, which is gradually decayed to 0.01 as the training progresses. The experience replay buffer size is set to 10,000, and the target network parameters are updated every 4 steps. The training lasts for 1 million steps, and finally a model is obtained that can quickly give the optimal adjustment strategy according to the current state.

[0120] Based on the power grid load prediction results, a fuzzy comprehensive evaluation method is used to select the optimal adjustment strategy from the dynamic optimization adjustment strategy library. First, a fuzzy relationship matrix is established, and the evaluation indexes include power generation efficiency, peak regulation capacity, equipment life impact, and economy. For each candidate strategy, the membership values of each index are given according to expert experience and historical data. For example, the membership values of a certain strategy may be: power generation efficiency 0.8, peak regulation capacity 0.7, equipment life impact 0.6, and economy 0.75.

[0121] The index weights are determined by the analytic hierarchy process. First, a judgment matrix is constructed to compare the relative importance of each index. Assuming that the weight vector obtained through expert evaluation is: power generation efficiency 0.35, peak regulation capacity 0.30, equipment life impact 0.20, and economy 0.15. Multiply the weight vector by the fuzzy relationship matrix to get the comprehensive evaluation score of each candidate strategy. Select the strategy with the highest score as the optimal adjustment strategy.

[0122] Finally, the parameter set values in the optimal adjustment strategy are transmitted to the corresponding control system through the OPC UA protocol. OPC UA (OPC Unified Architecture) is an industrial communication protocol with good cross-platform and security. In this embodiment, X.509 certificates are used for identity authentication to ensure that the identities of both parties are real and trustworthy. The communication data is encrypted using the AES-256 encryption algorithm to protect the security of data transmission.

[0123] Specifically, the high-pressure cylinder extraction steam pressure set value, extraction steam temperature set value, and extraction steam flow set value are transmitted to the steam turbine extraction control system via the OPC UA protocol. For example, the optimal strategy at a certain time may be: extraction steam pressure 3.2 MPa, extraction steam temperature 460°C, and extraction steam flow 35 kg / s. These parameters are encoded as OPC UA data points and sent to the OPC UA server of the steam turbine control system through an encrypted channel.

[0124] Similarly, the molten salt thermal storage temperature set value and molten salt flow set value are transmitted to the high-temperature molten salt thermal storage control system via the OPC UA protocol. For example, the molten salt thermal storage temperature in the optimal strategy may be 540°C, and the molten salt flow may be 350 kg / s. These data are also transmitted to the controller of the thermal storage system through a secure OPC UA channel.

[0125] After the control system receives these set values, it will adjust the corresponding actuators, such as adjusting valve opening, adjusting pump speed, etc., through PID controller or more advanced model predictive control algorithm, so that the actual operating parameters of the system gradually approach the set values. At the same time, the actual operating data will be fed back to the upper control system through the OPC UA protocol for monitoring and the next round of optimization calculation.

[0126] This dynamic optimization and adjustment method based on deep learning and reinforcement learning has the following advantages:

[0127] The power grid load prediction model can accurately capture the load change trend, providing a reliable basis for optimization decisions. Through the long short-term memory network and attention mechanism, the model can effectively handle long-term dependencies and focus on key information, improving prediction accuracy.

[0128] The dynamic optimization and adjustment strategy library constructed by the double deep Q network algorithm can learn complex nonlinear decision rules and adapt to the optimal control strategy under different working conditions. By considering the reward function of multiple objectives, the comprehensive optimization of power generation efficiency, peak shaving capacity, and economy is realized.

[0129] The fuzzy comprehensive evaluation method introduces expert knowledge in the strategy selection process, improving the explainability and reliability of the decision. Through the analytic hierarchy process, the index weight is determined, making the evaluation result more objective and reasonable.

[0130] Using the OPC UA protocol for data transmission ensures the interoperability and security of system integration. X.509 certificate authentication and AES-256 encryption protect the secure transmission of control instructions, preventing unauthorized access and data tampering.

[0131] By this method, the steam extraction energy storage system can dynamically adjust the operating parameters according to the load prediction results of the power grid, while ensuring the safe and stable operation of the system, maximizing the power generation efficiency and peak shaving capacity. For example, when the load trough period is predicted, the system may increase the steam extraction amount, increase the molten salt storage temperature, and prepare for subsequent peak shaving. During the load peak period, the system will reduce the steam extraction amount, increase the molten salt heat release, and increase the power generation capacity of the unit.

[0132] This intelligent control strategy not only improves the flexibility and economy of the thermal power generating unit, but also provides strong support for the stable operation of the power grid and the large-scale access of renewable energy.

[0133] In an alternative embodiment,

[0134] The coordinated control of the steam turbine extraction process and the molten salt heat storage process realizes the flexible operation and efficient peak shaving of the thermal power generating unit under the dynamic fluctuation of the power grid load, which mainly includes:

[0135] The model predictive control method is used to realize the coordinated control of the steam turbine extraction process and the molten salt heat storage process, the objective function of the model predictive control method includes an output tracking error term and a control increment penalty term, and the constraint conditions include the extraction pressure range, the extraction temperature range, the extraction flow range, the molten salt temperature range, the molten salt flow range and the control increment limit;

[0136] The improved fast gradient method is used to solve the model predictive control optimization problem, the improved fast gradient method includes converting the original problem into a quadratic programming problem, using the Nesterov accelerated gradient method for iterative solution, and using a hot start strategy; The recursive least squares algorithm is introduced to update the system model parameters online, the forgetting factor of the recursive least squares algorithm is set to 0.98, and the adaptive update of the system model is realized.

[0137] Exemplarily, the present application provides a model predictive control-based coordinated control method of a steam extraction energy storage system of a thermal power generating unit, which is used to realize flexible operation and efficient peak shaving under the dynamic fluctuation of the power grid load. The method mainly includes using model predictive control to realize the coordinated control of the steam turbine extraction process and the molten salt heat storage process, and using an improved fast gradient method to solve the optimization problem.

[0138] Firstly, the model predictive control method is established. The objective function of this method includes output tracking error terms and control increment penalty terms. The output tracking error terms are used to measure the deviation between the actual output and the desired output of the system, and the control increment penalty terms are used to limit the drastic changes of the control quantity, ensuring the smooth operation of the system. The constraint conditions include the extraction pressure range, extraction temperature range, extraction flow range, molten salt temperature range, molten salt flow range and control increment limit. For example, for a 300MW unit, the extraction pressure range can be set to 2-5MPa, the extraction temperature range to 300-500℃, the extraction flow range to 0-50kg / s, the molten salt temperature range to 290-565℃, and the molten salt flow range to 100-500kg / s. The control increment limit can be set to not more than 10% of the rated value per control period.

[0139] Next, the improved fast gradient method is used to solve the model predictive control optimization problem. The method includes the following steps:

[0140] Convert the original problem to a quadratic programming problem: by linearizing and discretizing the objective function and constraint conditions, the original nonlinear optimization problem is converted into a standard quadratic programming problem. This step can greatly simplify the problem solving process and improve the calculation efficiency.

[0141] Use Nesterov accelerated gradient method for iterative solution: Nesterov accelerated gradient method is an improved gradient descent algorithm with faster convergence speed. In practical application, the initial step size can be set to 0.01 and the maximum iteration number to 100. By continuously updating the gradient and momentum term, the optimal solution is gradually approached.

[0142] Adopt hot start strategy: using the optimization result of the last time as the initial value of the current time optimization can significantly reduce the number of iterations and speed up the solution speed. For example, if the optimal extraction flow of the last time is 30kg / s, this value can be used as the initial guess value for the current time optimization.

[0143] In order to adapt to the changes of system parameters and improve the control precision, the recursive least squares algorithm is introduced to update the system model parameters online. The forgetting factor of the recursive least squares algorithm is set to 0.98, which means that the algorithm will gradually "forget" the old data and rely more on the new observation data, so as to realize the adaptive update of the system model.

[0144] The specific implementation process is as follows:

[0145] Firstly, the current state of the system and the load prediction value within a certain period of time in the future are obtained. For example, the current extraction pressure, temperature, flow, and molten salt temperature, flow, etc. state variables can be obtained, and the load prediction value every 15 minutes within 4 hours in the future can be obtained.

[0146] Then, the system response within the future time window is calculated based on the system model and the predicted load change. Assuming the prediction time window is 4 hours and the control period is 15 minutes, the system response at 16 time points needs to be calculated.

[0147] Next, the objective function is constructed. The objective function includes an output tracking error term and a control increment penalty term. The output tracking error can be defined as the sum of squared differences between the predicted output and the desired output, and the control increment penalty term can be defined as the sum of squared changes in control variables. The control performance and system stability can be balanced by adjusting the weights of these two terms. For example, the weight of the output tracking error term can be set to 1 and the weight of the control increment penalty term can be set to 0.1.

[0148] The constraint conditions are constructed. According to the physical limitations and safety requirements of the system, the value range and change rate limit of each variable are set. For example, the change rate of extraction pressure can be limited to no more than 0.1 MPa per minute.

[0149] The improved fast gradient method is used to solve the optimization problem. First, the problem is transformed into a standard quadratic programming form, and then the Nesterov accelerated gradient method is used for iterative solution. In each iteration, the objective function value and gradient are calculated, and the search direction and step size are updated. When the maximum number of iterations is reached or the convergence condition is met, the iteration is stopped.

[0150] After obtaining the optimal control sequence, only the control action of the first control period is executed. For example, if the optimization result shows that the optimal extraction flow sequence within the next 4 hours is [35, 36, 38, 37,...] kg / s, only the 35 kg / s set value of the first control period is executed.

[0151] While executing the control action, the recursive least squares algorithm is used to update the system model parameters. New system input and output data are collected, the prediction error is calculated, and then the model parameters are updated. For example, if it is found that the actual extraction flow response is slower than the model prediction, the time constant parameter in the model will be adjusted accordingly.

[0152] The above steps are repeated, and the optimization problem is solved again at the beginning of each control period to realize rolling optimization control.

[0153] Through this collaborative control method, the extraction process of the steam turbine and the molten salt heat storage process can be flexibly adjusted according to the dynamic changes of the power grid load. For example, when the power grid load suddenly decreases, the controller will increase the extraction amount, increase the molten salt flow and storage temperature, and realize the rapid transfer and storage of energy. Conversely, when the load suddenly increases, the controller will reduce the extraction amount, reduce the molten salt flow, release the stored heat energy, and increase the power output.

[0154] A key advantage of this coordinated control strategy is its ability to anticipate load changes. By considering load forecasts for a future period, the controller can make adjustments in advance, avoiding the system's lagging response. For example, if a load peak is predicted to occur in 2 hours, the controller will increase the molten salt's heat storage in advance, preparing for the upcoming high load operation.

[0155] In addition, by updating the system model parameters online, this method can adapt to slow changes in system characteristics, such as performance changes caused by equipment aging, seasonal changes, and other factors. This ensures the long-term stability of control performance.

[0156] In summary, this model predictive control-based coordinated control method optimizes the steam extraction process and molten salt heat storage process simultaneously, enabling flexible operation and efficient peak shaving of thermal power generating units under dynamic load fluctuations in the power grid. It not only improves the unit's peak shaving capability and economic efficiency, but also provides strong support for the stable operation of the power grid and the large-scale integration of renewable energy.

[0157] In an alternative embodiment,

[0158] The improved fast gradient method for solving the model predictive control optimization problem includes transforming the original problem into a quadratic programming problem, using the Nesterov accelerated gradient method for iterative solution, and adopting a warm start strategy, including:

[0159] The Nesterov accelerated gradient method includes the following sub-steps:

[0160] Initialize the decision variable vector, auxiliary variable vector, step size, and momentum parameter; calculate the gradient of the objective function; update the decision variable vector and auxiliary variable vector; project the updated decision variable vector into the feasible region; dynamically adjust the step size using the Barzilai-Borwein method; and dynamically adjust the momentum parameter according to the iteration number;

[0161] Implement the warm start strategy, taking the optimal solution of the previous control period as the initial value of the current control period, specifically including:

[0162] Save the optimal solution of the previous control period; construct a new initial decision variable vector, where the first N-1 control increments come from the optimal solution of the previous period, and the last control increment is set to zero; perform preheating iterations with a larger step size to quickly approach the optimal solution.

[0163] Exemplarily, the present application provides a method for solving model predictive control optimization problems using an improved fast gradient method, which comprises converting the original problem into a quadratic programming problem, using the Nesterov accelerated gradient method for iterative solution, and adopting a warm start strategy. This method can effectively improve the solution speed and convergence, and is suitable for real-time control of steam extraction and energy storage systems of thermal power generating units.

[0164] Firstly, the original model predictive control optimization problem is converted into a standard quadratic programming problem. This step includes linearizing the system model, discretizing the prediction horizon, constructing the objective function and constraint conditions. For example, for a system with two control variables of steam extraction flow and molten salt flow, the control sequence in the future 4 hours can be represented as a 96-dimensional decision variable vector (assuming a control period of 5 minutes). The objective function can be represented as a quadratic form plus a linear term of the decision variable vector, and the constraint conditions are represented as linear inequalities.

[0165] Next, the improved Nesterov accelerated gradient method is used for iterative solution. This process includes the following key steps:

[0166] Initialize the decision variable vector, auxiliary variable vector, step size and momentum parameter. The decision variable vector can be initialized as a zero vector or the optimal solution of the last period. The auxiliary variable vector is initialized as the decision variable vector. The initial step size can be set to a small value, such as 0.01. The initial value of the momentum parameter can be set to 0.9.

[0167] Calculate the gradient of the objective function. This step requires calculating the partial derivative of the objective function with respect to the decision variable. For a quadratic programming problem, the gradient can be efficiently calculated by matrix operations.

[0168] Update the decision variable vector and the auxiliary variable vector. Using the calculated gradient, combined with the current step size and momentum parameter, update the decision variable vector and the auxiliary variable vector. This step is the core of the Nesterov accelerated gradient method, which accelerates convergence by introducing a momentum term.

[0169] Project the updated decision variable vector into the feasible region. This step ensures that the updated solution satisfies all the constraint conditions. For simple boundary constraints, the values that exceed the range can be directly truncated to the boundary. For more complex constraints, a quadratic programming subproblem may need to be solved.

[0170] Adjust the step size dynamically using the Barzilai-Borwein method. This method adaptively adjusts the step size according to the gradient change of the last two iterations, which can significantly improve the convergence speed. Specifically, the new step size can be estimated according to the difference between the current iteration and the last iteration divided by the difference between the decision variables.

[0171] Adjust the momentum parameter dynamically according to the number of iterations. Gradually increasing the momentum parameter can maintain stability in the early stage and accelerate convergence in the later stage. For example, the momentum parameter can be gradually increased from 0.9 initially to 0.99.

[0172] Implement the hot start strategy, taking the optimal solution of the last control period as the initial value of the current control period. This strategy includes the following steps:

[0173] Save the optimal solution of the last control period. At the end of each control period, save the optimal decision variable vector obtained to the memory or database.

[0174] Construct a new initial decision variable vector. Use the saved optimal solution of the last period to construct the new initial value. Specifically, for a problem with a control horizon of N, the last N-1 control increments in the optimal solution of the last period can be used as the first N-1 elements of the new initial value, and the last element is set to zero. This construction method ensures the continuity of the control sequence.

[0175] Use a larger step size for preheating iterations. Before formally starting the Nesterov accelerated gradient method, a larger step size (such as 0.1) can be used for several preheating iterations. This helps quickly approach the optimal solution and reduces the total number of iterations.

[0176] The following is a specific data case to illustrate the implementation process of this method:

[0177] Suppose we have a problem with a control horizon of 12 (corresponding to a prediction horizon of 1 hour and a control period of 5 minutes), and the control variables include steam flow and molten salt flow. Then the dimension of the decision variable vector is 24 (12*2).

[0178] In the initialization stage, we can set the decision variable vector to [30, 30,..., 30, 200, 200,..., 200], where the first 12 elements represent steam flow (units: kg / s) and the last 12 elements represent molten salt flow (units: kg / s). The initial step size is set to 0.01 and the momentum parameter is set to 0.9.

[0179] In each iteration, we first calculate the gradient of the objective function. Suppose in a certain iteration, the calculated gradient vector is [-0.5, -0.3,..., 0.2, 0.1, 0.3,..., -0.1].

[0180] Then, we update the decision variable vector and the auxiliary variable vector using this gradient. After updating, suppose the new decision variable vector obtained is [30.5, 30.3,..., 29.8, 199.9, 199.7,..., 200.1].

[0181] Next, we need to project this vector into the feasible region. Assuming the range of steam extraction flow rate is [0, 50] kg / s and the range of molten salt flow rate is [100, 300] kg / s, the projected result remains unchanged since all values are within the feasible range.

[0182] Adjust the step size using the Barzilai-Borwein method. Suppose the calculated new step size is 0.015.

[0183] Adjust the momentum parameter dynamically. If it is the 50th iteration, we can adjust the momentum parameter to 0.95.

[0184] Repeat the above process until the maximum number of iterations (e.g., 1000) is reached or the convergence condition (e.g., the relative change in the objective function value is less than 1e-6) is met.

[0185] At the beginning of the next control period, we use the warm start strategy. Suppose the optimal solution obtained in the last period is [32, 33, 35,..., 40, 210, 215, 220,..., 250]. We construct a new initial decision variable vector as [33, 35,..., 40, 0, 215, 220,..., 250, 0], where the last two elements are set to 0.

[0186] Use a larger step size (e.g., 0.1) for 10 warm-up iterations, then switch back to the normal iteration process.

[0187] Through this improved fast gradient method combined with Nesterov acceleration and warm start strategy, we can quickly solve the model predictive control optimization problem in each control period and provide real-time optimal control sequences for the steam extraction and energy storage system of the thermal power generating unit. This method not only improves the solving speed, but also enhances the robustness and convergence of the algorithm, which can effectively deal with the nonlinear and time-varying characteristics of the system model, and realize flexible operation and efficient peak shaving under dynamic load fluctuations of the power grid.

[0188] Figure 2 The structure diagram of the steam extraction and energy storage regulation optimization system of the high-temperature molten salt heat storage system according to the embodiment of the present application is shown in Figure 2 The system comprises:

[0189] The first unit is used for establishing a thermal coupling mathematical model of the high-temperature molten salt heat storage system and the steam turbine unit, the thermal coupling mathematical model comprising thermodynamic models of high-pressure cylinders, medium-pressure cylinders and low-pressure cylinders of the steam turbine, a heat transfer flow model of the high-temperature molten salt heat storage device, and mass and energy balance equations of steam extraction and the heat storage system; key parameters affecting the steam extraction and energy storage performance are determined, the key parameters comprising steam extraction pressure, steam extraction temperature, steam extraction flow rate, high-temperature molten salt heat storage temperature, molten salt flow rate, and reheater outlet temperature;

[0190] The second unit is used for establishing a dynamic optimization and regulation model of the steam extraction and energy storage with the power generation coal consumption rate as an optimization target, the objective function of the dynamic optimization and regulation model being minimization of the power generation coal consumption rate, the constraint conditions comprising upper and lower limit ranges of the steam extraction flow rate, the molten salt flow rate, the steam extraction temperature, the molten salt heat storage temperature and the reheater outlet temperature; the dynamic optimization and regulation model is solved by using an improved non-dominated sorting genetic algorithm to obtain an optimal solution set of the steam extraction and energy storage operation parameters under different working conditions, thereby forming a dynamic optimization and regulation strategy library of the steam extraction and energy storage.

[0191] The third unit is used for selecting an optimal regulation strategy from the obtained dynamic optimization and regulation strategy library according to a power grid load prediction result, and delivering set values of the high-pressure cylinder steam extraction pressure, the steam extraction temperature and the steam extraction flow rate to a steam extraction control system of the steam turbine, and delivering set values of the molten salt heat storage temperature and the molten salt flow rate to a high-temperature molten salt heat storage control system; the steam extraction process of the steam turbine and the molten salt heat storage process are cooperatively controlled to realize flexible operation and efficient peak shaving of the thermal power generator unit under dynamic fluctuation of the power grid load.

[0192] A third aspect of the embodiment of the present application,

[0193] An electronic device is provided, comprising:

[0194] a processor;

[0195] a memory for storing processor-executable instructions;

[0196] The processor is configured to invoke the instructions stored in the memory to perform the method described above.

[0197] A fourth aspect of the embodiment of the present application,

[0198] A computer readable storage medium is provided, which stores computer program instructions, and the computer program instructions are executed by a processor to implement the method described above.

[0199] The present application can be a method, device, system and / or computer program product. The computer program product can include a computer readable storage medium having computer readable program instructions loaded thereon for performing various aspects of the present application.

[0200] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit the present application; although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions recorded in the above embodiments can be modified, or some or all of the technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for regulating and optimizing steam extraction energy storage using a high-temperature molten salt thermal energy storage system, characterized in that, include: A thermodynamic coupling mathematical model of the high-temperature molten salt thermal energy storage system and the steam turbine unit is established. The thermodynamic coupling mathematical model includes thermodynamic models of the high-pressure cylinder, intermediate-pressure cylinder and low-pressure cylinder of the steam turbine, heat transfer flow model of the high-temperature molten salt thermal energy storage device, and mass energy balance equation of the steam extraction and thermal energy storage system of the steam turbine. The key parameters affecting the extraction energy storage performance are determined. The key parameters include high-pressure cylinder extraction pressure, extraction temperature and extraction flow rate, high-temperature molten salt thermal energy storage temperature and molten salt flow rate, and reheater outlet temperature. With the coal consumption rate for power generation as the optimization objective, a dynamic optimization and regulation model for extraction steam storage is established. The objective function of the dynamic optimization and regulation model is to minimize the coal consumption rate for power generation. The constraints include the upper and lower limits of extraction steam flow rate, molten salt flow rate, extraction steam temperature, molten salt thermal storage temperature, and reheater outlet temperature. An improved non-dominated sorting genetic algorithm is applied to solve the established dynamic optimization and regulation model to obtain the optimal solution set of extraction steam storage operation parameters under different operating conditions, which constitutes a dynamic optimization and regulation strategy library for extraction steam storage. Based on the power grid load forecast results, the optimal regulation strategy is selected from the obtained dynamic optimization regulation strategy library. The set values ​​of high-pressure cylinder extraction steam pressure, extraction steam temperature, and extraction steam flow rate are transmitted to the steam turbine extraction steam control system, and the set values ​​of molten salt thermal storage temperature and molten salt flow rate are transmitted to the high-temperature molten salt thermal storage control system. The steam turbine extraction process and molten salt thermal storage process are controlled in a coordinated manner to realize the flexible operation and efficient peak shaving of thermal power generating units under dynamic fluctuations in power grid load.

2. The method according to claim 1, characterized in that, A thermodynamic coupling mathematical model of the high-temperature molten salt thermal energy storage system and the steam turbine unit is established. This model includes thermodynamic models of the high-pressure, intermediate-pressure, and low-pressure cylinders of the steam turbine, a heat transfer flow model of the high-temperature molten salt thermal energy storage device, and the mass-energy balance equations of the steam turbine extraction and thermal energy storage system. A thermodynamic model of the high-pressure cylinder of a steam turbine is established, which includes a steam flow calculation formula based on Stodola's elliptic law and an isentropic efficiency correction formula considering partial load performance changes. Based on the thermodynamic model of the high-pressure cylinder of the steam turbine, the steam flow rate and the corrected isentropic efficiency of the high-pressure cylinder of the steam turbine are calculated. A heat transfer flow model for a high-temperature molten salt thermal storage device is established. The heat transfer flow model for the high-temperature molten salt thermal storage device includes a heat exchanger heat transfer equation based on the logarithmic mean temperature difference method and a modified Dittus-Boelter formula considering the flow characteristics of molten salt. Using the heat transfer flow model of the high-temperature molten salt thermal storage device, the heat transfer power of the high-temperature molten salt thermal storage device and the Nusselt number of the molten salt flow are calculated. A mass-energy balance equation is established for the steam turbine extraction and thermal storage system. The mass-energy balance equation includes the mass balance equation for the extraction point, the energy balance equation, and the energy accumulation rate equation for the high-temperature molten salt thermal storage system. Based on the mass-energy balance equation, the steam flow rate, specific enthalpy, and energy change rate of the high-temperature molten salt thermal storage system at each point in the system are calculated. By combining the thermodynamic model of the high-pressure cylinder of the steam turbine, the heat transfer flow model of the high-temperature molten salt heat storage device, and the mass energy balance equation of the steam turbine extraction and heat storage system, a complete thermodynamic coupling mathematical model is constructed.

3. The method according to claim 1, characterized in that, A dynamic optimization and regulation model for extraction steam storage is established with the coal consumption rate for power generation as the optimization objective. The objective function of the dynamic optimization and regulation model is to minimize the coal consumption rate for power generation. The constraints include the upper and lower limits of extraction steam flow rate, molten salt flow rate, extraction steam temperature, molten salt thermal storage temperature, and reheater outlet temperature. An improved non-dominated sorting genetic algorithm is applied to solve the established dynamic optimization and regulation model to obtain the optimal solution set of extraction steam storage operating parameters under different operating conditions. This constitutes the dynamic optimization and regulation strategy library for extraction steam storage, including: Initialize the population to generate an initial population containing decision variables, including high-pressure cylinder extraction steam pressure, extraction steam temperature, extraction steam flow rate, high-temperature molten salt thermal storage temperature, molten salt flow rate, and reheater outlet temperature. The initial population is sorted into different non-dominated layers by performing non-dominated ordination; uniformly distributed reference points are generated in the standardized target space; the distance from each individual to the nearest reference point is calculated, and individuals are selected to enter the next generation population based on the distance; the population diversity index is calculated, which is the average of the Euclidean distances from all individuals to the population center. Based on the population diversity index, the crossover rate and mutation rate are adaptively adjusted, wherein the crossover rate decreases as the population diversity index increases, and the mutation rate increases as the population diversity index increases. The adjustment of both the crossover rate and mutation rate adopts an exponential function form; crossover and mutation operations are performed on selected individuals to generate offspring populations. A subset of individuals is selected with a preset probability for simulated annealing optimization. New solutions are generated in the neighborhood of the selected individuals, the energy difference is calculated, the probability of accepting the new solution is calculated based on the energy difference and the current temperature, and the temperature is updated according to a preset cooling coefficient.

4. The method according to claim 3, characterized in that, The method further includes: The improved congestion is calculated by calculating the congestion of the target space and the congestion of the decision space, and then obtaining the comprehensive congestion by weighted averaging. The congestion of the target space and the congestion of the decision space take into account the distance between adjacent individuals in the target space and the decision space, respectively. The population is divided into multiple subpopulations, the number of which is equal to the number of available processor cores. For each subpopulation, objective function evaluation, constraint processing, non-dominated sorting, and crowding calculation are performed in parallel on a separate processor core. The evaluation results of each subpopulation are aggregated, and global non-dominated sorting and selection operations are performed. The parent and child populations are merged, and the merged population is subjected to non-dominated sorting. The top N individuals are selected to form the next generation population. Determine whether the maximum number of iterations has been reached or the convergence condition has been met. If so, output the optimization result; otherwise, return to the step of calculating the distance from each individual to the nearest reference point. Based on the output optimization result, determine the optimal operating parameters of the extraction steam storage system to achieve dynamic optimization control of the system.

5. The method according to claim 1, characterized in that, Based on the power grid load forecast results, the optimal regulation strategy is selected from the obtained dynamic optimization regulation strategy library. The set values ​​of high-pressure cylinder extraction steam pressure, extraction steam temperature, and extraction steam flow rate are transmitted to the turbine extraction steam control system, and the set values ​​of molten salt thermal storage temperature and molten salt flow rate are transmitted to the high-temperature molten salt thermal storage control system. A deep learning model using a long short-term memory network and an attention mechanism is used for power grid load prediction. The deep learning model includes an input layer, a long short-term memory network layer, an attention layer, a fully connected layer, and an output layer. The long short-term memory network layer has three layers, each containing 128 neurons. The fully connected layer has two layers, containing 64 and 32 neurons respectively. A dynamic optimization and adjustment strategy library is constructed using a dual deep Q-network algorithm. The dual deep Q-network algorithm includes an action value network and a target network. Both the action value network and the target network consist of three fully connected layers with 256, 128, and 64 neurons, respectively, and the activation function is the ReLU function. The reward function of the dynamic optimization and adjustment strategy library considers the improvement of power generation efficiency, the improvement of peak shaving capacity, equipment wear and tear costs, and operating costs. Based on the power grid load forecast results, the optimal regulation strategy is selected from the dynamic optimization regulation strategy library using the fuzzy comprehensive evaluation method. The fuzzy comprehensive evaluation method includes establishing a fuzzy relation matrix and determining the index weights, wherein the index weights are determined by the analytic hierarchy process. The high-pressure cylinder extraction steam pressure setpoint, extraction steam temperature setpoint, and extraction steam flow rate setpoint in the optimal regulation strategy are transmitted to the turbine extraction steam control system via the OPC UA protocol. The molten salt thermal storage temperature setpoint and molten salt flow rate setpoint are transmitted to the high-temperature molten salt thermal storage control system via the OPC UA protocol. The OPC UA protocol uses X.509 certificates for authentication and AES-256 encryption algorithm to encrypt communication data.

6. The method according to claim 5, characterized in that, The coordinated control of the steam turbine extraction process and the molten salt thermal storage process enables flexible operation and efficient peak shaving of thermal power generating units under dynamic fluctuations in grid load, including: Model predictive control is used to achieve coordinated control of the steam extraction process and the molten salt thermal storage process of the steam turbine. The objective function of the model predictive control method includes an output tracking error term and a control increment penalty term. The constraints include the range of extraction steam pressure, the range of extraction steam temperature, the range of extraction steam flow rate, the range of molten salt temperature, the range of molten salt flow rate, and the control increment limit. An improved fast gradient method is used to solve the model predictive control optimization problem. The improved fast gradient method includes transforming the original problem into a quadratic programming problem, using the Nesterov accelerated gradient method for iterative solution, and adopting a hot-start strategy. A recursive least squares algorithm is introduced to update the system model parameters online. The forgetting factor of the recursive least squares algorithm is set to 0.98 to achieve adaptive updating of the system model.

7. The method according to claim 6, characterized in that, An improved fast gradient method is used to solve the model predictive control optimization problem. This improved fast gradient method includes transforming the original problem into a quadratic programming problem, using Nesterov to accelerate the gradient method for iterative solution, and employing a warm-start strategy. The Nesterov accelerated gradient method includes the following sub-steps: Initialize the decision variable vector, auxiliary variable vector, step size, and momentum parameter; calculate the gradient of the objective function; update the decision variable vector and auxiliary variable vector; project the updated decision variable vector into the feasible region; dynamically adjust the step size using the Barzilai-Borwein method; dynamically adjust the momentum parameter based on the number of iterations. Implement a warm-start strategy, using the optimal solution from the previous control cycle as the initial value for the current control cycle. Specifically, this includes: Save the optimal solution from the previous control cycle; construct a new initial decision variable vector, where the first N-1 control increments come from the optimal solution of the previous cycle, and the last control increment is set to zero; use a larger step size for warm-up iteration to quickly approach the optimal solution.

8. A steam extraction and energy storage regulation and optimization system utilizing a high-temperature molten salt thermal energy storage system, used to implement the method described in any one of claims 1-7, characterized in that, include: The first unit is used to establish a thermodynamic coupling mathematical model between the high-temperature molten salt thermal storage system and the steam turbine unit. The thermodynamic coupling mathematical model includes thermodynamic models of the high-pressure cylinder, intermediate-pressure cylinder and low-pressure cylinder of the steam turbine, a heat transfer flow model of the high-temperature molten salt thermal storage device, and the mass energy balance equation of the steam extraction and thermal storage system of the steam turbine. The unit also determines the key parameters affecting the extraction energy storage performance, including the high-pressure cylinder extraction pressure, extraction temperature and extraction flow rate, the high-temperature molten salt thermal storage temperature and molten salt flow rate, and the reheater outlet temperature. The second unit is used to establish a dynamic optimization and regulation model for extraction steam storage with the optimization objective of power generation coal consumption rate. The objective function of the dynamic optimization and regulation model is to minimize the power generation coal consumption rate, and the constraints include the upper and lower limits of extraction steam flow rate, molten salt flow rate, extraction steam temperature, molten salt thermal storage temperature, and reheater outlet temperature. An improved non-dominated sorting genetic algorithm is applied to solve the established dynamic optimization and regulation model to obtain the optimal solution set of extraction steam storage operation parameters under different operating conditions, which constitutes the dynamic optimization and regulation strategy library for extraction steam storage. The third unit is used to select the optimal regulation strategy from the obtained dynamic optimization regulation strategy library based on the power grid load forecast results. It transmits the set values ​​of high-pressure cylinder extraction steam pressure, extraction steam temperature, and extraction steam flow rate to the steam turbine extraction steam control system, and transmits the set values ​​of molten salt thermal storage temperature and molten salt flow rate to the high-temperature molten salt thermal storage control system. It coordinates the control of the steam turbine extraction process and the molten salt thermal storage process to realize the flexible operation and efficient peak shaving of thermal power generating units under dynamic fluctuations in power grid load.

9. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1 to 7.

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