Molten salt energy storage system control method and system based on multi-input single-output dynamic matrix

Abstract

The invention discloses a molten salt energy storage system control method and system based on a multi-input and single-output dynamic matrix, and relates to the technical field of molten salt energy storage, and the method comprises the steps: constructing a prediction model through employing a multi-input and multi-output dynamic matrix control algorithm; tracking an expected value through rolling optimization; and the deviation is fed back and corrected. According to the fused salt energy storage system control method based on the multi-input and single-output dynamic matrix, a prediction model is constructed by adopting a multi-input and multi-output dynamic matrix control algorithm, the control precision and the response speed of the system are improved, the expected value is tracked through rolling optimization, high-precision control is kept in a dynamically changing environment, and the system performance is improved. According to the method, the robustness and the response capability of the system are improved, deviation feedback correction is carried out, safety accidents caused by overheating of the fused salt energy storage system are prevented, the safety of the system is improved, and better effects are achieved in the aspects of control precision, response speed and safety.

Description

Technical Field

[0001] The present invention relates to the technical field of molten salt energy storage, and in particular to a control method and system for a molten salt energy storage system based on a multi-input single-output dynamic matrix. Background Art

[0002] Molten salt energy storage is a new type of energy storage technology that uses molten salt as an energy storage medium to convert electrical energy into thermal energy for storage. Molten salt energy storage has been widely used in the fields of electricity, heat, metallurgy, and chemical industry.

[0003] The advantages of molten salt energy storage technology include: High efficiency: The conversion efficiency of molten salt energy storage is high, which can reach more than 95%, which is more efficient than traditional energy storage technology. Reliable: Molten salt energy storage technology has stable chemical and physical properties, can store energy for a long time, and is not easily affected by environmental factors. Environmental protection: Molten salt energy storage technology does not use toxic and harmful substances, has no pollutant emissions, and is environmentally friendly. Safety: Molten salt energy storage technology has high safety, is non-flammable and non-explosive, can store energy at high temperatures, and will not cause accidents such as fire and explosion.

[0004] Molten salt energy storage technology has broad application prospects in the fields of electricity, heat, metallurgy, and chemical industry. It can solve the imbalance of electricity supply and demand, adjust the stability of the power system, and reduce carbon emissions. With the continuous development of technology and the continuous reduction of costs, molten salt energy storage technology is expected to become one of the important energy storage technologies in the future.

[0005] The demonstration application of molten salt energy storage combined with thermal power units is underway. Molten salt energy storage can not only realize the peak-shaving function for thermal power units, store energy during the low-power consumption period, and release energy during the peak power consumption period to obtain peak-shaving benefits, but also assist thermal power units to enhance frequency regulation performance, shorten unit response time, improve regulation rate and regulation accuracy, and reduce equipment aging caused by frequent conditions of the units.

[0006] The existing multi-input multi-output control methods still have some shortcomings when applied to molten salt energy storage systems. First, the traditional MIMO control algorithm has high computational complexity when dealing with large-scale complex systems, making it difficult to achieve real-time control. Second, the existing methods are less robust to changes in system parameters and are easily affected by external disturbances and parameter uncertainties. In addition, many existing technologies lack effective rolling optimization and deviation feedback correction mechanisms, which makes the system prone to cumulative errors and performance degradation after long-term operation. Summary of the invention

[0007] In view of the above-mentioned problems, the present invention is proposed.

[0008] Therefore, the technical problem solved by the present invention is that the existing multi-input multi-output control method has the problems of low control accuracy, slow response speed, poor safety, and low response capability.

[0009] To solve the above technical problems, the present invention provides the following technical solutions: a control method for a molten salt energy storage system based on a multi-input and single-output dynamic matrix, including building a prediction model using a multi-input and multi-output dynamic matrix control algorithm; tracking expected values ​​through rolling optimization; and correcting deviations through feedback.

[0010] As a preferred solution of the control method of the molten salt energy storage system based on a multi-input single-output dynamic matrix according to the present invention, wherein: the use of a multi-input multi-output dynamic matrix control algorithm to construct a prediction model includes collecting input parameter data, output parameter data, dynamic characteristic vector data, control system data, environmental data, error and correction data, and system performance data;

[0011] The collected input parameter data include molten salt pump frequency, electric heater inlet molten salt temperature and electric heater power;

[0012] The output parameter data includes the molten salt temperature of the electric heater outlet pipeline;

[0013] The dynamic characteristic vector data includes step response data;

[0014] The control system data includes system operation status data;

[0015] The environmental data includes environmental temperature and pressure;

[0016] The error and correction data include error data between actual output and predicted output;

[0017] The system performance data includes system stability, response speed, and control accuracy.

[0018] As a preferred solution of the control method of the molten salt energy storage system based on a multi-input single-output dynamic matrix according to the present invention, wherein: the prediction model constructed by the multi-input multi-output dynamic matrix control algorithm includes the input parameters of the model including the molten salt pump frequency, the molten salt temperature at the inlet of the electric heater, and the power of the electric heater, and the three input parameters are respectively recorded as u1, u2, and u3;

[0019] The output parameter of the model is the molten salt temperature of the electric heater outlet pipeline, denoted as y;

[0020] The step response of the outlet temperature corresponding to the three input quantities of molten salt pump frequency, inlet temperature, and electric heater power is established through actual measurement, and the dynamic characteristic vector of the model is obtained from the step response:

[0021]

[0022] Where i represents the number of input parameters (1, 2, 3); N represents the number of samples of the step response, a i(1) ,a i(2) ...a i(N) represents the sampled value of the step response, a i(N) Indicates close to the steady-state value of the step response.

[0023] As a preferred solution of the control method of the molten salt energy storage system based on a multi-input single-output dynamic matrix according to the present invention, the prediction model constructed by the multi-input multi-output dynamic matrix control algorithm includes that when the input parameter generates an increment Δu(k) at time k, the contribution to the output is expressed as:

[0024]

[0025] Among them, Δu(k) represents the increment of the input parameter at time k, a i(j) Represents the step response sampling value of the i-th input parameter at the j-th sampling point;

[0026] According to the superposition principle of linear systems, when there are M continuous input parameters Δu(k), the predicted output is expressed as:

[0027]

[0028] Among them, for the predicted output of the system at P consecutive moments under M consecutive inputs, They represent the predicted values ​​of y with and without control incremental input, respectively; Represents the dynamic matrix y corresponding to the three input parameters u1, u2, u3, where Represents the control increments of the three input parameters, expressed as:

[0029]

[0030] in, represents a submatrix of the dynamic matrix, represents the control increment of the three input parameters, Represents the predicted output when there is a control increment input.

[0031] As a preferred solution of the control method of the multi-input single-output dynamic matrix molten salt energy storage system according to the present invention, wherein: the tracking of the expected value by rolling optimization includes that during the control process, the input parameter does not change and the output parameter tracks the expected value, and the optimization target value is expressed as:

[0032]

[0033] in, represents the expected output value, The matrix representing the output parameter tracking error at each moment, represents the control increment matrix of input parameters, J(k) represents the optimization target value;

[0034] To minimize J(k), let The optimal control increment is expressed as:

[0035]

[0036] in, The matrix representing the output parameter tracking error at each moment, The control increment matrix representing the input parameters;

[0037] The control quantity at the previous moment plus the control increment output at each step represents the control quantity actually input into the control system.

[0038] As a preferred solution of the control method of the molten salt energy storage system based on a multi-input single-output dynamic matrix according to the present invention, the deviation feedback correction includes that due to the uncertainty of the environment and the object model and the deviation from the actual system, after the real-time control at time k, the deviation between the actual output and the predicted output at time k+1 is expressed as:

[0039]

[0040] Among them, y(k+1) represents the actual output value of the system at time k+1, represents the predicted output value of the system at time k+1, Indicates that there is a deviation between the actual output at time k+1 and the predicted output;

[0041] Therefore, the error is weighted to correct the predictions for other moments in the future:

[0042]

[0043] in, represents the predicted output value after error correction, represents the output value at time k+1 in the future predicted at time k, represents the error correction matrix;

[0044] After the correction As the initial value of the prediction for the next moment, the introduction of correction also makes the system a negative feedback system.

[0045] As a preferred solution of the control method of the molten salt energy storage system based on a multi-input single-output dynamic matrix according to the present invention, wherein: the correction of the deviation feedback includes establishing a dynamic matrix control system for molten salt energy storage, and during the operation of the system, the system detects the temperature of the electric heater tube wall in real time;

[0046] If the temperature of the electric heater tube wall exceeds the maximum set allowable value, the control system will automatically reduce the outlet temperature setting value by 10°C. If the tube wall temperature continues to exceed the temperature after 1 minute, the outlet temperature setting value will continue to be reduced.

[0047] If the temperature does not exceed the limit, the existing set value is maintained and the cycle is repeated to ensure that the molten salt energy storage system does not exceed the temperature and cause a safety accident.

[0048] Another object of the present invention is to provide a molten salt energy storage system control system based on a multi-input single-output dynamic matrix, which can build a prediction model through a multi-input multi-output dynamic matrix control algorithm, solving the problem of low control accuracy in current multi-input multi-output control methods.

[0049] As a preferred solution of the control system of the molten salt energy storage system based on multi-input single-output dynamic matrix described in the present invention, it includes: a prediction model construction module, a rolling optimization module, and a feedback correction module; the prediction model construction module is used to construct a prediction model based on a multi-input multi-output dynamic matrix control algorithm; the dynamic optimization module is used to track the expected value; the feedback correction module is used to output the prediction initial value.

[0050] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of a control method for a multi-input single-output dynamic matrix molten salt energy storage system.

[0051] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a control method for a multi-input single-output dynamic matrix molten salt energy storage system.

[0052] Beneficial effects of the present invention: The control method of the molten salt energy storage system based on a multi-input single-output dynamic matrix provided by the present invention adopts a multi-input multi-output dynamic matrix control algorithm to construct a prediction model, and through the application of the dynamic matrix control algorithm, accurate modeling and control of the multivariable system are realized, and the control accuracy and response speed of the system are improved. By tracking the expected value through rolling optimization, high-precision control is maintained in a dynamically changing environment, and instability caused by drastic changes in parameters is avoided, the robustness and responsiveness of the system are improved, and feedback correction of deviations is performed. It can continuously and stably operate in a complex and changeable environment. At the same time, through real-time monitoring and adjustment, safety accidents caused by overheating of the molten salt energy storage system are prevented, and the safety of the system is improved. The present invention achieves better effects in terms of control accuracy, response speed and safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0054] Figure 1 An overall flow chart of a control method for a multi-input single-output dynamic matrix molten salt energy storage system provided for the first embodiment of the present invention.

[0055] Figure 2 A structural diagram of a molten salt energy storage system based on a multi-input single-output dynamic matrix molten salt energy storage system control method provided for the first embodiment of the present invention.

[0056] Figure 3 A dynamic matrix control flow chart of a multi-input single-output dynamic matrix molten salt energy storage system control method provided for the first embodiment of the present invention.

[0057] Figure 4 An overall flow chart of a control system based on a multi-input single-output dynamic matrix molten salt energy storage system provided for the third embodiment of the present invention. DETAILED DESCRIPTION

[0058] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0059] Example 1, reference Figure 1-Figure 3, is an embodiment of the present invention, and provides a control method for a molten salt energy storage system based on a multi-input single-output dynamic matrix, comprising:

[0060] S1: A prediction model is constructed using a multi-input and multi-output dynamic matrix control algorithm.

[0061] Furthermore, the input parameter data, output parameter data, dynamic characteristic vector data, control system data, environmental data, error and correction data and system performance data are collected.

[0062] The input parameter data includes:

[0063] Molten salt pump frequency (u1): Sampling frequency: It is recommended to record the frequency change data of the molten salt pump under different working conditions. Sampling period: It should cover the frequency change in the entire system operation cycle, including various stages such as startup, stable operation and stop.

[0064] Molten salt temperature at the inlet of the electric heater (u2): Sampling frequency: Real-time recording of the molten salt temperature at the inlet of the electric heater. Sampling period: Covers temperature data under various operating conditions, especially in the case of load changes and ambient temperature changes.

[0065] Electric heater power (u3): Sampling frequency: records the power output of the electric heater. Sampling period: includes power data during system startup, operation and shutdown.

[0066] The output parameter data includes:

[0067] Molten salt temperature of the electric heater outlet pipeline (y): Sampling frequency: High frequency sampling of the outlet molten salt temperature to ensure the continuity and accuracy of the data. Sampling cycle: Cover various operating conditions and operation cycles of the system, especially when the input parameters change, record the response of the outlet temperature.

[0068] Dynamic characteristic vector data includes:

[0069] Step response data: Sampling frequency: When the input parameter changes stepwise, record the change process of the outlet temperature. Sampling period: Ensure that the complete process of the system reaching a steady state is recorded, including all data points from the initial state to the new steady state.

[0070] Control system data includes:

[0071] The system's operating status data includes real-time changes in input parameters, real-time data of system responses, execution of control instructions, etc. Collection frequency: Real-time recording to ensure data continuity and integrity.

[0072] Environmental data includes:

[0073] Ambient temperature and pressure: Sampling frequency: Record ambient temperature and pressure regularly, especially under extreme ambient conditions. Sampling period: Includes changes in ambient conditions throughout the entire cycle of system operation.

[0074] Error and correction data include:

[0075] The error data between the actual output and the predicted output: Sampling frequency: Real-time recording of the error between the actual output and the predicted output. Sampling period: Covers the entire system operation process, especially the error data when the input parameters change.

[0076] System performance data includes:

[0077] System performance indicator data includes system stability, response speed, control accuracy and other indicators. Sampling frequency: Regularly evaluate and record system performance indicators, especially performance data under different operating conditions.

[0078] It should be noted that the model predictive control proposed in the present invention adopts a multi-input multi-output dynamic matrix control algorithm (MIMO-DMC).

[0079] The input parameters of the model include the molten salt pump frequency, the molten salt temperature at the electric heater inlet (referred to as the inlet temperature), and the electric heater power. The three input parameters are recorded as u1, u2, and u3. The output parameter of the model is the molten salt temperature at the outlet pipeline of the electric heater (referred to as the outlet temperature), recorded as y.

[0080] First, the step response of the outlet temperature corresponding to the three input quantities of molten salt pump frequency, inlet temperature, and electric heater power is established through actual measurement, and the dynamic characteristic vector of the model is obtained from the step response:

[0081]

[0082] Where i is the number of input parameters 1, 2, 3; N is the number of samples of the step response, a i(1) ,a i(2) ...a i(N) is the sampling value of the step response, where a i(N) is close enough to the steady-state value of the step response.

[0083] It should also be noted that if the input parameter produces an increment Δu(k) at a certain time k, the contribution to the output is:

[0084]

[0085] Among them, Δu(k) represents the increment of the input parameter at time k, a i(j) Represents the step response sampling value of the i-th input parameter at the j-th sampling point;

[0086] According to the superposition principle of linear systems, when there are M continuous input parameters Δu(k), the predicted output is:

[0087]

[0088] Among them, for the predicted output of the system at P consecutive moments under M consecutive inputs, They represent the predicted values ​​of y with and without control incremental input, respectively; Represents the dynamic matrix y corresponding to the three input parameters u1, u2, u3, where Represents the control increments of the three input parameters, expressed as:

[0089]

[0090] in, represents a submatrix of the dynamic matrix, represents the control increment of the three input parameters, Represents the predicted output when there is a control increment input.

[0091] S2: Tracking expected value through rolling optimization.

[0092] Furthermore, in order to control the process, the input parameters do not change drastically and the output parameters track the expected value as much as possible, the optimization target value is taken as:

[0093]

[0094] in, represents the expected output value, The matrix representing the output parameter tracking error at each moment, represents the control increment matrix of the input parameters, and J(k) represents the optimization target value.

[0095] To minimize J(k), let The optimal control increment is obtained as:

[0096]

[0097] in, The matrix representing the output parameter tracking error at each moment, Represents the control increment matrix for the input parameters.

[0098] The control quantity at the previous moment plus the control increment calculated at each step is the control quantity actually input into the control system.

[0099] S3: Feedback correction of deviation.

[0100] Furthermore, due to the uncertainty of the environment and object models and the deviation from the actual system, after the real-time control at time k, there is a deviation between the actual output at time k+1 and the predicted output:

[0101]

[0102] Among them, y(k+1) represents the actual output value of the system at time k+1, represents the predicted output value of the system at time k+1, Indicates that there is a deviation between the actual output at time k+1 and the predicted output;

[0103] Therefore, the error is weighted to correct the predictions for other moments in the future:

[0104]

[0105] in, represents the predicted output value after error correction, represents the output value at time k+1 in the future predicted at time k, represents the error correction matrix;

[0106] After the correction As the initial value of the prediction for the next moment, the introduction of correction also makes the system a negative feedback system, which plays a great role in improving the performance of the system.

[0107] It should be noted that after the dynamic matrix control system of molten salt energy storage is established, one-key control of the molten salt energy storage system can be realized. The user only needs to input the required outlet temperature of the molten salt electric heater, the inlet temperature sampling of the molten salt electric heater and the actual temperature measurement point. The control system will automatically adjust the power of the molten salt electric heater and the frequency of the molten salt pump. The power of the molten salt electric heater or the frequency of the molten salt pump can also be manually set. The system automatically adjusts the other input parameter to ultimately make the outlet temperature reach the set value.

[0108] During system operation, the system detects the temperature of the electric heater tube wall in real time. If it exceeds the maximum set allowable value, the control system automatically lowers the outlet temperature setting value by one step (for example, 10°C). After a period of time (for example, 1 minute), if the tube wall temperature continues to exceed the temperature, the outlet temperature setting value will continue to be lowered. If it does not exceed the temperature, the existing setting value will be maintained. This cycle is repeated to ensure that the molten salt energy storage system does not seriously overheat and cause a safety accident.

[0109] Example 2, an embodiment of the present invention, provides a control method for a molten salt energy storage system based on a multi-input single-output dynamic matrix. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0110] First, the experiment is divided into two groups: an experimental group and a control group. The experimental group applies the multi-input multi-output dynamic matrix control algorithm proposed by the present invention, and the control group adopts the traditional single-input single-output (SISO) control algorithm.

[0111] Prepare a molten salt pump to control the molten salt flow frequency. The parameter is the molten salt pump frequency (Hz)

[0112] Prepare an electric heater to control the molten salt temperature. The parameters are inlet temperature (℃) and electric heater power (kW)

[0113] Prepare the sensor to measure the molten salt temperature of the electric heater outlet pipeline (℃)

[0114] Before the experiment begins, record the initial parameters, including the molten salt pump frequency, the molten salt temperature at the electric heater inlet, and the electric heater power. Set the desired value of the molten salt temperature at the electric heater outlet pipeline.

[0115] A multi-input and multi-output dynamic matrix control algorithm is used to establish a prediction model by collecting input parameter data (u1: molten salt pump frequency, u2: electric heater inlet temperature, u3: electric heater power) and output parameter data (y: molten salt temperature of the electric heater outlet pipeline). Through the rolling optimization algorithm, the input parameters are adjusted every 5 minutes so that the output temperature can track the expected value as much as possible. After each control cycle, the error between the actual output and the predicted output is calculated, and the prediction model is corrected using error feedback.

[0116] The control group uses the traditional SISO control algorithm, which is based on a single input (inlet temperature) and a single output (outlet temperature). The input parameters are adjusted every 5 minutes, without rolling optimization and feedback correction mechanism.

[0117] During the experiment, the input parameters (molten salt pump frequency, inlet temperature, electric heater power) and output parameters (outlet temperature) at each time point, as well as the control increment and error data after each adjustment, were recorded in real time.

[0118] Table 1 Experimental data table

[0119]

[0120] In terms of control accuracy, experimental data show that in the experimental group, through the multi-input and multi-output dynamic matrix control algorithm, the outlet temperature can accurately track the expected value, with a small error, and gradually decreases over time. Compared with the control group, the traditional SISO control algorithm only considers a single input, resulting in lower control accuracy and larger temperature fluctuations, and the error deviates significantly from the expected value at multiple time points. The specific data is shown in the table. Within 20 minutes, the temperature error of the experimental group was always kept within ±3°C, while the error of the control group fluctuated greatly.

[0121] In terms of response speed, the experimental group adopted a rolling optimization algorithm and adjusted the input parameters every 5 minutes, so that the system could quickly respond to environmental changes and adjust the output. Through error feedback correction, the system can effectively reduce the error in a short time and quickly reach a steady state. Due to the lack of rolling optimization and feedback correction mechanism, the control group had a slow response speed, and the adjusted output temperature took a long time to approach the expected value. Experimental data showed that the experimental group was basically stable within 15 minutes, and the error was controlled within ±2°C, while the control group had a large temperature change in the same time period and failed to reach a stable state.

[0122] In terms of safety, during the experiment, through real-time monitoring and feedback correction, the experimental group was able to effectively prevent safety accidents caused by overheating of the molten salt energy storage system. When the temperature of the electric heater tube wall approaches the set safety threshold, the system automatically lowers the outlet temperature setting value to ensure the safe operation of the system. However, due to the lack of similar safety mechanisms, the control group has the risk of overheating, which may cause equipment damage or safety accidents. The data show that during the entire experiment, the outlet temperature of the experimental group was always maintained within a safe range, while the temperature of the control group approached or exceeded the safety threshold at certain moments.

[0123] In summary, the present invention achieves better results in terms of control accuracy, response speed and safety.

[0124] Example 3, reference Figure 4 , is an embodiment of the present invention, providing a control system for a molten salt energy storage system based on a multi-input single-output dynamic matrix, including a prediction model building module, a rolling optimization module, and a feedback correction module;

[0125] The prediction model construction module is used to construct the prediction model based on the multi-input and multi-output dynamic matrix control algorithm, the dynamic optimization module is used to track the expected value, and the feedback correction module is used to output the prediction initial value.

[0126] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0127] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0128] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0129] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0130] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A control method for a molten salt energy storage system based on a multi-input single-output dynamic matrix, characterized in that: include: A prediction model is constructed using a multi-input and multi-output dynamic matrix control algorithm; Tracking expected value through rolling optimization; Feedback correction for deviation.

2. The control method of the molten salt energy storage system based on a multi-input single-output dynamic matrix as claimed in claim 1, characterized in that: The use of a multi-input multi-output dynamic matrix control algorithm to construct a prediction model includes collecting input parameter data, output parameter data, dynamic characteristic vector data, control system data, environmental data, error and correction data, and system performance data; The collected input parameter data include molten salt pump frequency, electric heater inlet molten salt temperature and electric heater power; The output parameter data includes the molten salt temperature of the electric heater outlet pipeline; The dynamic characteristic vector data includes step response data; The control system data includes system operation status data; The environmental data include environmental temperature and pressure; The error and correction data include error data between actual output and predicted output; The system performance data includes system stability, response speed, and control accuracy.

3. The control method of the molten salt energy storage system based on a multi-input single-output dynamic matrix as claimed in claim 2, characterized in that: The prediction model constructed by adopting a multi-input multi-output dynamic matrix control algorithm includes input parameters of the model including molten salt pump frequency, electric heater inlet molten salt temperature, and electric heater power, and the three input parameters are respectively recorded as u1, u2, and u3; The output parameter of the model is the molten salt temperature of the electric heater outlet pipeline, denoted as y; The step response of the outlet temperature corresponding to the three input quantities of molten salt pump frequency, inlet temperature, and electric heater power is established through actual measurement, and the dynamic characteristic vector of the model is obtained from the step response: Where i represents the number of input parameters (1, 2, 3); N represents the number of samples of the step response, a i(1) ,a i(2) ...a i(N) represents the sampled value of the step response, a i(N) Indicates close to the steady-state value of the step response.

4. The control method of the molten salt energy storage system based on a multi-input single-output dynamic matrix as claimed in claim 3 is characterized in that: The prediction model constructed by adopting the multi-input multi-output dynamic matrix control algorithm includes that when the input parameter generates an increment Δu(k) at time k, the contribution to the output is expressed as: Among them, Δu(k) represents the increment of the input parameter at time k, a i(j) Represents the step response sampling value of the i-th input parameter at the j-th sampling point; According to the superposition principle of linear systems, when there are M continuous input parameters Δu(k), the predicted output is expressed as: Among them, for the predicted output of the system at P consecutive moments under M consecutive inputs, They represent the predicted values ​​of y with and without control incremental input, respectively; Represents the dynamic matrix y corresponding to the three input parameters u1, u2, u3, where Represents the control increments of the three input parameters, expressed as: in, represents a submatrix of the dynamic matrix, represents the control increment of the three input parameters, Represents the predicted output when there is a control increment input.

5. The control method of the molten salt energy storage system based on a multi-input single-output dynamic matrix as claimed in claim 4, characterized in that: The tracking of the expected value by rolling optimization includes that during the control process, the input parameter does not change and the output parameter tracks the expected value, and the optimization target value is expressed as: in, represents the expected output value, The matrix representing the output parameter tracking error at each moment, represents the control increment matrix of input parameters, J(k) represents the optimization target value; To minimize J(k), let The optimal control increment is expressed as: in, The matrix representing the output parameter tracking error at each moment, The control increment matrix representing the input parameters; The control quantity at the previous moment plus the control increment output at each step represents the control quantity actually input into the control system.

6. The control method of the molten salt energy storage system based on a multi-input single-output dynamic matrix as claimed in claim 5, characterized in that: The deviation feedback correction includes the deviation between the actual output and the predicted output at time k+1 after the real-time control at time k due to the uncertainty of the environment and object model and the deviation from the actual system, which is expressed as: Among them, y(k+1) represents the actual output value of the system at time k+1, represents the predicted output value of the system at time k+1, Indicates that there is a deviation between the actual output at time k+1 and the predicted output; Therefore, the error is weighted to correct the predictions for other moments in the future: in, represents the predicted output value after error correction, represents the output value at time k+1 in the future predicted at time k, represents the error correction matrix; After the correction As the initial value of the prediction for the next moment, the introduction of correction also makes the system a negative feedback system.

7. The control method of the molten salt energy storage system based on a multi-input single-output dynamic matrix as claimed in claim 6, characterized in that: The deviation feedback correction includes establishing a dynamic matrix control system for molten salt energy storage, and during the operation of the system, the system detects the temperature of the electric heater tube wall in real time; If the temperature of the electric heater tube wall exceeds the maximum set allowable value, the control system will automatically reduce the outlet temperature setting value by 10°C. If the tube wall temperature continues to exceed the temperature after 1 minute, the outlet temperature setting value will continue to be reduced. If the temperature does not exceed the limit, the existing set value is maintained and the cycle is repeated to ensure that the molten salt energy storage system does not exceed the temperature and cause a safety accident.

8. A system using the control method of a multi-input single-output dynamic matrix molten salt energy storage system according to any one of claims 1 to 7, characterized in that: Including prediction model building module, rolling optimization module, feedback correction module; The prediction model building module is used to build a prediction model based on a multi-input multi-output dynamic matrix control algorithm; The dynamic optimization module is used to track the expected value; The feedback correction module is used to output the prediction initial value.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the control method of the multi-input single-output dynamic matrix molten salt energy storage system according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the control method of a multi-input single-output dynamic matrix molten salt energy storage system according to any one of claims 1 to 7 are implemented.

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