Compressed air energy storage compression side power tracking control method

Abstract

The invention relates to a compressed air energy storage compression side power tracking control method, which comprises the following steps of: constructing a multi-stage compressor basic dynamic model, and obtaining a mixed power expression by combining thermodynamic deviation, an inter-stage coupling effect and an unstable area power abrupt change correction data set; establishing an explicit efficiency expression between the real-time operation efficiency and the operation condition of each stage of compressor; power tracking deviation is determined based on a hybrid power expression, so that a multivariate objective function is constructed, a multi-objective model prediction control system is constructed according to an explicit efficiency expression in combination with a preset safety constraint condition, and weight coefficients in the number of the multi-objective model prediction control system are adjusted according to requirements of different operation scenes; and a self-adaptive control instruction is obtained. Therefore, the problems that in the prior art, when strong disturbance such as power grid frequency sudden change and wind-solar power sudden change is coped with, the instability phenomena such as surge and blockage of a compressor unit are easily induced, and in a serious situation, mechanical damage to equipment and energy storage interruption are caused are solved, and the multi-scene self-adaptive control effect is improved.

Description

Technical Field

[0001] This application relates to the field of new energy technology, and in particular to a method for power tracking control on the compression side of compressed air energy storage. Background Technology

[0002] Compressed air energy storage (CAES) technology, as one of the core directions of large-scale energy storage, has shown significant application prospects in fields such as frequency regulation and peak shaving in new energy power systems and grid inertia support due to its advantages of high power level, long lifespan, and low cost. A CAES system uses an electrically driven compressor to compress air to a high-pressure state for storage. When releasing energy, it utilizes the expansion of the high-pressure air to generate electricity. Its core components include three major subsystems: the compression side, the air storage device, and the expansion side. Among them, the compression side, as the core energy conversion unit in the energy storage process, directly affects the system efficiency, stability, and response speed due to its dynamic characteristics. Especially when dealing with the strong volatility of renewable energy, it is urgent to address the problems of insufficient accuracy in full-condition modeling and prominent risks of dynamic instability.

[0003] In related technologies, compression-side modeling methods are mostly based on steady-state thermodynamic analysis. They describe the compressor characteristics through a lumped parameter model, simplify the motor drive to an ideal torque source, and maintain steady-state operation through a proportional-integral-derivative (PID) controller combined with a feedforward compensation architecture.

[0004] However, the relevant technologies neglect the characteristics of strong nonlinearity, multi-timescale coupling and large-scale migration of operating conditions. When dealing with strong disturbances such as sudden changes in grid frequency and sudden changes in wind and solar power, they are prone to instability phenomena such as surge and blockage of compressor units. In severe cases, they can cause mechanical damage to equipment and interruption of energy storage, which urgently needs to be solved. Summary of the Invention

[0005] This application provides a compressed air energy storage compression side power tracking control method to solve the problem that related technologies are prone to instability phenomena such as surge and blockage of compressor units when dealing with strong disturbances such as sudden changes in grid frequency and sudden changes in wind and solar power, which can cause mechanical damage to equipment and interruption of energy storage in severe cases, thereby improving the adaptive control effect in multiple scenarios.

[0006] The first aspect of this application provides a compressed air energy storage compression-side power tracking control method, including the following steps: Construct a basic dynamic model of a multi-stage compressor; Obtain the thermodynamic deviation correction dataset, the interstage coupling effect correction dataset, and the unstable region power mutation correction dataset. Then, obtain the thermodynamic power, interstage coupling power, and unstable mutation power based on the thermodynamic deviation correction dataset, the interstage coupling effect correction dataset, and the unstable region power mutation correction dataset, respectively. Finally, obtain the mixed power expression based on the multi-stage compressor basic dynamic model, thermodynamic power, interstage coupling power, and unstable mutation power. Establish a multidimensional mapping relationship between the real-time operating efficiency and operating conditions of compressors at all levels, generate an explicit efficiency expression, determine the power tracking deviation based on the hybrid power expression, construct a multivariate objective function based on the power tracking deviation and the real-time operating efficiency of compressors at all levels, and construct a multi-objective model predictive control system based on the multivariate objective function and preset safety constraints, according to the explicit efficiency expression. The system obtains the requirements of different operating scenarios, adjusts at least one weight coefficient in the multi-objective model predictive control system according to the requirements of different operating scenarios, obtains adaptive control commands, and uses the adaptive control commands to perform adaptive operation control of multi-stage compressors in multiple scenarios.

[0007] Optionally, in some embodiments, the thermodynamic deviation correction dataset, the interstage coupling effect correction dataset, and the unstable region power mutation correction dataset include: Collect measured data of the compressor's thermodynamic parameters under multiple loads and speeds, and construct a thermodynamic deviation correction dataset based on the measured data of the thermodynamic parameters; A valve step operation is performed on each stage compressor in a multi-stage compressor system, and the state variable time-series change curves of each stage compressor are collected synchronously. Based on the state variable time-series change curves, a dataset for correcting inter-stage coupling effects is constructed. Critical operating condition data were obtained through surge and blockage tests, and multiple characteristic parameters at the moment of compressor instability were collected. Phase features and amplitude decay features were extracted from the characteristic parameters using Fourier transform, and a power mutation correction dataset for the instability region was constructed based on the phase features and amplitude decay features.

[0008] Optionally, in some embodiments, the basic dynamic model of the multi-stage compressor is: ; ; ;

[0009] Optionally, in some embodiments, the mixed power is: ;

[0010] Optionally, in some embodiments, the explicit efficiency expression is: ;

[0011] A second aspect of this application provides a compressed air energy storage compression-side power tracking control device, comprising: The first building module is used to build the basic dynamic model of a multi-stage compressor. The acquisition module is used to acquire the thermodynamic deviation correction dataset, the interstage coupling effect correction dataset, and the unstable region power mutation correction dataset. It then obtains the thermodynamic power, interstage coupling power, and unstable mutation power based on these datasets, and finally derives the mixed power expression based on the multi-stage compressor basic dynamic model, thermodynamic power, interstage coupling power, and unstable mutation power. The second construction module is used to establish a multidimensional mapping relationship between the real-time operating efficiency and operating conditions of compressors at all levels, generate an explicit efficiency expression, determine the power tracking deviation based on the hybrid power expression, construct a multivariate objective function based on the power tracking deviation and the real-time operating efficiency of compressors at all levels, and construct a multi-objective model predictive control system based on the multivariate objective function and preset safety constraints, according to the explicit efficiency expression. The control module is used to acquire the requirements of different operating scenarios, and adjust at least one weight coefficient in the multi-objective model predictive control system according to the requirements of different operating scenarios to obtain adaptive control commands, and use the adaptive control commands to adaptively control the operation of the multi-stage compressor in multiple scenarios.

[0012] Optionally, in some embodiments, the acquisition module is specifically used for: Collect measured data of the compressor's thermodynamic parameters under multiple loads and speeds, and construct a thermodynamic deviation correction dataset based on the measured data of the thermodynamic parameters; A valve step operation is performed on each stage compressor in a multi-stage compressor system, and the state variable time-series change curves of each stage compressor are collected synchronously. Based on the state variable time-series change curves, a dataset for correcting inter-stage coupling effects is constructed. Critical operating condition data were obtained through surge and blockage tests, and multiple characteristic parameters at the moment of compressor instability were collected. Phase features and amplitude decay features were extracted from the characteristic parameters using Fourier transform, and a power mutation correction dataset for the instability region was constructed based on the phase features and amplitude decay features.

[0013] Optionally, in some embodiments, the basic dynamic model of the multi-stage compressor is: ; ; ;

[0014] Optionally, in some embodiments, the mixed power is: ;

[0015] Optionally, in some embodiments, the explicit efficiency expression is: ;

[0016] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the program to implement the compressed air energy storage compression-side power tracking control method described in the first aspect embodiment.

[0017] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the compressed air energy storage compression-side power tracking control method described in the first aspect embodiment.

[0018] Therefore, a basic dynamic model of a multi-stage compressor can be constructed. A hybrid power expression can be obtained by combining a dataset corrected for thermodynamic deviations, inter-stage coupling effects, and power surges in the instability region. Explicit efficiency expressions between the real-time operating efficiency of each compressor stage and its operating conditions can be established. Based on the hybrid power expression, the power tracking deviation can be determined, thereby constructing a multivariate objective function. Combined with preset safety constraints, a multi-objective model predictive control system can be constructed based on the explicit efficiency expression. The weighting coefficients in the multi-objective model predictive control system can be adjusted according to the needs of different operating scenarios to obtain adaptive control commands. This solves the problem that related technologies are prone to instability phenomena such as surge and blockage in compressor units when dealing with strong disturbances such as sudden changes in grid frequency and rapid fluctuations in wind and solar power, which can lead to mechanical damage and energy storage interruptions in severe cases. This improves the adaptive control effect in multiple scenarios.

[0019] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0020] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1This is a flowchart of a compressed air energy storage compression-side power tracking control method according to an embodiment of this application; Figure 2 This is a schematic diagram of the physical model of the compression side of a compressed air energy storage system according to an embodiment of this application; Figure 3 This is a block diagram illustrating a compressed air energy storage compression-side power tracking control according to an embodiment of this application; Figure 4 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0021] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0022] The following describes a compressed air energy storage compression-side power tracking control method according to embodiments of this application, with reference to the accompanying drawings. Addressing the issue mentioned in the background art where related technologies are prone to instability phenomena such as compressor surge and blockage when dealing with strong disturbances like sudden changes in grid frequency and wind / solar power, which can lead to mechanical damage and energy storage interruption in severe cases, this application provides a compressed air energy storage compression-side power tracking control method. This method constructs a multi-stage compressor basic dynamic model and obtains a hybrid power expression by combining thermodynamic deviation, inter-stage coupling effect, and power surge correction dataset in the instability region. It establishes explicit efficiency expressions between the real-time operating efficiency of each compressor stage and its operating conditions. Based on the hybrid power expression, it determines the power tracking deviation, thereby constructing a multivariate objective function. Combined with preset safety constraints, a multi-objective model predictive control system is constructed based on the explicit efficiency expression. The weight coefficients in the multi-objective model predictive control system are adjusted according to the needs of different operating scenarios to obtain adaptive control commands. This solves the problem that related technologies are prone to instability phenomena such as compressor surge and blockage when dealing with strong disturbances like sudden changes in grid frequency and wind / solar power, which can lead to mechanical damage and energy storage interruption in severe cases, thus improving the adaptive control effect in multiple scenarios.

[0023] Specifically, Figure 1 A flowchart of the compressed air energy storage compression-side power tracking control method provided in the embodiments of this application.

[0024] like Figure 1 As shown, the compressed air energy storage compression-side power tracking control method includes the following steps: In step S101, a basic dynamic model of a multi-stage compressor is constructed.

[0025] Specifically, in this embodiment, a basic dynamic model of a single-stage compressor can be constructed based on the principles of mass conservation, momentum conservation, and energy conservation. Then, based on the principles of mass flow rate, pressure, and temperature continuity, the single-stage compressors are connected in series to form a basic dynamic model of a multi-stage compressor. The basic dynamic model of the multi-stage compressor is as follows: ; ; ;

[0026] In step S102, the thermodynamic deviation correction dataset, the interstage coupling effect correction dataset, and the unstable region power mutation correction dataset are obtained. The thermodynamic power, interstage coupling power, and unstable mutation power are obtained based on the thermodynamic deviation correction dataset, the interstage coupling effect correction dataset, and the unstable region power mutation correction dataset, respectively. The mixed power expression is obtained based on the multi-stage compressor basic dynamic model, the thermodynamic power, the interstage coupling power, and the unstable mutation power.

[0027] In some embodiments, the thermodynamic deviation correction dataset, the interstage coupling effect correction dataset, and the unstable region power mutation correction dataset include: collecting measured thermodynamic parameter data of the compressor under multiple loads and multiple speeds, and constructing a thermodynamic deviation correction dataset based on the measured thermodynamic parameter data; performing valve step operation on each stage compressor in the multi-stage compressor system, and synchronously collecting the state variable time-series change curves of each stage compressor, and constructing an interstage coupling effect correction dataset based on the state variable time-series change curves; obtaining critical operating condition data through surge tests and blockage tests, and collecting multiple characteristic parameters at the moment of compressor instability, and using Fourier transform to extract phase features and amplitude attenuation features from the characteristic parameters, so as to construct an unstable region power mutation correction dataset based on the phase features and amplitude attenuation features.

[0028] Specifically, embodiments of this application can construct a neural network training dataset based on the deviation between measured data of multi-stage compressors and simulation data of a basic dynamic model. Specifically, regarding thermodynamic deviation, measured data of thermodynamic parameters such as temperature and pressure of the compressor under different loads and speeds are collected. After eliminating sensor noise using wavelet transform, this data is subtracted point-by-point from the steady-state simulation results of the basic dynamic model to construct a thermodynamic deviation correction dataset. Regarding interstage coupling, a valve step operation is performed on a single-stage compressor, and the time-series change curves of each state quantity during the transient response are recorded simultaneously. The time series of measured and simulated data are aligned, and the dynamic response deviation of each stage of the compressor is extracted to construct an interstage coupling effect correction dataset that includes nonlinear effects such as interstage pressure fluctuation transmission and thermal inertial coupling. Regarding compressor instability, critical operating condition data is obtained through surge and blockage tests. A high-frequency data acquisition system (sampling rate > 10kHz) is used to record characteristic parameters such as power oscillation waveforms and pressure pulsation spectra at the moment of instability. Fourier transform is used to extract the phase and amplitude attenuation characteristics of power mutations to construct a power mutation correction dataset for the instability region.

[0029] In step S103, a multidimensional mapping relationship between the real-time operating efficiency and operating conditions of each level of compressor is established, an explicit efficiency expression is generated, and the power tracking deviation is determined based on the hybrid power expression. Based on the power tracking deviation and the real-time operating efficiency of each level of compressor, a multivariate objective function is constructed. Based on the multivariate objective function and the preset safety constraints, a multi-objective model predictive control system is constructed according to the explicit efficiency expression.

[0030] Specifically, in this embodiment, the neural network can be trained based on a thermodynamic deviation correction dataset, an interstage coupling effect correction dataset, and an unstable region power mutation correction dataset, respectively. Symbolic regression is then used to transform the black-box parameters of the neural network into explicit efficiency expressions. Next, the physical model and the neural network output are superimposed to form a hybrid power expression, achieving high-precision modeling of the compressor power and completing the construction of a multi-objective model predictive control system. The construction process of the multi-objective model predictive control system is as follows: ; ; ; ; ; ; ; ; ;

[0031] Furthermore, embodiments of this application can collect real-time operating data of compressors at each stage, and construct a real-time efficiency expression for the multi-stage compressor through high-order polynomial fitting: ;

[0032] Furthermore, in embodiments of this application, an explicit power expression can be used as a power prediction formula to construct an MPC control framework and establish a state-space model: ; ; ; ;

[0033] Furthermore, in this embodiment, a multivariate objective function is constructed with power tracking deviation, real-time efficiency of each stage of compressor, and distances between each major state variable and the operating boundary as optimization objectives: ;

[0034] Furthermore, the compressor's steady-state safety boundary, temperature safety limit, and power constraint are as follows: ; ; ; In step S104, the requirements of different operating scenarios are obtained, and at least one weight coefficient in the number of multi-objective model predictive control systems is adjusted according to the requirements of different operating scenarios to obtain adaptive control instructions. The adaptive control instructions are then used to perform adaptive operation control of the multi-stage compressor in multiple scenarios.

[0035] Specifically, during operation, embodiments of this application can increase the weighting coefficient. Achieve power point tracking; equalization , , Achieve frequency modulation response; adjust Achieve safe control of the compressor.

[0036] Furthermore, in peak-shaving operation scenarios, the focus is on long-term economical operation, requiring the maintenance of optimal compressor efficiency, with power tracking as a secondary objective; therefore, it is appropriate to increase the efficiency-optimal term in the objective function. Weighting, reducing the power deviation term Weights, real-time adjustment of safety boundary weight coefficients In frequency regulation operation scenarios, the core requirement is rapid response to grid frequency fluctuations. Priority must be given to reducing power point tracking deviation to ensure rapid matching of compressor output power with grid demand, allowing for a short-term sacrifice of some operating efficiency to improve dynamic performance. When sudden changes in grid frequency trigger frequency regulation requirements, the focus should be on improving... The weighting coefficient prioritizes minimizing power point tracking deviation. Through MPC rolling optimization, control commands such as compressor speed increases and valve opening transients are rapidly generated, reducing response time to the second level to meet the primary frequency regulation requirements of the power grid. Short-term deviations from the compressor's highest efficiency point are allowed by reducing efficiency optimization factors. Weighting weakens the constraint of efficiency objectives on control variables, while setting a lower efficiency limit using explicit efficiency formulas to avoid excessive sacrifice of economy. Simultaneously, the safety boundary weight coefficient is dynamically adjusted based on the distance between the real-time state variable and the safety boundary. .

[0037] Therefore, the embodiments of this application can construct the power expression of the compressor by fusing physical models and neural networks. Addressing the need for rapid response of the compressed air energy storage compressor side to grid power, and while considering the operating efficiency of each level of compressor, model predictive control technology is adopted to achieve adaptive control for multiple scenarios such as peak shaving and frequency regulation by dynamically optimizing weighting coefficients.

[0038] Furthermore, to enable those skilled in the art to better understand the compressed air energy storage compression-side power tracking control method of this application, the following is combined with... Figure 2 Specific embodiments will be described below.

[0039] Figure 2 This is a schematic diagram of the physical model of the compression side of a compressed air energy storage system provided in one embodiment of this application.

[0040] like Figure 2 As shown, the physical model 20 of the compressed air energy storage system mainly includes: compressor 201, heat exchanger 202, air storage tank 203, heat storage tank 204, and cold storage tank 205.

[0041] Specifically, a basic dynamic model of a multi-stage compressor is constructed based on the principles of mass conservation, momentum conservation, and energy conservation. Secondly, a neural network training set is built based on the deviations between measured data of the multi-stage compressor and simulation data of the basic dynamic model. Specifically, a thermodynamic deviation correction dataset is constructed by mapping the deviations of thermodynamic parameters such as temperature and pressure during steady-state operation to their corresponding operating conditions; a dataset correcting the inter-stage coupling effect is constructed by mapping the deviations of system state variables during step actions of a single-stage compressor; and a dataset correcting power mutations in the instability region is constructed by mapping the surge and blockage test data of each stage of the compressor. Subsequently, the neural network is trained using these three datasets, and the neural network parameters are extracted to construct three types of corrected explicit expressions. Finally, the physical model and the neural network output are superimposed to form a hybrid power expression, achieving high-precision modeling of compressor power.

[0042] Furthermore, embodiments of this application can construct a multi-dimensional mapping relationship between the real-time operating efficiency of each compressor stage and its operating conditions, forming an explicit efficiency formula. Secondly, using power tracking deviation, the real-time efficiency of each compressor stage, and the distances between each major state variable and the operating boundary as multivariate objective functions, and considering the safety constraints of each control variable, the explicit power expression is used as the power prediction formula to construct an MPC control framework. Finally, during operation, the weight coefficients of each objective are adjusted in real time according to the operating requirements of different scenarios such as peak shaving and frequency regulation, achieving multi-scenario adaptive control effects.

[0043] According to the compressed air energy storage compression-side power tracking control method proposed in this application, a multi-stage compressor basic dynamic model is constructed. A hybrid power expression is obtained by combining thermodynamic deviation, inter-stage coupling effect, and power mutation correction dataset in the instability region. Explicit efficiency expressions are established between the real-time operating efficiency of each compressor stage and its operating conditions. Based on the hybrid power expression, the power tracking deviation is determined, thereby constructing a multivariate objective function. Combined with preset safety constraints, a multi-objective model predictive control system is constructed based on the explicit efficiency expression. The weight coefficients in the multi-objective model predictive control system are adjusted according to the needs of different operating scenarios to obtain adaptive control commands. This solves the problem that related technologies are prone to instability phenomena such as compressor surge and blockage when dealing with strong disturbances such as sudden changes in grid frequency and rapid fluctuations in wind and solar power, which can lead to mechanical damage to equipment and energy storage interruption in severe cases. This improves the adaptive control effect in multiple scenarios.

[0044] Next, the compressed air energy storage compression-side power tracking control device proposed in this application is described with reference to the accompanying drawings.

[0045] Figure 3 This is a block diagram of the compressed air energy storage compression-side power tracking control device proposed in an embodiment of this application.

[0046] like Figure 3As shown, the compressed air energy storage compression side power tracking control device 10 includes: a first building module 100, an acquisition module 200, a second building module 300, and a control module 400.

[0047] The system comprises the following modules: a first construction module 100, used to construct a basic dynamic model of a multi-stage compressor; an acquisition module 200, used to acquire a thermodynamic deviation correction dataset, an interstage coupling effect correction dataset, and an unstable region power mutation correction dataset, and obtain thermodynamic power, interstage coupling power, and unstable mutation power based on these datasets, respectively, and obtain a mixed power expression based on the basic dynamic model of the multi-stage compressor, thermodynamic power, interstage coupling power, and unstable mutation power; a second construction module 300, used to establish a multidimensional mapping relationship between the real-time operating efficiency and operating conditions of each stage of the compressor, generate an explicit efficiency expression, determine the power tracking deviation based on the mixed power expression, construct a multivariate objective function based on the power tracking deviation and the real-time operating efficiency of each stage of the compressor, construct a multi-objective model predictive control system based on the multivariate objective function and preset safety constraints, and construct a multi-objective model predictive control system based on the explicit efficiency expression; and a control module 400, used to acquire the requirements of different operating scenarios, adjust at least one weight coefficient in the multi-objective model predictive control system according to the requirements of different operating scenarios, obtain adaptive control commands, and perform adaptive operation control of the multi-stage compressor in multiple scenarios through the adaptive control commands.

[0048] Optionally, in some embodiments, the acquisition module 200 is specifically used for: acquiring measured thermodynamic parameter data of the compressor under multiple loads and multiple speeds, and constructing a thermodynamic deviation correction dataset based on the measured thermodynamic parameter data; performing valve step operation on each stage compressor in the multi-stage compressor system, and synchronously acquiring the state variable time-series change curve of each stage compressor, and constructing an inter-stage coupling effect correction dataset based on the state variable time-series change curve; obtaining critical operating condition data through surge test and blockage test, and acquiring multiple characteristic parameters at the moment of compressor instability, and extracting phase features and amplitude attenuation features from the characteristic parameters using Fourier transform, so as to construct a power mutation correction dataset in the instability region based on the phase features and amplitude attenuation features.

[0049] Optionally, in some embodiments, the basic dynamic model of the multi-stage compressor is: ; ; ;

[0050] Optionally, in some embodiments, the mixed power is: ;

[0051] Optionally, in some embodiments, the explicit efficiency expression is: ;

[0052] It should be noted that the foregoing explanation of the embodiment of the compressed air energy storage compression side power tracking control method also applies to the compressed air energy storage compression side power tracking control device of this embodiment, and will not be repeated here.

[0053] According to the compressed air energy storage compression-side power tracking control device proposed in this application, a multi-stage compressor basic dynamic model is constructed. A hybrid power expression is obtained by combining thermodynamic deviation, inter-stage coupling effect, and power mutation correction dataset in the instability region. Explicit efficiency expressions are established between the real-time operating efficiency of each compressor stage and its operating conditions. Based on the hybrid power expression, the power tracking deviation is determined, thereby constructing a multivariate objective function. Combined with preset safety constraints, a multi-objective model predictive control system is constructed based on the explicit efficiency expression. The weight coefficients in the multi-objective model predictive control system are adjusted according to the needs of different operating scenarios to obtain adaptive control commands. This solves the problem that related technologies are prone to instability phenomena such as compressor surge and blockage when dealing with strong disturbances such as sudden changes in grid frequency and rapid fluctuations in wind and solar power, which can lead to mechanical damage to equipment and energy storage interruption in severe cases. This improves the adaptive control effect in multiple scenarios.

[0054] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: The memory 401, the processor 402, and the computer program stored on the memory 401 and capable of running on the processor 402.

[0055] When the processor 402 executes the program, it implements the compressed air energy storage compression side power tracking control method provided in the above embodiments.

[0056] Furthermore, the electronic device also includes: Communication interface 403 is used for communication between memory 401 and processor 402.

[0057] The memory 401 is used to store computer programs that can run on the processor 402.

[0058] Memory 401 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0059] If the memory 401, processor 402, and communication interface 403 are implemented independently, then the communication interface 403, memory 401, and processor 402 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 4 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0060] Optionally, in a specific implementation, if the memory 401, processor 402, and communication interface 403 are integrated on a single chip, then the memory 401, processor 402, and communication interface 403 can communicate with each other through an internal interface.

[0061] Processor 402 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0062] This application also provides a computer-readable storage medium having a computer program stored thereon, which is implemented when executed by a processor. Figure 1 The compressed air energy storage compression-side power tracking control method described in the embodiment.

[0063] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0064] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0065] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0066] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0067] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0068] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it includes one or a combination of the steps of the method embodiments.

[0069] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0070] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for power tracking control on the compression side of compressed air energy storage, characterized in that, include: Construct a basic dynamic model of a multi-stage compressor; Acquire the thermodynamic deviation correction dataset, the interstage coupling effect correction dataset, and the unstable region power mutation correction dataset, and obtain the thermodynamic power, interstage coupling power, and unstable mutation power respectively based on the thermodynamic deviation correction dataset, the interstage coupling effect correction dataset, and the unstable region power mutation correction dataset. Then, obtain the mixed power expression based on the multi-stage compressor basic dynamic model, the thermodynamic power, the interstage coupling power, and the unstable mutation power. A multidimensional mapping relationship between the real-time operating efficiency and operating conditions of each level of compressor is established, an explicit efficiency expression is generated, and based on the hybrid power expression, the power tracking deviation is determined. Based on the power tracking deviation and the real-time operating efficiency of each level of compressor, a multivariate objective function is constructed. Based on the multivariate objective function and the preset safety constraints, a multi-objective model predictive control system is constructed according to the explicit efficiency expression. The system obtains the requirements of different operating scenarios, adjusts at least one weight coefficient in the multi-objective model predictive control system according to the requirements of the different operating scenarios, obtains adaptive control instructions, and uses the adaptive control instructions to perform adaptive operation control of the multi-stage compressor in multiple scenarios.

2. The method according to claim 1, characterized in that, The thermodynamic deviation correction dataset, the interstage coupling effect correction dataset, and the unstable region power mutation correction dataset include: Collect measured thermodynamic parameter data of the compressor under multiple loads and multiple speeds, and construct the thermodynamic deviation correction dataset based on the measured thermodynamic parameter data; A valve step operation is performed on each stage of the compressor in the multi-stage compressor system, and the time-series change curves of the state variables of each stage compressor are collected synchronously. Based on the time-series change curves of the state variables, the inter-stage coupling effect correction dataset is constructed. Critical operating condition data were obtained through surge and blockage tests, and multiple characteristic parameters of the compressor at the moment of instability were collected. Phase features and amplitude attenuation features were extracted from the characteristic parameters using Fourier transform, and a power mutation correction dataset for the instability region was constructed based on the phase features and amplitude attenuation features.

3. The method according to claim 1, characterized in that, The basic dynamic model of the multi-stage compressor is as follows: ； ； ； Braking torque at the impeller; i It is a positive integer.

4. The method according to claim 3, characterized in that, The mixed power is: ； power; For the first i Instantaneous power of the stage compressor; i and N All are positive integers.

5. The method according to claim 1, characterized in that, The explicit efficiency expression is: ； number; For residual terms; i , j , k , n All are positive integers.

6. A compressed air energy storage compression-side power tracking control device, characterized in that, include: The first building module is used to build the basic dynamic model of a multi-stage compressor. The acquisition module is used to acquire the thermodynamic deviation correction dataset, the interstage coupling effect correction dataset, and the unstable region power mutation correction dataset, and obtain the thermodynamic power, interstage coupling power, and unstable mutation power respectively based on the thermodynamic deviation correction dataset, the interstage coupling effect correction dataset, and the unstable region power mutation correction dataset, and obtain the mixed power expression based on the multi-stage compressor basic dynamic model, the thermodynamic power, the interstage coupling power, and the unstable mutation power; The second construction module is used to establish a multidimensional mapping relationship between the real-time operating efficiency and operating conditions of each level of compressor, generate an explicit efficiency expression, determine the power tracking deviation based on the hybrid power expression, construct a multivariate objective function based on the power tracking deviation and the real-time operating efficiency of each level of compressor, and construct a multi-objective model predictive control system based on the multivariate objective function and preset safety constraints, according to the explicit efficiency expression. The control module is used to acquire the requirements of different operating scenarios, and adjust at least one weight coefficient in the multi-objective model predictive control system according to the requirements of the different operating scenarios to obtain adaptive control instructions, and use the adaptive control instructions to adaptively control the operation of the multi-stage compressor in multiple scenarios.

7. The apparatus according to claim 6, characterized in that, The acquisition module is specifically used for: Collect measured thermodynamic parameter data of the compressor under multiple loads and multiple speeds, and construct the thermodynamic deviation correction dataset based on the measured thermodynamic parameter data; A valve step operation is performed on each stage of the compressor in the multi-stage compressor system, and the time-series change curves of the state variables of each stage compressor are collected synchronously. Based on the time-series change curves of the state variables, the inter-stage coupling effect correction dataset is constructed. Critical operating condition data were obtained through surge and blockage tests, and multiple characteristic parameters of the compressor at the moment of instability were collected. Phase features and amplitude attenuation features were extracted from the characteristic parameters using Fourier transform, and a power mutation correction dataset for the instability region was constructed based on the phase features and amplitude attenuation features.

8. The apparatus according to claim 6, characterized in that, The basic dynamic model of the multi-stage compressor is as follows: ； ； ； Braking torque at the impeller; i It is a positive integer.

9. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the compressed air energy storage compression-side power tracking control method as described in any one of claims 1-5.

10. A computer-readable storage medium storing a computer program, characterized in that, When the program is executed by the processor, it implements the compressed air energy storage compression side power tracking control method as described in any one of claims 1-5.

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