Compression side power tracking control method and device of compressed air energy storage system

Abstract

The invention relates to the technical field of novel energy, in particular to a compression side power tracking control method and device for a compressed air energy storage system, and the method comprises the steps: constructing a three-loop feedback incremental proportion integration differentiation (PID) control law based on a constructed transfer function model and a three-loop control framework of the compression side of the compressed air energy storage system; and training a preset radial basis function (RBF) neural network by using the target data set, updating an incremental PID target control parameter by using the trained neural network, and optimizing an incremental PID control law according to the target control parameter so as to execute a compression side power tracking control action on the compressed air energy storage system by using the optimized PID control law. Therefore, the problems that in the prior art, PID control parameters are set only according to engineering experience and experimental tests, comprehensive balance and optimization of efficiency and dynamic performance cannot be achieved, and economic benefits of the compression side of the compressed air energy storage power station are reduced are solved.

Description

Technical Field

[0001] The present invention relates to the field of new energy technologies, and in particular to a compression-side power tracking control method and device for a compressed air energy storage system. Background Art

[0002] With the proposal of the "dual carbon" goal, the large-scale access of renewable energy represented by wind power and photovoltaics to the power grid has brought significant volatility and uncertainty, bringing new challenges to the stable and efficient operation of the new power system. Energy storage technology can store excess electricity when there is a surplus of new energy, and release the stored electricity for load use during peak electricity consumption, effectively smoothing the fluctuations of new energy, which is an effective way to improve the power supply quality of the new power system. In recent years, compressed air energy storage technology, as one of the large-scale energy storage technologies that can be commercially used, is not restricted by geographical conditions, is green, safe and efficient, and has become a hot area of ​​common concern in industry and academia. The construction of related demonstration projects and commercial power stations is also gradually advancing.

[0003] At present, compressed air energy storage power stations are mainly used for new energy consumption and volatility smoothing on the power supply side to improve the external characteristics of renewable energy power stations such as wind power and photovoltaic power, and reduce their impact and influence on the power grid. In this scenario, the compressed air energy storage system needs to track the power power instructions. At present, compressed air energy storage power stations mainly use PID (Proportion Integration Differentiation) control algorithm to achieve real-time tracking of power instructions, and the tracking effect mainly depends on the parameter values ​​adjusted by engineering experience.

[0004] However, in the related art, since the compression side of the compressed air energy storage system is composed of a multi-stage composite compressor unit and a corresponding inter-stage heat exchanger, the coupling relationship between each control variable and the state variable is complex. The PID control parameters adjusted only based on engineering experience and experimental tests cannot achieve a comprehensive balance and optimization of efficiency and dynamic performance, which reduces the economic benefits of the compression side of the compressed air energy storage power station and needs to be solved urgently. Summary of the invention

[0005] The present invention provides a compression side power tracking control method and device for a compressed air energy storage system, so as to solve the problem that the PID control parameters adjusted only according to engineering experience and experimental tests in the related technology cannot achieve the comprehensive trade-off and optimization of efficiency and dynamic performance, thereby reducing the economic benefits of the compression side of the compressed air energy storage power station.

[0006] The first aspect of the present invention provides a compression side power tracking control method for a compressed air energy storage system, comprising the following steps: constructing a transfer function model of the compression side of the compressed air energy storage system, and constructing a three-loop control framework of the compression side of the compressed air energy storage system; constructing an incremental proportional integral differential PID control law with three-loop feedback based on the transfer function model and the three-loop control framework; using a target data set to train a pre-set RBF (Radial Basis Function) neural network to obtain a trained neural network, using the trained neural network to update the target control parameters of the incremental PID, and optimizing the incremental PID control law according to the target control parameters, so as to use the optimized PID control law to perform compression side power tracking control action on the compressed air energy storage system.

[0007] Optionally, in one embodiment of the present invention, constructing a transfer function model of the compression side of the compressed air energy storage system includes: constructing a transfer function model of the compression side of the compressed air energy storage system based on the target physical processes of the compressor, heat exchanger and gas storage reservoir of the compressed air energy storage system.

[0008] Optionally, in one embodiment of the present invention, constructing the three-loop control framework of the compressed air energy storage system includes: determining the three-loop control framework based on the speed control relationship, air mass flow rate control relationship and heat exchange medium control relationship when the compressor is running.

[0009] Optionally, in one embodiment of the present invention, the incremental PID is expressed as:

[0010] Λu(k)=K p (k-1)x c1 (k)+K i (k-1)x c2 (k)+K d (k-1)x c3 (k)

[0011]

[0012] Among them, K p , K i and K d are all adjustable parameters, k is the kth signal value after discretization, r(k) is the kth discrete power tracking instruction reference value, y(k) is the kth discrete system power output value, e c (k) The kth discrete deviation value.

[0013] Optionally, in one embodiment of the present invention, the use of the trained neural network to update the target control parameters of the incremental PID and optimize the incremental PID control law according to the target control parameters includes: obtaining the output power discrete sequence and the control law discrete sequence of the compressed air energy storage system in the target time period; based on the trained neural network, the output power discrete sequence and the control law discrete sequence, updating the target control parameters of the incremental PID in the current time period, and optimizing the incremental PID control law according to the target control parameters.

[0014] The second aspect of the present invention provides a compression-side power tracking control device for a compressed air energy storage system, comprising: a first construction module, used to construct a transfer function model of the compression side of the compressed air energy storage system, and to construct a three-loop control framework of the compression side of the compressed air energy storage system; a second construction module, used to construct an incremental proportional-integral-differential (PID) control law with three-loop feedback based on the transfer function model and the three-loop control framework; a control module, used to train a preset radial basis function (RBF) neural network using a target data set to obtain a trained neural network, use the trained neural network to update the target control parameters of the incremental PID, and optimize the incremental PID control law according to the target control parameters, so as to use the optimized PID control law to perform compression-side power tracking control action on the compressed air energy storage system.

[0015] Optionally, in one embodiment of the present invention, the first building module includes: a first building unit, used to build a transfer function model of the compression side of the compressed air energy storage system based on the target physical processes of the compressor, heat exchanger and gas storage reservoir of the compressed air energy storage system.

[0016] Optionally, in one embodiment of the present invention, the first building module includes: a second building unit, used to determine the three-loop control framework based on the speed control relationship, the air mass flow rate control relationship and the heat exchange medium control relationship when the compressor is running.

[0017] Optionally, in one embodiment of the present invention, the incremental PID is expressed as:

[0018] Λu(k)=K p (k-1)x c1 (k)+K i (k-1)x c2 (k)+K d (k-1)x c3 (k)

[0019]

[0020] Among them, Kp , K i and K d are all adjustable parameters, k is the kth signal value after discretization, r(k) is the kth discrete power tracking instruction reference value, y(k) is the kth discrete system power output value, e c (k) The kth discrete deviation value.

[0021] Optionally, in one embodiment of the present invention, the control module includes: an acquisition unit, used to acquire an output power discrete sequence and a control law discrete sequence of the compressed air energy storage system in a target time period; a control unit, used to update the target control parameters of the incremental PID in the current time period based on the trained neural network, the output power discrete sequence and the control law discrete sequence, and optimize the incremental PID control law according to the target control parameters.

[0022] An embodiment of the third aspect of the present invention provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the compression side power tracking control method of the compressed air energy storage system as described in the above embodiment.

[0023] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, which, when executed by a processor, implements the compression-side power tracking control method of a compressed air energy storage system as described above.

[0024] A fifth aspect of the present invention provides a computer program product, including a computer program, which, when executed, is used to implement the compression-side power tracking control method of the compressed air energy storage system as described above.

[0025] The embodiment of the present invention can construct an incremental proportional integral differential PID control law with three-loop feedback based on the constructed transfer function model and three-loop control framework of the compression side of the compressed air energy storage system; use the target data set to train a pre-set radial basis function RBF neural network, use the trained neural network to update the target control parameters of the incremental PID, and optimize the incremental PID control law according to the target control parameters, so as to use the optimized PID control law to perform compression side power tracking control actions on the compressed air energy storage system, effectively achieve a comprehensive trade-off and optimization of efficiency and dynamic performance, and improve the economic benefits of the compression side of the compressed air energy storage power station. Thus, the problem that the PID control parameters adjusted only according to engineering experience and experimental tests in the related technology cannot achieve a comprehensive trade-off and optimization of efficiency and dynamic performance, and reduce the economic benefits of the compression side of the compressed air energy storage power station is solved.

[0026] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0028] Figure 1 A flowchart of a compression-side power tracking control method of a compressed air energy storage system provided according to an embodiment of the present invention;

[0029] Figure 2 A schematic diagram of a three-loop control framework for compression-side power tracking control of a compressed air energy storage system according to a specific embodiment of the present invention;

[0030] Figure 3 A block diagram of a PID self-correcting control structure based on an RBF network according to a specific embodiment of the present invention;

[0031] Figure 4 A schematic diagram of the structure of a RBF neural network model is provided for a specific embodiment of the present invention;

[0032] Figure 5 A schematic structural diagram of a compression-side power tracking control device of a compressed air energy storage system provided according to an embodiment of the present invention;

[0033] Figure 6 It is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present invention.

[0034] Among them, 10-compression side power tracking control device of compressed air energy storage system; 100-first building module, 200-second building module and 300-control module; 601-memory, 602-processor and 603-communication interface. DETAILED DESCRIPTION

[0035] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0036] The following describes the compression side power tracking control method and device of the compressed air energy storage system of the embodiment of the present invention with reference to the accompanying drawings. In view of the problem that the PID control parameters in the related technologies mentioned in the background technology center are only adjusted according to engineering experience and experimental tests, and the comprehensive trade-off and optimization of efficiency and dynamic performance are not achieved, and the economic benefits of the compression side of the compressed air energy storage power station are reduced, the present invention provides a compression side power tracking control method of a compressed air energy storage system, in which a three-loop feedback incremental proportional integral differential PID control law can be constructed based on the constructed transfer function model and three-loop control framework of the compression side of the compressed air energy storage system; a pre-set radial basis function RBF neural network is trained using a target data set, and the target control parameters of the incremental PID are updated using the trained neural network, and the incremental PID control law is optimized according to the target control parameters, so as to use the optimized PID control law to perform compression side power tracking control actions on the compressed air energy storage system, effectively achieve a comprehensive trade-off and optimization of efficiency and dynamic performance, and improve the economic benefits of the compression side of the compressed air energy storage power station. This solves the problem that the PID control parameters in the related technology are adjusted only based on engineering experience and experimental tests, which cannot achieve a comprehensive balance and optimization of efficiency and dynamic performance, and reduces the economic benefits of the compression side of the compressed air energy storage power station.

[0037] Specifically, Figure 1 A schematic flow chart of a compression-side power tracking control method for a compressed air energy storage system provided in an embodiment of the present invention.

[0038] like Figure 1 As shown, the compression side power tracking control method of the compressed air energy storage system includes the following steps:

[0039] In step S101, a transfer function model of the compression side of the compressed air energy storage system is constructed, and a three-loop control framework of the compression side of the compressed air energy storage system is constructed.

[0040] It can be understood that the embodiments of the present invention can construct a transfer function model of the compression side of the compressed air energy storage system, such as a transfer function dynamic simulation model, and construct a three-loop control framework of the compression side of the compressed air energy storage system in the following steps, which effectively improves the feasibility of power tracking on the compression side of the compressed air energy storage system.

[0041] Among them, in one embodiment of the present invention, a transfer function model of the compression side of a compressed air energy storage system is constructed, including: based on the target physical processes of the compressor, heat exchanger and gas storage reservoir of the compressed air energy storage system, a transfer function model of the compression side of the compressed air energy storage system is constructed.

[0042] In the actual implementation process, the embodiment of the present invention can construct a compression side transfer function model of the compressed air energy storage system based on the target physical process of the compressor, heat exchanger and gas storage reservoir of the compressed air energy storage system, wherein the pressure ratio of the k-th compressor can be set to:

[0043]

[0044] in, and are the inlet and outlet pressures of the compressor, β c,k is the pressure ratio of the compressor.

[0045] The temperature of the compressor can be approximately calculated using the pressure ratio:

[0046]

[0047] in, and are the inlet and outlet temperatures of the compressor, γ is the multivariate coefficient.

[0048] Next, in the embodiment of the present invention, air can be regarded as an ideal gas, and the isentropic efficiency η of the k-th compressor is ci,k for:

[0049]

[0050] Therefore, the outlet temperature and power of the k-th compressor can be obtained as follows according to the embodiment of the present invention:

[0051]

[0052]

[0053] Among them, P c,k is the power of the kth compressor, c p,air is the specific heat capacity at constant pressure, is the mass flow rate.

[0054] In addition, the equation for the heat exchanger is:

[0055]

[0056] The subscripts air and HTF are air and heat transfer fluid, respectively. r is the energy efficiency coefficient of the heat exchanger, τ r is the heat transfer inertia time constant, and ΔT is the temperature change.

[0057] Among them, in one embodiment of the present invention, a three-loop control framework of a compressed air energy storage system is constructed, including: determining the three-loop control framework based on the speed control relationship, air mass flow rate control relationship and heat exchange medium control relationship when the compressor is running.

[0058] As a possible way to achieve this, Figure 2 As shown, it is a three-loop control framework for power tracking control on the compression side of the compressed air energy storage system, wherein the speed, air mass flow rate, and heat transfer medium mass flow rate of the compressor during operation comprehensively affect the real-time absorbed power on the compression side. Therefore, the embodiment of the present invention can measure the real-time working state of the compressor unit through a speed sensor and a flow sensor respectively, and calculate the real-time control signal through a PID adaptive correction control law, and then feed back the output control signal to the speed governor, throttle valve, and oil pump, thereby realizing the three-loop state variable control on the compression side of the compressed air energy storage system to optimize the power tracking effect of the compressed air energy storage system.

[0059] In step S102, based on the transfer function model and the three-loop control framework, an incremental proportional-integral-differential PID control law with three-loop feedback is constructed.

[0060] It can be understood that the embodiments of the present invention can construct an incremental proportional integral differential PID control law with three-loop feedback based on the transfer function model and the three-loop control framework. For example, the embodiments of the present invention can construct an incremental PID control law with three-loop feedback based on the incremental PID control algorithm, that is, a three-loop feedback control law of the speed-air mass flow rate-heat transfer oil mass flow rate of the compressed air energy storage system, which effectively improves the executability of the compression side power tracking control.

[0061] For example, Figure 3 As shown in FIG. 1 , it is a block diagram of the PID self-correction control structure based on the RBF network. The embodiment of the present invention can be set based on the transfer function model and the three-loop control framework in the above steps to design a PID adaptive correction controller based on the RBF neural network, wherein the PID part adopts incremental PID control, and the incremental PID can be expressed as:

[0062] Λu(k)=K p (k-1)x c1 (k)+K i (k-1)x c2 (k)+K d (k-1)x c3 (k)

[0063]

[0064] Among them, K p , K i and Kd are all adjustable parameters, k is the kth signal value after discretization, r(k) is the kth discrete power tracking instruction reference value, y(k) is the kth discrete system power output value, e c (k) The kth discrete deviation value.

[0065] Among them, K p , K i and K d The three parameters affect the performance of the compressed air energy storage control law. According to engineering experience, it is difficult for a single set of parameters to meet the operating requirements of different working conditions. Therefore, the RBF neural network optimization algorithm in the following steps can be used to update the parameters to meet the real-time optimization control under different working conditions.

[0066] In step S103, a preset radial basis function RBF neural network is trained using a target data set to obtain a trained neural network, the trained neural network is used to update the target control parameters of the incremental PID, and the incremental PID control law is optimized according to the target control parameters, so as to use the optimized PID control law to perform compression-side power tracking control action on the compressed air energy storage system.

[0067] In an embodiment of the present invention, the embodiment of the present invention can use the gradient descent method to adjust each incremental PID control parameter, which can be expressed as:

[0068]

[0069] Among them, ΔK p , ΔK i , ΔK d All are changes in PID adjustable parameters. is the partial derivative of the system power output value to the PID control law, η c is the gradient descent rate, which can be set according to the actual value and is not specifically limited here.

[0070] The parameter adjustment algorithms are as follows:

[0071]

[0072] Among them, K p , K i and K d are adjustable parameters, α c is the momentum term factor, which can be set according to actual conditions and is not specifically limited here.

[0073] In the above formula, since the compressor and heat exchanger are strong nonlinear models, It is difficult to solve quickly and accurately, so Figure 4As shown in the figure, it is a schematic diagram of the RBF neural network model structure. In the embodiment of the present invention, the RBF neural network can be used for nonlinear approximate fitting to solve

[0074] Specifically, in the RBF model structure, x i =[x1,x2,...,x n ] is the input vector, where i = 1, 2, ..., n; h j =[h1,h2,...,h m ] is a radial basis vector, where j = 1, 2, ..., m. The mapping from the input layer to the hidden layer is mapped using a radial basis function, as shown below:

[0075]

[0076] Among them, c j is the node center vector, σ is the network base width, h j is the radial basis vector, x i is the input vector.

[0077] The mapping from the hidden layer to the output layer is a linear transformation, and the output of the RBF neural network is:

[0078]

[0079] Among them, w kj is the weight vector from the hidden layer to the output layer.

[0080] Therefore, the embodiment of the present invention can use the data set collected by the actual system in the following steps to train the RBF neural network to obtain the trained neural network, use the trained neural network to update the target control parameters of the incremental PID, and optimize the incremental PID control law according to the target control parameters, so that the optimized PID control law can be used to perform compression side power tracking control actions on the compressed air energy storage system, effectively achieve a comprehensive trade-off and optimization of efficiency and dynamic performance, and improve the economic benefits of the compression side of the compressed air energy storage power station.

[0081] Among them, in one embodiment of the present invention, the target control parameters of the incremental PID are updated using the trained neural network, and the incremental PID control law is optimized according to the target control parameters, including: obtaining the output power discrete sequence and the control law discrete sequence of the compressed air energy storage system in the target period; based on the trained neural network, the output power discrete sequence and the control law discrete sequence, the target control parameters of the incremental PID in the current period are updated, and the incremental PID control law is optimized according to the target control parameters.

[0082] In some embodiments, the input of the RBF network in the embodiment of the present invention is the discrete sequence of power output on the compression side of the compressed air energy storage system and the discrete sequence of the control law, and the output is The objective function is the square value of the power tracking deviation. The actual system is used to collect data sets. After the RBF neural network is trained with the data sets, the trained RBF neural network can update the incremental PID control parameters of the current period based on the system output of the past period and the control law of the past period, and optimize the incremental PID control law according to the updated PID control parameters. That is, the embodiment of the present invention can realize self-correction control correction of incremental PID parameters through the trained RBF neural network, and finally embed the discrete control law into the hardware system to realize the optimized control of compression side power tracking, thereby improving the compression side power tracking control effect.

[0083] According to the compression side power tracking control method of the compressed air energy storage system proposed in the embodiment of the present invention, based on the constructed transfer function model and three-loop control framework of the compression side of the compressed air energy storage system, an incremental proportional integral differential PID control law with three-loop feedback can be constructed; a pre-set radial basis function RBF neural network is trained using a target data set, the target control parameters of the incremental PID are updated using the trained neural network, and the incremental PID control law is optimized according to the target control parameters, so as to use the optimized PID control law to perform compression side power tracking control actions on the compressed air energy storage system, effectively achieve a comprehensive trade-off and optimization of efficiency and dynamic performance, and improve the economic benefits of the compression side of the compressed air energy storage power station. Thus, the problem that the PID control parameters that are adjusted only based on engineering experience and experimental tests in the related technology cannot achieve a comprehensive trade-off and optimization of efficiency and dynamic performance, and reduce the economic benefits of the compression side of the compressed air energy storage power station is solved.

[0084] Next, the compression-side power tracking control device of the compressed air energy storage system proposed in accordance with an embodiment of the present invention will be described with reference to the accompanying drawings.

[0085] Figure 5 It is a block diagram of a compression-side power tracking control device of a compressed air energy storage system according to an embodiment of the present invention.

[0086] like Figure 5 As shown, the compression-side power tracking control device 10 of the compressed air energy storage system includes: a first building module 100, a second building module 200 and a control module 300.

[0087] Specifically, the first construction module 100 is used to construct a transfer function model of the compression side of the compressed air energy storage system, and to construct a three-loop control framework of the compression side of the compressed air energy storage system.

[0088] The second building module 200 is used to build an incremental proportional-integral-differential PID control law with three-loop feedback based on the transfer function model and the three-loop control framework.

[0089] The control module 300 is used to train a preset radial basis function (RBF) neural network using a target data set to obtain a trained neural network, update target control parameters of an incremental PID using the trained neural network, and optimize the incremental PID control law according to the target control parameters, so as to use the optimized PID control law to perform compression-side power tracking control actions on a compressed air energy storage system.

[0090] Optionally, in one embodiment of the present invention, the first building module 100 includes: a first building unit.

[0091] Among them, the first construction unit is used to construct a transfer function model of the compression side of the compressed air energy storage system based on the target physical processes of the compressor, heat exchanger and gas storage reservoir of the compressed air energy storage system.

[0092] Optionally, in one embodiment of the present invention, the first building block 100 includes: a second building unit.

[0093] The second construction unit is used to determine a three-loop control framework based on a speed control relationship, an air mass flow rate control relationship, and a heat exchange medium control relationship when the compressor is running.

[0094] Optionally, in one embodiment of the present invention, the incremental PID is expressed as:

[0095] Λu(k)=K p (k-1)x c1 (k)+K i (k-1)x c2 (k)+K d (k-1)x c3 (k)

[0096]

[0097] Among them, K p , K i and K d are all adjustable parameters, k is the kth signal value after discretization, r(k) is the kth discrete power tracking instruction reference value, y(k) is the kth discrete system power output value, e c (k) The kth discrete deviation value.

[0098] Optionally, in one embodiment of the present invention, the control module 300 includes: an acquisition unit and a control unit.

[0099] Among them, the acquisition unit is used to obtain the output power discrete sequence and control law discrete sequence of the compressed air energy storage system in the target period.

[0100] The control unit is used to update the target control parameters of the incremental PID in the current period based on the trained neural network, the output power discrete sequence and the control law discrete sequence, and optimize the incremental PID control law according to the target control parameters.

[0101] It should be noted that the aforementioned explanation of the embodiment of the compression-side power tracking control method of the compressed air energy storage system is also applicable to the compression-side power tracking control device of the compressed air energy storage system of this embodiment, and will not be repeated here.

[0102] According to the compression side power tracking control device of the compressed air energy storage system proposed in the embodiment of the present invention, based on the constructed transfer function model and three-loop control framework of the compression side of the compressed air energy storage system, an incremental proportional integral differential PID control law with three-loop feedback can be constructed; a pre-set radial basis function RBF neural network is trained using a target data set, the target control parameters of the incremental PID are updated using the trained neural network, and the incremental PID control law is optimized according to the target control parameters, so as to use the optimized PID control law to perform compression side power tracking control actions on the compressed air energy storage system, effectively realize the comprehensive trade-off and optimization of efficiency and dynamic performance, and improve the economic benefits of the compression side of the compressed air energy storage power station. Thus, the problem that the PID control parameters adjusted only according to engineering experience and experimental tests in the related technology cannot realize the comprehensive trade-off and optimization of efficiency and dynamic performance, and reduce the economic benefits of the compression side of the compressed air energy storage power station is solved.

[0103] Figure 6 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. The electronic device may include:

[0104] A memory 601 , a processor 602 , and a computer program stored in the memory 601 and executable on the processor 602 .

[0105] When the processor 602 executes the program, the compression-side power tracking control method of the compressed air energy storage system provided in the above embodiment is implemented.

[0106] Furthermore, the electronic device further comprises:

[0107] The communication interface 603 is used for communication between the memory 601 and the processor 602 .

[0108] The memory 601 is used to store computer programs that can be executed on the processor 602 .

[0109] The memory 601 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0110] If the memory 601, the processor 602 and the communication interface 603 are implemented independently, the communication interface 603, the memory 601 and the processor 602 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 6 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0111] Optionally, in a specific implementation, if the memory 601, the processor 602 and the communication interface 603 are integrated on a chip, the memory 601, the processor 602 and the communication interface 603 can communicate with each other through an internal interface.

[0112] The processor 602 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention.

[0113] This embodiment also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the compression-side power tracking control method of the compressed air energy storage system as described above is implemented.

[0114] This embodiment also provides a computer program product, including a computer program, which, when executed, is used to implement the compression-side power tracking control method of the compressed air energy storage system as described above.

[0115] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0116] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "N" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0117] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present invention belong.

[0118] 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 combination with these instruction execution systems, devices or apparatuses. For the purpose 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 combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways as necessary and then storing it in a computer memory.

[0119] 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 embodiment, the N 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 or a combination of the following technologies known in the art: 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.

[0120] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0121] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0122] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A compression side power tracking control method for a compressed air energy storage system, characterized in that: The following steps are involved: Constructing a transfer function model of the compression side of the compressed air energy storage system, and constructing a three-loop control framework of the compression side of the compressed air energy storage system; Based on the transfer function model and the three-loop control framework, an incremental proportional-integral-differential PID control law with three-loop feedback is constructed; A pre-set radial basis function (RBF) neural network is trained using a target data set to obtain a trained neural network, and the trained neural network is used to update the target control parameters of the incremental PID. The incremental PID control law is optimized according to the target control parameters, so as to use the optimized PID control law to perform compression-side power tracking control action on the compressed air energy storage system.

2. The compression side power tracking control method of the compressed air energy storage system according to claim 1, characterized in that: The construction of the transfer function model of the compression side of the compressed air energy storage system includes: Based on the target physical processes of the compressor, heat exchanger and gas storage reservoir of the compressed air energy storage system, a transfer function model of the compression side of the compressed air energy storage system is constructed.

3. The compression side power tracking control method of the compressed air energy storage system according to claim 1, characterized in that: The three-loop control framework for constructing the compressed air energy storage system includes: The three-loop control framework is determined based on the speed control relationship, the air mass flow rate control relationship and the heat exchange medium control relationship when the compressor is running.

4. The compression side power tracking control method of the compressed air energy storage system according to claim 1, characterized in that: The incremental PID is expressed as: Λu(k)=K p (k-1)x c1 (k)+K i (k-1)x c2 (k)+K d (k-1)x c3 (k) Among them, K p , K i and K d are all adjustable parameters, k is the kth signal value after discretization, r(k) is the kth discrete power tracking instruction reference value, y(k) is the kth discrete system power output value, e c (k) The kth discrete deviation value.

5. The compression side power tracking control method of the compressed air energy storage system according to claim 1, characterized in that: The method of updating the target control parameters of the incremental PID by using the trained neural network and optimizing the incremental PID control law according to the target control parameters includes: Obtaining a discrete sequence of output power and a discrete sequence of control laws of the compressed air energy storage system during a target period; Based on the trained neural network, the output power discrete sequence and the control law discrete sequence, the target control parameters of the incremental PID in the current period are updated, and the incremental PID control law is optimized according to the target control parameters.

6. A compression side power tracking control device for a compressed air energy storage system, characterized in that: include: The first construction module is used to construct a transfer function model of the compression side of the compressed air energy storage system and to construct a three-loop control framework of the compression side of the compressed air energy storage system; A second building module is used to build an incremental proportional integral differential PID control law of three-loop feedback based on the transfer function model and the three-loop control framework; A control module is used to train a preset radial basis function (RBF) neural network using a target data set to obtain a trained neural network, update target control parameters of the incremental PID using the trained neural network, and optimize the incremental PID control law according to the target control parameters, so as to use the optimized PID control law to perform compression-side power tracking control action on the compressed air energy storage system.

7. The compression side power tracking control device of the compressed air energy storage system according to claim 6, characterized in that: The first building block comprises: The first construction unit is used to construct a transfer function model of the compression side of the compressed air energy storage system based on the target physical processes of the compressor, heat exchanger and gas storage reservoir of the compressed air energy storage system.

8. The compression side power tracking control device of the compressed air energy storage system according to claim 6, characterized in that: The first building block comprises: The second construction unit is used to determine the three-loop control framework based on the speed control relationship, the air mass flow rate control relationship and the heat exchange medium control relationship when the compressor is running.

9. The compression side power tracking control device of the compressed air energy storage system according to claim 6, characterized in that: The incremental PID is expressed as: Λu(k)=K p (k-1)x c1 (k)+K i (k-1)x c2 (k)+K d (k-1)x c3 (k) Among them, K p , K i and K d are all adjustable parameters, k is the kth signal value after discretization, r(k) is the kth discrete power tracking instruction reference value, y(k) is the kth discrete system power output value, e c (k) The kth discrete deviation value.

10. The compression side power tracking control device of the compressed air energy storage system according to claim 6, characterized in that: The control module comprises: An acquisition unit, used to acquire an output power discrete sequence and a control law discrete sequence of the compressed air energy storage system in a target period; A control unit is used to update the target control parameters of the incremental PID in the current period based on the trained neural network, the output power discrete sequence and the control law discrete sequence, and optimize the incremental PID control law according to the target control parameters.

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

12. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the compression-side power tracking control method of a compressed air energy storage system as described in any one of claims 1 to 5.

13. A computer program product, comprising a computer program, characterized in that The computer program is executed by a processor to implement the compression-side power tracking control method of a compressed air energy storage system as described in any one of claims 1 to 5.

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