Compressed air energy storage multistage turbine interstage parameter optimization method and device

Abstract

The invention relates to a compressed air energy storage multistage turbine interstage parameter optimization method and device, and the method comprises the steps: firstly obtaining a plurality of representative turbine inlet pressure dynamic attenuation curves as design input according to the characteristics of a gas storage; secondly, constructing an optimization function taking the multi-scene integrated full energy release period weighted average efficiency as a target; then, a variable set containing the collaborative mapping relation of all levels of parameters is determined; and finally, the optimal turbine design parameters are obtained through global optimization solution. Therefore, according to the embodiment of the invention, the defect of low efficiency of a traditional fixed working condition point design in compressed air energy storage variable working condition operation can be overcome, and through the dynamic collaborative design of the interstage parameters, the operation efficiency, adaptability and system economy of the turbine under the variable working condition are remarkably improved. Therefore, the problem that the actual operation efficiency is low due to the fact that an existing multi-stage turbine design method cannot adapt to the dynamic pressure attenuation characteristic of the compressed air energy storage system is solved.

Description

Method and apparatus for optimizing interstage parameters of multi-stage compressed air energy storage turbine Technical Field

[0001] This application relates to the field of new energy technology, and in particular to a method and apparatus for optimizing interstage parameters of a multi-stage compressed air energy storage turbine. Background Technology

[0002] Currently, the design methods for multi-stage turbines are usually based on fixed inlet parameters (such as pressure and temperature) as the design operating point. They employ isentropic optimization or three-dimensional flow theory to allocate parameters such as enthalpy drop, reaction degree, and flow coefficient for each stage in order to achieve the highest isentropic efficiency at the design point.

[0003] However, during the energy release process of a compressed air energy storage system, the pressure in the storage tank (such as a salt cavern or storage tank) drops significantly and continuously from its peak pressure. This results in a dynamic process where the inlet pressure and flow rate of the turbine expander are continuously changing throughout the entire power generation cycle. When a turbine designed using the traditional fixed operating point method operates under such dynamic pressure conditions, its first-stage turbine will deviate from its design operating condition first due to the continuous drop in inlet pressure, leading to a sharp decrease in efficiency. Although subsequent stages can maintain a relatively stable pressure ratio through valve control, the overall efficiency of the entire cascaded turbine system is constrained by the least efficient stage. Therefore, in the actual operating environment of compressed air energy storage, the overall efficiency of a turbine designed based on a fixed operating point will be lower than the design peak, resulting in significant performance loss.

[0004] In summary, although existing technologies can optimize the inter-stage matching or local parameters of multi-stage turbines, their optimization objectives are still within the scope of fixed operating conditions or steady-state assumptions, and cannot fundamentally solve the efficiency mismatch problem caused by the inherent pressure decay characteristics of the system, which urgently needs to be addressed. Summary of the Invention

[0005] This application provides a method and apparatus for optimizing interstage parameters of a multi-stage compressed air energy storage turbine, in order to solve the problems that existing multi-stage turbine design methods cannot adapt to the dynamic pressure decay characteristics of compressed air energy storage systems, resulting in low actual operating efficiency.

[0006] The first aspect of this application provides a method for optimizing interstage parameters of a multi-stage turbine in compressed air energy storage, comprising the following steps: obtaining multiple dynamic decay curves of the gas storage tank pressure that meet preset representative requirements during the energy release process of the target compressed air energy storage system, and discretizing the multiple dynamic decay curves into multiple representative operating points; calculating the weighted average efficiency corresponding to the multiple representative operating points under different scenarios, and determining the total number of representative operating points and the weight corresponding to each representative operating point, so as to construct a system-level optimization function based on the weighted average efficiency, the total number of representative operating points, and the weight; obtaining the first-stage turbine reference parameters corresponding to the target compressed air energy storage system, and determining the collaborative mapping function of the reference parameters of each stage of the turbine in the target compressed air energy storage system other than the first-stage turbine reference parameters as the system pressure of the target compressed air energy storage system dynamically changes, so as to construct a collaborative optimization variable set based on the first-stage turbine reference parameters and the collaborative mapping function; and iteratively solving the collaborative optimization variable set based on the system-level optimization function to obtain the target turbine design parameters that meet the preset optimization requirements for the target compressed air energy storage system.

[0007] Optionally, in one embodiment of this application, obtaining multiple dynamic decay curves of the gas storage tank pressure that meet preset representative requirements during the energy release process of the target compressed air energy storage system, and discretizing the multiple dynamic decay curves into multiple representative operating points, includes: obtaining the gas storage tank volume, initial pressure, and energy release power requirement of the target compressed air energy storage system, and establishing multiple gas storage tank pressure decay functions based on the gas storage tank volume, the initial pressure, and the energy release power requirement; and determining the corresponding dynamic decay curves according to the multiple gas storage tank pressure decay functions.

[0008] Optionally, in one embodiment of this application, the step of iteratively solving the collaborative optimization variable set based on the system-level optimization function to obtain the target turbine design parameters corresponding to the target compressed air energy storage system that meet the preset optimization requirements includes: constructing a global optimization model under different scenarios based on the system-level optimization function, the collaborative optimization variable set, and a preset step-by-step aerodynamic thermodynamic calculation module; inputting each design variable vector to be optimized in the collaborative optimization variable set into the global optimization model under different scenarios, so as to calculate multiple state parameters corresponding to the target compressed air energy storage system step by step through the step-by-step aerodynamic thermodynamic calculation module, and calculating the efficiency and power of the target compressed air energy storage system under each scenario based on the multiple state parameters; determining the comprehensive performance index of the global optimization model under different scenarios based on the efficiency and the power, and iteratively solving the collaborative optimization variable set using the system-level optimization function to obtain the target design variable vector to be optimized when the comprehensive performance index reaches its maximum value, and using the design variable vector to be optimized as the target turbine design parameters.

[0009] Optionally, in one embodiment of this application, the mathematical expression of the system-level optimization function is:

[0010] Where N is the total number of representative operating conditions; Let be the isentropic efficiency at the k-th representative operating point; This represents the vector of design variables to be optimized. This represents the weight of the k-th representative working condition point; This represents the gas pressure value of the gas storage facility at the k-th working point in the j-th scenario; This represents the system-level optimization function for the j-th scenario.

[0011] A second aspect of this application provides a multi-stage turbine stage parameter optimization device for compressed air energy storage, comprising: a discretization module, used to acquire multiple dynamic decay curves of the gas storage tank pressure that meet preset representative requirements during the energy release process of the target compressed air energy storage system, and to discretize the multiple dynamic decay curves into multiple representative operating points; and a first construction module, used to calculate the weighted average efficiency corresponding to the multiple representative operating points under different scenarios, and to determine the total number of representative operating points and the weight corresponding to each representative operating point, so as to construct a system-level optimization function based on the weighted average efficiency, the total number of representative operating points, and the weight. The second construction module is used to obtain the first-level turbine reference parameters corresponding to the target compressed air energy storage system, and determine the collaborative mapping function of the turbine reference parameters of each level in the target compressed air energy storage system other than the first-level turbine reference parameters as the system pressure of the target compressed air energy storage system changes dynamically, so as to construct a collaborative optimization variable set based on the first-level turbine reference parameters and the collaborative mapping function; the solution module is used to iteratively solve the collaborative optimization variable set based on the system-level optimization function to obtain the target turbine design parameters corresponding to the target compressed air energy storage system that meet the preset optimization requirements.

[0012] Optionally, in one embodiment of this application, the discrete module includes: an acquisition unit, configured to acquire the gas storage tank volume, initial pressure, and energy release power requirement of the target compressed air energy storage system, and establish multiple gas storage tank pressure decay functions based on the gas storage tank volume, the initial pressure, and the energy release power requirement; and a determination unit, configured to determine the corresponding dynamic decay curve according to the multiple gas storage tank pressure decay functions.

[0013] Optionally, in one embodiment of this application, the solution module includes: a modeling unit, used to construct a global optimization model for different scenarios based on the system-level optimization function, the collaborative optimization variable set, and a preset step-by-step aero-thermodynamic calculation module; a calculation unit, used to input each design variable vector to be optimized from the collaborative optimization variable set into the global optimization model for different scenarios, so as to calculate multiple state parameters corresponding to the target compressed air energy storage system step by step through the step-by-step aero-thermodynamic calculation module, and calculate the efficiency and power of the target compressed air energy storage system in each scenario based on the multiple state parameters; and an optimization unit, used to determine the comprehensive performance index of the global optimization model in different scenarios based on the efficiency and the power, and use the system-level optimization function to iteratively solve the collaborative optimization variable set to obtain the target design variable vector to be optimized when the comprehensive performance index reaches its maximum value, and use the design variable vector to be optimized as the target turbine design parameter.

[0014] Optionally, in one embodiment of this application, the mathematical expression of the system-level optimization function is:

[0015] Where N is the total number of representative operating conditions; Let be the isentropic efficiency at the k-th representative operating point; This represents the vector of design variables to be optimized. This represents the weight of the k-th representative working condition point; This represents the gas pressure value of the gas storage facility at the k-th working point in the j-th scenario; This represents the system-level optimization function for the j-th scenario.

[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, wherein the processor executes the program to implement the method for optimizing interstage parameters of a multi-stage compressed air energy storage turbine as described in the above embodiments.

[0017] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for optimizing interstage parameters of a multi-stage compressed air energy storage turbine.

[0018] A fifth aspect of this application provides a computer program product, including a computer program that is executed to implement the above-described method for optimizing interstage parameters of a multi-stage compressed air energy storage turbine.

[0019] Therefore, the embodiments of this application have the following beneficial effects: The embodiments of this application can obtain multiple dynamic decay curves of the gas storage pressure that meet the preset representative requirements during the energy release process of the target compressed air energy storage system, and discretize the multiple dynamic decay curves into multiple representative operating points; calculate the weighted average efficiency corresponding to multiple representative operating points under different scenarios, and determine the total number of representative operating points and the weight corresponding to each representative operating point, so as to construct a system-level optimization function based on the weighted average efficiency, the total number of representative operating points and the weights; obtain the first-level turbine reference parameters corresponding to the target compressed air energy storage system, and determine the collaborative mapping function of the turbine reference parameters of each level in the target compressed air energy storage system other than the first-level turbine reference parameters as the system pressure of the target compressed air energy storage system changes dynamically, so as to construct a collaborative optimization variable set based on the first-level turbine reference parameters and the collaborative mapping function; and iteratively solve the collaborative optimization variable set based on the system-level optimization function to obtain the target turbine design parameters that meet the preset optimization requirements of the target compressed air energy storage system. This application significantly improves the turbine's operating efficiency, adaptability, and system economy under varying operating conditions through dynamic collaborative design of interstage parameters, enabling the turbine to maintain high-efficiency operation throughout the entire energy release cycle and possessing stronger engineering applicability. This solves the problem that existing multi-stage turbine design methods cannot adapt to the dynamic pressure decay characteristics of compressed air energy storage systems, leading to low actual operating efficiency.

[0020] 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

[0021] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which: Figure 1 is a flowchart of a method for optimizing interstage parameters of a multi-stage compressed air energy storage turbine according to an embodiment of this application; Figure 2 is a schematic diagram of the execution logic of a method for optimizing interstage parameters of a multi-stage compressed air energy storage turbine according to an embodiment of this application; Figure 3 is an example diagram of a device for optimizing interstage parameters of a multi-stage compressed air energy storage turbine according to an embodiment of this application; Figure 4 is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0022] Among them, 10-compressed air energy storage multi-stage turbine interstage parameter optimization device; 100-discrete module, 200-first construction module, 300-second construction module, 400-solution module; 401-memory, 402-processor, 403-communication interface. Detailed Implementation

[0023] 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.

[0024] The following describes, with reference to the accompanying drawings, a method and apparatus for optimizing interstage parameters of a multi-stage compressed air energy storage turbine according to embodiments of this application. To address the problems mentioned in the background art, this application provides a method for optimizing interstage parameters of a multi-stage turbine in compressed air energy storage. In this method, multiple dynamic decay curves of the gas storage tank pressure meeting preset representative requirements are obtained during the energy release process of the target compressed air energy storage system. These dynamic decay curves are discretized into multiple representative operating points. The weighted average efficiency corresponding to these representative operating points under different scenarios is calculated, and the total number of representative operating points and the weight corresponding to each representative operating point are determined. Based on the weighted average efficiency, the total number of representative operating points, and the weights, a system-level optimization function is constructed. The first-stage turbine reference parameters corresponding to the target compressed air energy storage system are obtained, and a collaborative mapping function is determined for the dynamic changes of the reference parameters of each stage of the turbine in the target compressed air energy storage system (excluding the first-stage turbine reference parameters) with the system pressure of the target compressed air energy storage system. Based on the first-stage turbine reference parameters and the collaborative mapping function, a collaborative optimization variable set is constructed. Based on the system-level optimization function, the collaborative optimization variable set is iteratively solved to obtain the target turbine design parameters that meet the preset optimization requirements for the target compressed air energy storage system. This application significantly improves the turbine's operating efficiency, adaptability, and system economy under varying operating conditions through dynamic collaborative design of interstage parameters, enabling the turbine to maintain high-efficiency operation throughout the entire energy release cycle and possessing stronger engineering applicability. This solves the problem that existing multi-stage turbine design methods cannot adapt to the dynamic pressure decay characteristics of compressed air energy storage systems, leading to low actual operating efficiency.

[0025] Specifically, Figure 1 is a flowchart of a method for optimizing interstage parameters of a multi-stage compressed air energy storage turbine provided in an embodiment of this application.

[0026] As shown in Figure 1, the method for optimizing the interstage parameters of a multi-stage turbine for compressed air energy storage includes the following steps: In step S101, multiple dynamic decay curves of the gas storage tank pressure that meet the preset representative requirements during the energy release process of the target compressed air energy storage system are obtained, and the multiple dynamic decay curves are discretized into multiple representative operating points.

[0027] This application embodiment first obtains multiple representative dynamic decay curves of the gas storage tank pressure during the energy release process of the compressed air energy storage system, and discretizes them into multiple representative operating points, thereby providing reliable data guidance and basis for the subsequent construction of system-level optimization functions.

[0028] Optionally, in one embodiment of this application, multiple dynamic decay curves of the gas storage tank pressure that meet preset representative requirements during the energy release process of the target compressed air energy storage system are obtained, and the multiple dynamic decay curves are discretized into multiple representative operating points, including: obtaining the gas storage tank volume, initial pressure and energy release power requirements of the target compressed air energy storage system, and establishing multiple gas storage tank pressure decay functions based on the gas storage tank volume, initial pressure and energy release power requirements; and determining the corresponding dynamic decay curves according to the multiple gas storage tank pressure decay functions.

[0029] It should be noted that the embodiments of this application can establish multiple typical gas storage pressure decay functions representing different operating scenarios based on the gas storage volume, initial pressure, and energy release power requirements of the target compressed air energy storage power station through a gas storage thermodynamic model. This function serves as the basic input and boundary condition for all subsequent optimization work, determining the operating scenarios faced by the turbine throughout the entire energy release cycle. It comprehensively covers pressure decay scenarios corresponding to different gas storage volumes, different energy release power requirements, and different initial pressures.

[0030] Furthermore, to facilitate calculation and solution, the embodiments of this application require that continuous functions be... Discretize it into a series of representative operating points.

[0031] In step S102, the weighted average efficiency corresponding to multiple representative working points under different scenarios is calculated, and the total number of representative working points and the weight corresponding to each representative working point are determined, so as to construct a system-level optimization function based on the weighted average efficiency, the total number of representative working points and the weight.

[0032] Furthermore, embodiments of this application also require the construction of a system-level optimization function with the weighted average efficiency of multiple representative operating points in multiple scenarios as the objective, thereby effectively ensuring the acquisition of optimal turbine design parameters.

[0033] Optionally, in one embodiment of this application, the mathematical expression of the system-level optimization function is:

[0034] Where N is the total number of representative operating conditions; Let be the isentropic efficiency at the k-th representative operating point; This represents the vector of design variables to be optimized. This represents the weight of the k-th representative working condition point; This represents the gas pressure value of the gas storage facility at the k-th working point in the j-th scenario; This represents the system-level optimization function for the j-th scenario.

[0035] After establishing the dynamic design input (i.e., multiple representative operating points), this application's embodiments define a novel optimization objective; compared to the traditional method of pursuing isentropic efficiency at a single design point. To maximize the efficiency across different scenarios, the optimization objective of this application embodiment is to maximize the weighted average efficiency over the entire energy release cycle, as shown in the following formula: ,in, The objective function (i.e., the system-level optimization function) for the j-th scenario is expressed mathematically as follows:

[0036] Where N is the total number of representative operating points (i.e., the total number of discrete operating points). Let be the isentropic efficiency at the k-th representative operating point; This represents the vector of design variables to be optimized. This represents the weight of the k-th representative operating point. The specific assignment strategy can be determined during implementation based on the actual operational needs of the power plant, such as based on time weights. (Optimization targets are biased towards long-duration operating conditions); This represents the gas pressure value of the gas storage facility at the k-th working point in the j-th scenario; Let represent the system-level optimization function for the j-th scenario. This objective function guides the design direction from pursuing the performance peak in a certain isolated state to ensuring the turbine's overall performance is optimal across the entire operating condition.

[0037] It is understood that the embodiments of this application construct system-level optimization functions, thereby not pursuing the performance peak in a certain isolated state, but ensuring the optimal overall performance of the turbine in the entire operating condition.

[0038] In step S103, the first-level turbine reference parameters corresponding to the target compressed air energy storage system are obtained, and the collaborative mapping function of the turbine reference parameters of each level in the target compressed air energy storage system other than the first-level turbine reference parameters is determined as the system pressure of the target compressed air energy storage system changes dynamically, so as to construct a collaborative optimization variable set based on the first-level turbine reference parameters and the collaborative mapping function.

[0039] Furthermore, this application embodiment also needs to establish a set of collaborative optimization variables that includes a collaborative mapping function of the first-stage turbine reference parameters and subsequent level parameters as the inlet pressure changes.

[0040] Specifically, the embodiments of this application include the collaborative optimization variable set of the present invention. This can be expressed as: .

[0041] This set of variables includes not only the baseline design parameters of the first-stage turbine, such as pressure ratio The degree of reaction at the virtual design point and flow coefficient More importantly, the embodiments of this application introduce a series of cooperative mapping functions that describe how subsequent parameters at each level dynamically change with system pressure. , This series of functions provides the aerodynamic parameters (such as reaction degree) for subsequent stages and... The parameterized functional relationships between functions are determined through a global optimization algorithm. These parameterized functional relationships include linear functions, logarithmic functions, and other nonlinear functions, as well as combinations thereof.

[0042] It is understood that the embodiments of this application optimize the coefficients in the set of collaborative optimization variables by designing and optimizing the dynamic response strategies of each level when facing system pressure decay, thereby realizing the active coordination and matching of parameters between levels.

[0043] In step S104, based on the system-level optimization function, the set of collaborative optimization variables is iteratively solved to obtain the target turbine design parameters that meet the preset optimization requirements for the target compressed air energy storage system.

[0044] Subsequently, embodiments of this application may employ a global optimization algorithm to iteratively solve the set of collaborative optimization variables in order to obtain the turbine design parameters (i.e., the target turbine design parameters) that optimize the system-level optimization function value.

[0045] Therefore, the embodiments of this application can start from the design source to enable the turbine to maintain efficient operation throughout the entire energy release cycle, thereby having stronger engineering applicability.

[0046] Optionally, in one embodiment of this application, the collaborative optimization variable set is iteratively solved based on a system-level optimization function to obtain the target turbine design parameters that meet the preset optimization requirements for the target compressed air energy storage system. This includes: constructing global optimization models for different scenarios based on the system-level optimization function, the collaborative optimization variable set, and a preset step-by-step aerodynamic and thermodynamic calculation module; inputting each design variable vector to be optimized from the collaborative optimization variable set into the global optimization model for different scenarios, so as to calculate multiple state parameters corresponding to the target compressed air energy storage system step by step through the step-by-step aerodynamic and thermodynamic calculation module, and calculating the efficiency and power of the target compressed air energy storage system in each scenario based on the multiple state parameters; determining the comprehensive performance index of the global optimization model in different scenarios based on the efficiency and power, and iteratively solving the collaborative optimization variable set using the system-level optimization function to obtain the target design variable vector to be optimized when the comprehensive performance index reaches its maximum value, and using the design variable vector to be optimized as the target turbine design parameters.

[0047] In practical implementation, embodiments of this application can employ global optimization algorithms (such as constrained NSGA-II) suitable for multi-objective or multi-scenario problems for solution. During optimization, for each set of candidate design variables in the collaborative optimization variable set... Each scenario model needs to be substituted sequentially for performance evaluation. The evaluation of each scenario requires calling a high-fidelity stepwise aerodynamic thermodynamic calculation module to calculate the efficiency and power of the operating condition by progressively calculating state parameters such as outlet pressure and temperature. Specifically, for each operating point, this embodiment first calculates the actual outlet temperature and pressure of the first stage based on the given inlet total pressure, total temperature, and the first-stage pressure ratio and reaction degree among the optimization variables. This is achieved by solving the isentropic expansion process and combining it with the cascade loss model. Subsequent stages iteratively solve the results based on the previous stage's calculations, finally summing up the overall efficiency and power of the operating condition, and thus calculating the performance indicators for that scenario. The final summary yields a comprehensive index. .

[0048] The global optimization algorithm finds the optimal solution by iteratively calculating and determining whether the convergence condition is met in each iteration. Maximize the optimal set of variables (i.e., the vector of design variables to be optimized). The design variable vector to be optimized is used as the target turbine design parameter to obtain the corresponding design scheme. After the turbine is manufactured, it can cope with a variety of preset operating challenges without complex adjustments, and while ensuring high performance, it shows excellent robustness and wide applicability.

[0049] The following describes the execution process of the method for optimizing interstage parameters of a multi-stage compressed air energy storage turbine according to this application, with reference to the accompanying drawings.

[0050] Figure 2 is a schematic diagram of the execution logic of the multi-stage turbine parameter optimization method for compressed air energy storage according to this application. As shown in Figure 2, the execution process of the multi-stage turbine parameter optimization method for compressed air energy storage according to this application is as follows: S201: Establish the pressure drop input of the multi-scenario gas storage tank; S202: Establish the optimization objective of the multi-scenario system and initialize the optimization variable set; S203: Iteratively solve the problem using a global optimization algorithm (such as NSGA-ID); S204: Evaluate the turbine-side efficiency performance, and then proceed to S205 and S206; S205: Determine whether the convergence condition is met. If it is met, proceed to S207; otherwise, proceed to S206; S206: Update the optimization variable set; S207: Output the optimal variable set to control the turbine to operate efficiently under varying conditions.

[0051] In summary, the embodiments of this application first obtain multiple representative turbine inlet pressure dynamic decay curves as design inputs based on the characteristics of the gas storage facility; secondly, construct an optimization function with the weighted average efficiency of the entire energy release cycle as the objective; furthermore, establish a variable set including the collaborative mapping relationship of parameters at each level; and finally, obtain the optimal turbine design parameters through global optimization. Therefore, the embodiments of this application can overcome the shortcomings of traditional fixed-point design in the low efficiency of compressed air energy storage under varying operating conditions, and significantly improve the turbine's operating efficiency, adaptability, and system economy under varying operating conditions through dynamic collaborative design of inter-stage parameters.

[0052] According to the optimization method for interstage parameters of multi-stage turbines in compressed air energy storage proposed in this application, the following steps are taken: First, multiple dynamic decay curves of the gas storage tank pressure that meet preset representative requirements during the energy release process of the target compressed air energy storage system are obtained. These dynamic decay curves are then discretized into multiple representative operating points. The weighted average efficiency corresponding to these representative operating points under different scenarios is calculated, and the total number of representative operating points and the weight corresponding to each representative operating point are determined. Based on the weighted average efficiency, the total number of representative operating points, and the weights, a system-level optimization function is constructed. Second, the first-stage turbine reference parameters corresponding to the target compressed air energy storage system are obtained. Third, a collaborative mapping function is determined for the dynamic changes of the reference parameters of each stage of the turbine in the target compressed air energy storage system (excluding the first-stage turbine reference parameters) with the system pressure of the target compressed air energy storage system. Based on the first-stage turbine reference parameters and the collaborative mapping function, a collaborative optimization variable set is constructed. Finally, based on the system-level optimization function, the collaborative optimization variable set is iteratively solved to obtain the target turbine design parameters that meet the preset optimization requirements for the target compressed air energy storage system. This application significantly improves the turbine's operating efficiency, adaptability, and system economy under varying operating conditions through dynamic collaborative design of interstage parameters, enabling the turbine to maintain high-efficiency operation throughout the entire energy release cycle and possessing stronger engineering applicability.

[0053] Secondly, the device for optimizing interstage parameters of a multi-stage compressed air energy storage turbine according to an embodiment of this application is described with reference to the accompanying drawings.

[0054] Figure 3 is a block diagram of the compressed air energy storage multi-stage turbine interstage parameter optimization device according to an embodiment of this application.

[0055] As shown in Figure 3, the multi-stage parameter optimization device 10 for compressed air energy storage turbine includes: a discrete module 100, a first construction module 200, a second construction module 300, and a solution module 400.

[0056] The discrete module 100 is used to acquire multiple dynamic decay curves of the gas storage pressure that meet the preset representative requirements during the energy release process of the target compressed air energy storage system, and to discretize the multiple dynamic decay curves into multiple representative operating points.

[0057] The first construction module 200 is used to calculate the weighted average efficiency corresponding to multiple representative working points under different scenarios, and to determine the total number of representative working points and the weight corresponding to each representative working point, so as to construct a system-level optimization function based on the weighted average efficiency, the total number of representative working points and the weight.

[0058] The second construction module 300 is used to obtain the first-level turbine reference parameters corresponding to the target compressed air energy storage system, and to determine the collaborative mapping function of the turbine reference parameters of each level in the target compressed air energy storage system other than the first-level turbine reference parameters as the system pressure of the target compressed air energy storage system changes dynamically, so as to construct a collaborative optimization variable set based on the first-level turbine reference parameters and the collaborative mapping function.

[0059] The solver module 400 is used to iteratively solve the set of co-optimization variables based on the system-level optimization function to obtain the target turbine design parameters that meet the preset optimization requirements of the target compressed air energy storage system.

[0060] Optionally, in one embodiment of this application, the discrete module 100 includes: an acquisition unit and a determination unit.

[0061] The acquisition unit is used to acquire the gas storage tank volume, initial pressure, and energy release power requirements of the target compressed air energy storage system, and to establish multiple gas storage tank pressure decay functions based on the gas storage tank volume, initial pressure, and energy release power requirements.

[0062] The determination unit is used to determine the corresponding dynamic decay curve based on the pressure decay function of multiple gas storage tanks.

[0063] Optionally, in one embodiment of this application, the solver module 400 includes a modeling unit, a calculation unit, and an optimization unit.

[0064] The modeling unit is used to construct global optimization models for different scenarios based on system-level optimization functions, collaborative optimization variable sets, and preset hierarchical aero-thermodynamic calculation modules.

[0065] The calculation unit is used to input the vector of each design variable to be optimized in the collaborative optimization variable set into the global optimization model under different scenarios, so as to calculate the various state parameters corresponding to the target compressed air energy storage system step by step through the step-by-step aerodynamic thermodynamic calculation module, and calculate the efficiency and power of the target compressed air energy storage system under each scenario based on the various state parameters.

[0066] The optimization unit is used to determine the comprehensive performance index of the global optimization model under different scenarios based on efficiency and power, and to use the system-level optimization function to iteratively solve the set of collaborative optimization variables to obtain the target design variable vector to be optimized when the comprehensive performance index reaches its maximum value, and to use the design variable vector to be optimized as the target turbine design parameters.

[0067] Optionally, in one embodiment of this application, the mathematical expression of the system-level optimization function is:

[0068] Where N is the total number of representative operating conditions; Let be the isentropic efficiency at the k-th representative operating point; This represents the vector of design variables to be optimized. This represents the weight of the k-th representative working condition point; This represents the gas pressure value of the gas storage facility at the k-th working point in the j-th scenario; This represents the system-level optimization function for the j-th scenario.

[0069] It should be noted that the foregoing explanation of the method for optimizing interstage parameters of a multi-stage compressed air energy storage turbine also applies to the device for optimizing interstage parameters of a multi-stage compressed air energy storage turbine in this embodiment, and will not be repeated here.

[0070] The compressed air energy storage multi-stage turbine interstage parameter optimization device proposed in this application includes a discrete module 100, used to acquire multiple dynamic decay curves of the gas storage tank pressure that meet preset representative requirements during the energy release process of the target compressed air energy storage system, and to discretize the multiple dynamic decay curves into multiple representative operating points; and a first construction module 200, used to calculate the weighted average efficiency corresponding to multiple representative operating points under different scenarios, and to determine the total number of representative operating points and the weight corresponding to each representative operating point, so as to construct a system-level optimization based on the weighted average efficiency, the total number of representative operating points, and the weight. The system comprises two modules: a first module 300, which acquires the first-stage turbine reference parameters corresponding to the target compressed air energy storage system and determines the collaborative mapping function of the reference parameters of each stage of the turbine in the target compressed air energy storage system, excluding the first-stage turbine reference parameters, as the system pressure of the target compressed air energy storage system dynamically changes, so as to construct a collaborative optimization variable set based on the first-stage turbine reference parameters and the collaborative mapping function; and a second module 400, which iteratively solves the collaborative optimization variable set based on the system-level optimization function to obtain the target turbine design parameters corresponding to the target compressed air energy storage system that meet the preset optimization requirements. This application significantly improves the turbine's operating efficiency, adaptability, and system economy under varying operating conditions through dynamic collaborative design of inter-stage parameters, enabling the turbine to maintain high-efficiency operation throughout the entire energy release cycle and possessing stronger engineering applicability.

[0071] Figure 4 is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: a memory 401, a processor 402, and a computer program stored in the memory 401 and executable on the processor 402.

[0072] When the processor 402 executes the program, it implements the method for optimizing the interstage parameters of a multi-stage compressed air energy storage turbine provided in the above embodiments.

[0073] Furthermore, the electronic device also includes a communication interface 403 for communication between the memory 401 and the processor 402.

[0074] The memory 401 is used to store computer programs that can run on the processor 402.

[0075] Memory 401 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0076] If the memory 401, processor 402, and communication interface 403 are implemented independently, they can be interconnected via a bus to 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, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, only one thick line is used in Figure 4, but this does not indicate that there is only one bus or one type of bus.

[0077] 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.

[0078] 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.

[0079] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for optimizing interstage parameters of a multi-stage compressed air energy storage turbine.

[0080] This application also provides a computer program product, including a computer program, which, when executed, is used to implement the above-described method for optimizing interstage parameters of a multi-stage compressed air energy storage turbine.

[0081] 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.

[0082] 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.

[0083] 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 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.

[0084] 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 by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0085] 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.

[0086] Those skilled in the art will understand that all or part of the steps of the methods 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, the program includes one or a combination of the steps of the method embodiments.

[0087] 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.

[0088] 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 optimizing interstage parameters of a multi-stage compressed air energy storage turbine, characterized in that, Includes the following steps: During the energy release process of the target compressed air energy storage system, multiple dynamic decay curves of the gas storage tank pressure that meet the preset representative requirements are obtained, and the multiple dynamic decay curves are discretized into multiple representative operating points. Calculate the weighted average efficiency corresponding to the multiple representative working conditions under different scenarios, and determine the total number of representative working conditions and the weight corresponding to each representative working condition, so as to construct a system-level optimization function based on the weighted average efficiency, the total number of representative working conditions and the weight; Obtain the first-level turbine reference parameters corresponding to the target compressed air energy storage system, and determine the collaborative mapping function of the turbine reference parameters of each level in the target compressed air energy storage system other than the first-level turbine reference parameters as the system pressure of the target compressed air energy storage system dynamically changes. Construct a collaborative optimization variable set based on the first-level turbine reference parameters and the collaborative mapping function. Iteratively solve the collaborative optimization variable set based on the system-level optimization function to obtain the target turbine design parameters corresponding to the target compressed air energy storage system that meet the preset optimization requirements.

2. The method according to claim 1, characterized in that, The process of acquiring multiple dynamic decay curves of the gas storage tank pressure that meet preset representative requirements during the energy release process of the target compressed air energy storage system includes: acquiring the gas storage tank volume, initial pressure, and energy release power requirements of the target compressed air energy storage system; establishing multiple gas storage tank pressure decay functions based on the gas storage tank volume, the initial pressure, and the energy release power requirements; and determining the corresponding dynamic decay curves according to the multiple gas storage tank pressure decay functions.

3. The method according to claim 1, characterized in that, The step of iteratively solving the collaborative optimization variable set based on the system-level optimization function to obtain the target turbine design parameters of the target compressed air energy storage system that meet the preset optimization requirements includes: constructing a global optimization model under different scenarios based on the system-level optimization function, the collaborative optimization variable set, and a preset step-by-step aerodynamic thermodynamic calculation module; inputting each design variable vector to be optimized in the collaborative optimization variable set into the global optimization model under different scenarios, so as to calculate multiple state parameters corresponding to the target compressed air energy storage system step by step through the step-by-step aerodynamic thermodynamic calculation module, and calculating the efficiency and power of the target compressed air energy storage system under each scenario based on the multiple state parameters; determining the comprehensive performance index of the global optimization model under different scenarios based on the efficiency and the power, and iteratively solving the collaborative optimization variable set using the system-level optimization function to obtain the target design variable vector to be optimized when the comprehensive performance index reaches its maximum value, and using the design variable vector to be optimized as the target turbine design parameters.

4. The method according to claim 1, characterized in that, The mathematical expression for the system-level optimization function is: Where N is the total number of representative operating conditions; Let be the isentropic efficiency at the k-th representative operating point; This represents the vector of design variables to be optimized. This represents the weight of the k-th representative working condition point; This represents the gas pressure value of the gas storage facility at the k-th working point in the j-th scenario; This represents the system-level optimization function for the j-th scenario.

5. A device for optimizing interstage parameters of a multi-stage compressed air energy storage turbine, characterized in that, include: The discrete module is used to acquire multiple dynamic decay curves of the gas storage tank pressure that meet the preset representative requirements during the energy release process of the target compressed air energy storage system, and to discretize the multiple dynamic decay curves into multiple representative operating points. The first construction module is used to calculate the weighted average efficiency corresponding to the multiple representative working points under different scenarios, and determine the total number of representative working points and the weight corresponding to each representative working point, so as to construct a system-level optimization function based on the weighted average efficiency, the total number of representative working points and the weight. The second construction module is used to obtain the first-level turbine reference parameters corresponding to the target compressed air energy storage system, and determine the collaborative mapping function of the turbine reference parameters of each level in the target compressed air energy storage system other than the first-level turbine reference parameters as the system pressure of the target compressed air energy storage system changes dynamically, so as to construct a collaborative optimization variable set based on the first-level turbine reference parameters and the collaborative mapping function; the solution module is used to iteratively solve the collaborative optimization variable set based on the system-level optimization function to obtain the target turbine design parameters corresponding to the target compressed air energy storage system that meet the preset optimization requirements.

6. The apparatus according to claim 5, characterized in that, The discrete module includes: an acquisition unit, used to acquire the gas storage tank volume, initial pressure, and energy release power requirement of the target compressed air energy storage system, and establish multiple gas storage tank pressure decay functions based on the gas storage tank volume, the initial pressure, and the energy release power requirement; and a determination unit, used to determine the corresponding dynamic decay curve according to the multiple gas storage tank pressure decay functions.

7. The apparatus according to claim 5, characterized in that, The solution module includes: a modeling unit, used to construct global optimization models for different scenarios based on the system-level optimization function, the collaborative optimization variable set, and a preset step-by-step aero-thermodynamic calculation module; a calculation unit, used to input each design variable vector to be optimized from the collaborative optimization variable set into the global optimization model for different scenarios, so as to calculate multiple state parameters corresponding to the target compressed air energy storage system step by step through the step-by-step aero-thermodynamic calculation module, and calculate the efficiency and power of the target compressed air energy storage system in each scenario based on the multiple state parameters; and an optimization unit, used to determine the comprehensive performance index of the global optimization model in different scenarios based on the efficiency and the power, and to iteratively solve the collaborative optimization variable set using the system-level optimization function to obtain the target design variable vector to be optimized when the comprehensive performance index reaches its maximum value, and to use the design variable vector to be optimized as the target turbine design parameter.

8. An electronic device, characterized in that, include: The memory, the processor, and the computer program stored in the memory and executable on the processor, the processor executing the program to implement the method for optimizing interstage parameters of a multi-stage compressed air energy storage turbine as described in any one of claims 1-4.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the method for optimizing interstage parameters of a multi-stage compressed air energy storage turbine as described in any one of claims 1-4.

10. A computer program product, comprising a computer program, characterized in that, The computer program is executed to implement the method for optimizing interstage parameters of a multi-stage compressed air energy storage turbine as described in any one of claims 1-4.

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