Compressed air energy storage power station assembly layout optimization method

Abstract

The invention relates to the technical field of novel energy, in particular to a compressed air energy storage power station component layout optimization method, which comprises the following steps: acquiring historical load data of a compressed air energy storage power station; predicting the long-time load of the compressed air energy storage power station according to the historical load data; the target energy storage capacity of the compressed air energy storage power station is predicted according to the long-time load, and at least one assembly target parameter of the compressed air energy storage power station is determined according to the target energy storage capacity; and establishing a layout optimization model of the compressed air energy storage power station according to the at least one component target parameter of the compressed air energy storage power station and the compressed air energy storage power station model, and optimizing the component layout of the compressed air energy storage power station based on the layout optimization model. Therefore, the problems of power grid acceptance challenge, high compressed air energy storage cost and the like caused by intermittency of wind energy and solar energy in the prior art are solved.

Description

Technical Field

[0001] The present application relates to the field of new energy technologies, and in particular to a method for optimizing the layout of compressed air energy storage power station components. Background Art

[0002] With the large-scale application of wind and solar energy, their intermittency and instability challenge the grid's ability to accept energy, requiring large-scale, long-cycle, and high-efficiency energy storage technology to maintain a balance between electricity supply and demand.

[0003] Compressed air energy storage technology came into being and attracted global attention as a solution and has been gradually incorporated into energy development plans. However, its cost is mainly concentrated in the construction of components such as compressors, turbines, and gas storage facilities, and the layout needs to be optimized to reduce costs. Summary of the invention

[0004] The present application provides a method for optimizing the layout of compressed air energy storage power station components to solve the problems of grid acceptance caused by the intermittent nature of wind energy and solar energy and the high cost of compressed air energy storage in the prior art.

[0005] A first aspect of the present application provides a method for optimizing the layout of components of a compressed air energy storage power station, comprising the following steps: acquiring historical load data of a compressed air energy storage power station; predicting the long-term load of the compressed air energy storage power station based on the historical load data; predicting the target energy storage capacity of the compressed air energy storage power station based on the long-term load, and determining at least one component target parameter of the compressed air energy storage power station based on the target energy storage capacity; establishing a layout optimization model of the compressed air energy storage power station based on at least one component target parameter of the compressed air energy storage power station and a compressed air energy storage power station model, and optimizing the component layout of the compressed air energy storage power station based on the layout optimization model.

[0006] Optionally, optimizing the component layout of the compressed air energy storage power station based on the layout optimization model includes: introducing slack variables into the layout optimization model to obtain the objective function of the layout optimization model; solving the objective function of the layout optimization model based on the interior point method to obtain a layout optimization result; and optimizing the component layout of the compressed air energy storage power station according to the layout optimization result.

[0007] Optionally, the objective function of the layout optimization model is:

[0008]

[0009] Among them, C air is the total cost of the gas pipeline network, C heat is the total cost of the heat pipe network, is the power of the ith compressor, is the power of the i-th turbine, is the cost per unit compressor power, is the cost per unit turbine power, is a slack variable that represents the length of the gas pipeline network between the gas storage and the compressor. is a relaxation variable representing the length of the gas pipeline network between the gas storage and the turbine. is the relaxation variable that represents the length of the heat pipe network between the heat storage and the compressor. It is a relaxation variable that characterizes the length of the heat transfer pipeline network between the heat storage and the turbine.

[0010] Optionally, the layout optimization model is:

[0011]

[0012]

[0013] Among them, (x1, y1) is the location of the heat storage reservoir, d is the minimum allowable distance between the heat storage reservoir and the gas storage reservoir, P C is the required value of the compressor, P t is the required value of the turbine, is the position of the compressor, c air is the unit cost of the gas pipeline network, c heat is the unit cost of the heat pipe network, The location of the turbine.

[0014] Optionally, the compressed air energy storage power station model includes at least one component model of a compressor, a turbine, a gas storage reservoir, a heat storage reservoir and a gas transmission pipeline network.

[0015] Optionally, the at least one component target parameter includes at least one of compressor power, turbine power, heat storage tank capacity and gas storage tank capacity.

[0016] A second aspect of the present application provides a device for optimizing the layout of components of a compressed air energy storage power station, including: an acquisition module for acquiring historical load data of a compressed air energy storage power station; a prediction module for predicting the long-term load of the compressed air energy storage power station based on the historical load data; a determination module for predicting the target energy storage capacity of the compressed air energy storage power station based on the long-term load, and determining at least one component target parameter of the compressed air energy storage power station based on the target energy storage capacity; an optimization module for establishing a layout optimization model of the compressed air energy storage power station based on at least one component target parameter of the compressed air energy storage power station and a compressed air energy storage power station model, and optimizing the component layout of the compressed air energy storage power station based on the layout optimization model.

[0017] The third aspect of the present application 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 method for optimizing the layout of compressed air energy storage power station components as described in the above embodiment.

[0018] The fourth aspect of the present application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the method for optimizing the layout of compressed air energy storage power station components as described in the above embodiment.

[0019] The fifth aspect of the present application provides a computer program product, including a computer program or instructions, which, when executed, are used to implement the method for optimizing the layout of compressed air energy storage power station components as in the above-mentioned embodiment.

[0020] Therefore, this application includes the following beneficial effects:

[0021] The embodiment of the present application determines the scale of the compressed air energy storage power station based on data such as long-term load forecasts; then establishes a compressed air energy storage power station model including compressors, turbines, gas storage reservoirs, heat storage reservoirs, gas transmission pipelines, etc., considers capacity constraints, uses total cost as the objective function, and finally forms a second-order cone optimization model for layout optimization; finally, the interior point method is used to solve the problem, and the layout optimization results of each component of the compressed air energy storage power station are finally obtained. As a result, technical problems such as the challenges of grid acceptance caused by the intermittent nature of wind and solar energy and the high cost of compressed air energy storage in the prior art are solved.

[0022] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0024] Figure 1 A flowchart of a method for optimizing the layout of compressed air energy storage power station components provided according to an embodiment of the present application;

[0025] Figure 2 A flow chart of a method for optimizing the layout of compressed air energy storage power station components according to one embodiment of the present application;

[0026] Figure 3 This is an example diagram of a device for optimizing the layout of components of a compressed air energy storage power station according to an embodiment of the present application;

[0027] Figure 4 is a schematic diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0028] The embodiments of the present application are described in detail below, and examples of the embodiments 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 application, and should not be construed as limiting the present application.

[0029] The following describes a method for optimizing the layout of components of a compressed air energy storage power station in accordance with an embodiment of the present application with reference to the accompanying drawings. In response to the problem of the construction cost of compressors, turbines, gas storage reservoirs, etc. mentioned in the above background technology, the present application provides a method for optimizing the layout of components of a compressed air energy storage power station. In this method, the scale of the compressed air energy storage power station is determined based on data such as long-term load forecasts; then a compressed air energy storage power station model including compressors, turbines, gas storage reservoirs, heat storage reservoirs, gas transmission pipelines, etc. is established, and the capacity constraints are considered. The total cost is used as the objective function, and finally a second-order cone optimization model for layout optimization is constructed; finally, the interior point method is used to solve the problem, and the layout optimization results of each component of the compressed air energy storage power station are finally obtained. As a result, the problems of grid acceptance challenges caused by the intermittent nature of wind and solar energy and the high cost of compressed air energy storage in the prior art are solved.

[0030] Specifically, Figure 1 A schematic flow chart of a method for optimizing the layout of compressed air energy storage power station components provided in an embodiment of the present application.

[0031] like Figure 1 As shown, the compressed air energy storage power station component layout optimization method includes the following steps:

[0032] In step S101, historical load data of a compressed air energy storage power station is obtained.

[0033] Among them, the historical load data can be a data record of the actual amount of electricity carried or released by the compressed air energy storage power station and its changes in the past period of time.

[0034] It can be understood that the embodiments of the present application can formulate a more scientific operation plan for the power station by accurately grasping the historical load data, thereby promoting the further development of compressed air energy storage technology.

[0035] In step S102, the long-term load of the compressed air energy storage power station is predicted based on historical load data.

[0036] Among them, long-term load may refer to the power load demand with a relatively long time span involved in the forecast.

[0037] It can be understood that by predicting long-term loads, the power station can plan energy storage and release strategies in advance to ensure the balance of supply and demand in the power grid, which can not only avoid waste of resources caused by excessive energy storage capacity, but also prevent power supply shortages caused by insufficient energy storage capacity.

[0038] In step S103, the target energy storage capacity of the compressed air energy storage power station is predicted according to the long-term load, and the target parameter of at least one component of the compressed air energy storage power station is determined according to the target energy storage capacity.

[0039] Among them, the target energy storage capacity can be the maximum energy storage capacity that the energy storage power station should have, determined based on the long-term load forecast results, combined with multiple factors such as the operating needs, economic costs, and technical conditions of the energy storage power station. At least one component target parameter may include at least one of the compressor power, turbine power, heat storage tank capacity, and gas storage tank capacity.

[0040] It is understandable that the embodiment of the present application determines the target energy storage capacity by predicting the long-term load of the compressed air energy storage power station, and sets the target parameters of at least one key component in the power station accordingly, which can optimize the performance of the energy storage power station, improve energy utilization efficiency, and ensure stable and reliable operation of the system. Through accurate prediction and reasonable configuration, it provides effective support for the development and application of compressed air energy storage technology.

[0041] In step S104, a layout optimization model of the compressed air energy storage power station is established according to at least one component target parameter of the compressed air energy storage power station and the compressed air energy storage power station model, and the component layout of the compressed air energy storage power station is optimized based on the layout optimization model.

[0042] Among them, the layout optimization model can be how the various components in the power station are laid out to maximize energy storage efficiency, reduce operating costs or meet other performance indicators. The compressed air energy storage power station model can include at least one component model of the compressor, turbine, gas storage, heat storage and gas pipeline network.

[0043] It is understandable that the embodiment of the present application constructs a layout optimization model by integrating the component target parameters of the compressed air energy storage power station with the energy storage power station model. The overall performance, operating efficiency and economic benefits of the energy storage power station are improved by optimizing the layout of the components in the power station. Based on the layout optimization model, the best component layout solution can be found to achieve higher energy storage density, lower energy loss, and stronger system reliability and safety.

[0044] In an embodiment of the present application, the component layout of the compressed air energy storage power station is optimized based on the layout optimization model, including: introducing slack variables in the layout optimization model to obtain the objective function of the layout optimization model; solving the objective function of the layout optimization model based on the interior point method to obtain the layout optimization result; and optimizing the component layout of the compressed air energy storage power station according to the layout optimization result.

[0045] Among them, the interior point method can be an iterative algorithm for solving constrained optimization problems, and the component layout can be the placement and connection method of key components such as compressors, expanders, and gas tanks.

[0046] It is understandable that in the layout optimization process of the compressed air energy storage power station, the embodiment of the present application introduces slack variables to construct the objective function of the layout optimization model, and uses the interior point method to solve the objective function, thereby obtaining the optimized component layout result. By adjusting the layout of the components in the power station, the energy storage efficiency is improved, the operating cost is reduced, and the performance optimization is ensured.

[0047] In the embodiment of the present application, the objective function of the layout optimization model is:

[0048]

[0049] Among them, C air is the total cost of the gas pipeline network, C heat is the total cost of the heat pipe network, is the power of the ith compressor, is the power of the i-th turbine, is the cost per unit compressor power, is the cost per unit turbine power, is a slack variable that represents the length of the gas pipeline network between the gas storage and the compressor. is a relaxation variable representing the length of the gas pipeline network between the gas storage and the turbine. is the relaxation variable that represents the length of the heat pipe network between the heat storage and the compressor. It is a relaxation variable that characterizes the length of the heat transfer pipeline network between the heat storage and the turbine.

[0050] In the embodiment of the present application, the layout optimization model is:

[0051]

[0052] Among them, (x1, y1) is the location of the heat storage reservoir, d is the minimum allowable distance between the heat storage reservoir and the gas storage reservoir, P V is the required value of the compressor, P t is the required value of the turbine, is the position of the compressor, c air is the unit cost of the gas pipeline network, c heatis the unit cost of the heat pipe network, The location of the turbine.

[0053] According to the method for optimizing the layout of compressed air energy storage power station components proposed in the embodiment of the present application, the scale of the compressed air energy storage power station is determined based on data such as long-term load forecasts; then a compressed air energy storage power station model including compressors, turbines, gas storage reservoirs, heat storage reservoirs, gas transmission pipelines, etc. is established, and the capacity constraints are considered. The total cost is used as the objective function, and finally a second-order cone optimization model for layout optimization is constructed; finally, the interior point method is used to solve the problem, and the layout optimization results of each component of the compressed air energy storage power station are finally obtained. In this way, the problems of grid acceptance challenges caused by the intermittent nature of wind and solar energy and the high cost of compressed air energy storage in the prior art are solved.

[0054] The following will be combined Figure 2 The optimization method of the layout of compressed air energy storage power station components is specifically described as follows:

[0055] (1) Carry out capacity planning of compressed air energy storage power station.

[0056] The scale of the compressed air energy storage power station is determined based on long-term load forecast and other data. First, long-term load forecasts are conducted based on historical load data, and the energy storage capacity requirements are determined in combination with the expected development of thermal power units, new energy units and other power capacity and related requirements. Furthermore, the power of the compressor and turbine, and the capacity of the heat storage tank and gas storage tank are determined based on the capacity of the compressed air energy storage power station.

[0057] Long-term load forecasting can be estimated using the gross domestic product (GDP) comprehensive electricity consumption method. First, the energy required per unit GDP in the next five or ten years is estimated based on the historical energy required per unit GDP. The GDP value in the next five or ten years is estimated based on the historical GDP development law and local development potential. Finally, a long-term forecast of the load in the next five or ten years is made.

[0058] Among them, the predicted value of future load is:

[0059] P f =GDP f ×p f

[0060] Among them, GDP f is the predicted value of future GDP, p f is the predicted value of energy required per unit GDP, P f is the predicted value of future load.

[0061] Similarly, the potential of renewable energy output in a region can be evaluated based on the region’s climate conditions. Then, based on the load forecast results, renewable energy output results, and relevant regulations, the required capacity of energy storage can be determined. Finally, based on the capacity of the compressed air energy storage power station, the power of the compressor and turbine, and the capacity of the gas and heat storage reservoirs can be predicted.

[0062] (2) Establish a compressed air energy storage power station layout optimization model.

[0063] A compressed air energy storage power station model including compressors, turbines, gas storage, heat storage, gas transmission network, etc. is established. Considering the capacity constraint, the total cost is used as the objective function, and finally a second-order cone optimization model for layout optimization is constructed. The exhaust gas of the compressor can be transferred to the next-stage compressor or to the gas storage, which is connected by a gas transmission network; similarly, the gas in the gas storage is supplied to the turbine, and the exhaust gas of the turbine can continue to be reduced in the next-stage turbine, or it can be directly supplied to the synchronous generator for power generation, which is connected by a gas transmission network. Similar to the heat storage, the heat generated by the compressor is transmitted to the heat storage tank through the heat transmission network, and the heat required by the turbine is also supplied by the heat storage through the heat transmission network. The objective function is the cost of the system, which is composed of the cost of the compressor, the cost of the turbine, the gas transmission network, and the heat transmission network.

[0064] Because the location of the gas storage is determined by salt caverns, mine caves, etc., the location is relatively limited, and its location can be considered fixed. Here it is set to (0,0), then the compressed air energy storage power station layout optimization model can be written as follows:

[0065]

[0066] Among them, (x1, y1) is the location of the heat storage reservoir, d is the minimum allowable distance between the heat storage reservoir and the gas storage reservoir, P C is the required value of the compressor, P t is the required value of the turbine, is the position of the compressor, c air is the unit cost of the gas pipeline network, c heat is the unit cost of the heat pipe network, The location of the turbine.

[0067] (3) Use the interior point method to solve the model.

[0068] The interior point method is used to solve the problem, and finally the layout optimization results of each component of the compressed air energy storage power station are obtained. After relaxation, the model can be transformed into a second-order cone optimization problem, which can be solved very efficiently using the interior point method. By solving this model, the optimized layout of the compressed air energy storage power station can be obtained.

[0069] Introducing slack variables And make it satisfy the following constraints:

[0070]

[0071] The objective function can be rewritten as:

[0072]

[0073] Since the optimization direction is the minimum, this relaxation is tight and the model is expressed as a second-order cone optimization problem that can be quickly solved by the interior point method.

[0074] In summary, the embodiment of the present application determines the scale of the energy storage power station based on data such as long-term load forecasts and power supply capacity requirements, including the power of compressors and turbines and the capacity of heat storage and gas storage tanks. Next, a power station model containing key components such as compressors, turbines, gas storage reservoirs, heat storage reservoirs and gas transmission pipelines was established, and a second-order cone optimization model with total cost as the objective function was set, taking into account the capacity constraints of each component and the connection relationship between them. Finally, by applying the interior point method to solve the model, the optimized layout results of the components of the compressed air energy storage power station were obtained, providing a scientific basis for achieving efficient and economical power station design.

[0075] Next, a device for optimizing the layout of components of a compressed air energy storage power station proposed according to an embodiment of the present application will be described with reference to the accompanying drawings.

[0076] Figure 3 It is a block diagram of a device for optimizing the layout of components of a compressed air energy storage power station according to an embodiment of the present application.

[0077] like Figure 3 As shown, the compressed air energy storage power station component layout optimization device 10 includes: an acquisition module 100, a prediction module 200, a determination module 300 and an optimization module 400.

[0078] Among them, the acquisition module 100 is used to obtain the historical load data of the compressed air energy storage power station; the prediction module 200 is used to predict the long-term load of the compressed air energy storage power station based on the historical load data; the determination module 300 is used to predict the target energy storage capacity of the compressed air energy storage power station based on the long-term load, and determine at least one component target parameter of the compressed air energy storage power station according to the target energy storage capacity; the optimization module 400 is used to establish a layout optimization model of the compressed air energy storage power station according to at least one component target parameter of the compressed air energy storage power station and the compressed air energy storage power station model, and optimize the component layout of the compressed air energy storage power station based on the layout optimization model.

[0079] It should be noted that the aforementioned explanation of the embodiment of the method for optimizing the layout of components of a compressed air energy storage power station is also applicable to the device for optimizing the layout of components of a compressed air energy storage power station of this embodiment, and will not be repeated here.

[0080] According to the compressed air energy storage power station component layout optimization device proposed in the embodiment of the present application, the scale of the compressed air energy storage power station is determined according to data such as long-term load forecasting; then a compressed air energy storage power station model including compressors, turbines, gas storage reservoirs, heat storage reservoirs, gas transmission pipelines, etc. is established, and the capacity constraint is considered. The total cost is used as the objective function to form a second-order cone optimization model for layout optimization; finally, the interior point method is used to solve the problem, and the layout optimization results of each component of the compressed air energy storage power station are finally obtained. In this way, the problems of grid acceptance challenges caused by the intermittent nature of wind and solar energy and the high cost of compressed air energy storage in the prior art are solved.

[0081] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0082] Memory 401 , processor 402 , and a computer program stored in the memory 401 and executable on the processor 402 .

[0083] When the processor 402 executes the program, the compressed air energy storage power station component layout optimization method provided in the above embodiment is implemented.

[0084] Furthermore, the electronic device further comprises:

[0085] The communication interface 403 is used for communication between the memory 401 and the processor 402 .

[0086] The memory 401 is used to store computer programs that can be executed on the processor 402 .

[0087] The memory 401 may include a high-speed RAM (Random Access Memory) memory, and may also include a non-volatile memory, such as at least one disk memory.

[0088] If the memory 401, the processor 402 and the communication interface 403 are implemented independently, the communication interface 403, the memory 401 and the processor 402 can be connected to each other through a bus and communicate with each other. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 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.

[0089] Optionally, in a specific implementation, if the memory 401, the processor 402 and the communication interface 403 are integrated on a chip, the memory 401, the processor 402 and the communication interface 403 can communicate with each other through an internal interface.

[0090] The processor 402 may be a CPU (Central Processing Unit), or an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present application.

[0091] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for optimizing the layout of components of a compressed air energy storage power station.

[0092] The embodiments of the present application also provide a computer program product, including a computer program or instructions, which, when executed, implement the above-mentioned method for optimizing the layout of components of a compressed air energy storage power station.

[0093] 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 application. In this specification, the schematic representations of the above terms are not necessarily directed 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.

[0094] 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 technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0095] 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 application 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 application belong.

[0096] It should be understood that the various parts of the present application can be implemented in hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, the steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array, a field programmable gate array, etc.

[0097] A person of ordinary skill in the art may understand that all or part of the steps carried by the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the above-mentioned program may be stored in a computer-readable storage medium, which, when executed, includes one of the steps of the method embodiment or a combination thereof.

[0098] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for optimizing the layout of compressed air energy storage power station components, characterized in that: The following steps are involved: Obtain historical load data of compressed air energy storage power stations; Predicting the long-term load of the compressed air energy storage power station according to the historical load data; Predicting a target energy storage capacity of the compressed air energy storage power station according to the long-term load, and determining a target parameter of at least one component of the compressed air energy storage power station according to the target energy storage capacity; A layout optimization model of the compressed air energy storage power station is established according to at least one component target parameter of the compressed air energy storage power station and a compressed air energy storage power station model, and the component layout of the compressed air energy storage power station is optimized based on the layout optimization model.

2. The method for optimizing the layout of compressed air energy storage power station components according to claim 1, characterized in that: Optimizing the component layout of the compressed air energy storage power station based on the layout optimization model includes: Introducing slack variables into the layout optimization model to obtain an objective function of the layout optimization model; Solving the objective function of the layout optimization model based on the interior point method to obtain a layout optimization result; The component layout of the compressed air energy storage power station is optimized according to the layout optimization result.

3. The method for optimizing the layout of compressed air energy storage power station components according to claim 2, characterized in that: The objective function of the layout optimization model is: Among them, C air is the total cost of the gas transmission network, C heat is the total cost of the heat pipe network, is the power of the ith compressor, is the power of the i-th turbine, is the cost per unit compressor power, is the cost per unit turbine power, is a slack variable that represents the length of the gas pipeline network between the gas storage and the compressor. is a relaxation variable representing the length of the gas pipeline network between the gas storage and the turbine. is the relaxation variable that represents the length of the heat pipe network between the heat storage and the compressor. It is a relaxation variable that characterizes the length of the heat transfer pipeline network between the heat storage and the turbine.

4. The method for optimizing the layout of compressed air energy storage power station components according to claim 2, characterized in that: The layout optimization model is: Among them, (x1, y1) is the location of the heat storage reservoir, d is the minimum allowable distance between the heat storage reservoir and the gas storage reservoir, P C is the required value of the compressor, P t is the required value of the turbine, is the position of the compressor, c air is the unit cost of the gas pipeline network, c heat is the unit cost of the heat pipe network, The location of the turbine.

5. The method for optimizing the layout of compressed air energy storage power station components according to claim 1, characterized in that: The compressed air energy storage power station model includes at least one component model of a compressor, a turbine, a gas storage reservoir, a heat storage reservoir and a gas transmission pipeline network.

6. The method for optimizing the layout of compressed air energy storage power station components according to claim 1, characterized in that: The at least one component target parameter includes at least one of compressor power, turbine power, heat storage tank capacity, and gas storage tank capacity.

7. A device for optimizing the layout of compressed air energy storage power station components, characterized in that: include: An acquisition module, used to obtain historical load data of a compressed air energy storage power station; A prediction module, used to predict the long-term load of the compressed air energy storage power station according to the historical load data; A determination module, configured to predict a target energy storage capacity of the compressed air energy storage power station according to the long-term load, and determine a target parameter of at least one component of the compressed air energy storage power station according to the target energy storage capacity; An optimization module is used to establish a layout optimization model of a compressed air energy storage power station according to at least one component target parameter of the compressed air energy storage power station and a compressed air energy storage power station model, and optimize the component layout of the compressed air energy storage power station based on the layout optimization model.

8. 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 method for optimizing the layout of components of a compressed air energy storage power station as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed, the method for optimizing the layout of components of a compressed air energy storage power station as described in any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed, the method for optimizing the layout of components of a compressed air energy storage power station as described in any one of claims 1 to 6 is implemented.

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