A compressed air energy storage power plant component layout optimization method

Abstract

The application relates to the field of new energy technology, in particular to a compressed air energy storage power station component layout optimization method, wherein the method comprises the following steps: obtaining historical load data of a compressed air energy storage power station; predicting long-time 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-time load, and determining at least one component target parameter of the compressed air energy storage power station according to the target energy storage capacity; 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 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. Therefore, the problems of intermittent wind energy and solar energy leading to power grid accommodation challenges and high compressed air energy storage cost in the prior art are solved.

Description

Technical Field

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

[0002] With the large-scale application of wind and solar energy, their intermittency and instability challenge the grid's capacity to accommodate them, requiring large-scale, long-cycle, and high-efficiency energy storage technologies to maintain the balance between power supply and demand.

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

[0004] This application provides a method for optimizing the layout of compressed air energy storage power station components to address the challenges of grid acceptance caused by the intermittency of wind and solar energy and the high cost of compressed air energy storage in the prior art.

[0005] The first aspect of this application provides a method for optimizing the layout of components in a compressed air energy storage power station, comprising the following steps: acquiring historical load data of the 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; determining at least one 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 the at least one 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 the 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 C represents the total cost of the gas pipeline network. heat The total cost of the heat transmission pipeline network, Let be the power of the i-th compressor. Let be the power of the i-th turbine. Cost per unit compressor power Cost per unit turbine power To characterize the slack variable of the length of the gas pipeline between the gas storage facility and the compressor, To characterize the slack variable of the length of the gas pipeline between the gas storage facility and the turbine, To characterize the slack variable of the length of the heat transfer pipeline between the thermal storage tank and the compressor, This is a slack variable characterizing the length of the heat transfer pipeline between the thermal storage facility and the turbine.

[0010] Optionally, the layout optimization model is:

[0011]

[0012]

[0013] Where (x1, y1) represents the location of the thermal storage facility, d is the minimum allowable distance between the thermal storage facility and the gas storage facility, and P C P is the required value for the compressor. t The required value for the turbine. For the location of the compressor, c air c represents the unit cost of the gas pipeline network. heat The unit cost of the heat transmission pipeline network, This indicates 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, an air storage tank, a thermal storage tank, and an air transmission pipeline network.

[0015] Optionally, the target parameters of the at least one component include at least one of the following: compressor power, turbine power, thermal storage tank capacity, and gas storage tank capacity.

[0016] A second aspect of this application provides a compressed air energy storage power station component layout optimization device, comprising: an acquisition module for acquiring historical load data of the 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; and an optimization module for establishing a layout optimization model of the compressed air energy storage power station based on the at least one component target parameter 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] 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 compressed air energy storage power station component layout optimization method as described in the above embodiments.

[0018] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the compressed air energy storage power station component layout optimization method as described in the above embodiments.

[0019] The fifth aspect of this application provides a computer program product, including a computer program or instructions, which, when executed, are used to implement the compressed air energy storage power station component layout optimization method as described in the above embodiments.

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

[0021] This application's embodiments determine the scale of a compressed air energy storage power station based on long-term load forecasting and other data. Then, a compressed air energy storage power station model is established, including compressors, turbines, gas storage tanks, thermal storage tanks, and gas transmission pipelines. Considering capacity constraints, the total cost is used as the objective function, and a second-order cone optimization model for layout optimization is finally constructed. Finally, the interior-point method is used to solve the problem, ultimately obtaining the optimized layout results for each component of the compressed air energy storage power station. This solves the technical problems in existing technologies, such as the challenges to grid acceptance due to the intermittency of wind and solar energy, and the high cost of compressed air energy storage.

[0022] 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

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

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

[0025] Figure 2 This is a flowchart of a method for optimizing the layout of compressed air energy storage power station components according to an embodiment of this application;

[0026] Figure 3 This is an example diagram of a compressed air energy storage power station component layout optimization device according to an embodiment of this application;

[0027] Figure 4 This is a schematic diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0028] The embodiments of this application are described in detail below. Examples of the 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.

[0029] The following describes a method for optimizing the layout of components in a compressed air energy storage power station according to an embodiment of this application, with reference to the accompanying drawings. Addressing the construction cost issues of compressors, turbines, and gas storage facilities mentioned in the background art, this application provides a method for optimizing the layout of components in a compressed air energy storage power station. In this method, the scale of the compressed air energy storage power station is determined based on long-term load forecast data; then, a model of the compressed air energy storage power station, including compressors, turbines, gas storage facilities, thermal storage facilities, and gas transmission pipelines, is established. Considering capacity constraints, 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, ultimately obtaining the optimized layout results of each component of the compressed air energy storage power station. This solves the problems of grid acceptance challenges caused by the intermittency of wind and solar energy and the high cost of compressed air energy storage in the prior art.

[0030] Specifically, Figure 1 This is a flowchart illustrating a method for optimizing the layout of compressed air energy storage power station components, as provided in an embodiment of this application.

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

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

[0033] Historical load data can be a record of the actual electrical power carried or released by the compressed air energy storage power station and its changes over a past period.

[0034] It is understood that by accurately grasping historical load data, the embodiments of this application can more scientifically formulate power plant operation plans, 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-duration load can refer to the electricity load demand that spans a relatively long period of time in the forecast.

[0037] It is understood that by predicting long-term loads, the power plant can plan energy storage and release strategies in advance to ensure the balance of power grid supply and demand. This not only avoids resource waste caused by excessive energy storage capacity, but also prevents 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 based on the long-term load, and at least one component target parameter of the compressed air energy storage power station is determined based on the target energy storage capacity.

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

[0040] It is understood that the embodiments of this application determine the target energy storage capacity of the compressed air energy storage power station by predicting its long-term load, and set the target parameters of at least one key component in the power station accordingly. This optimizes the performance of the energy storage power station, improves energy utilization efficiency, and ensures stable and reliable system operation. Through accurate prediction and reasonable configuration, effective support is provided for the development and application of compressed air energy storage technology.

[0041] In step S104, a layout optimization model for the compressed air energy storage power station is established based on the target parameters of at least one component of the compressed air energy storage power station and the compressed air energy storage power station model. The component layout of the compressed air energy storage power station is then 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 arranged 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 compressor, turbine, gas storage, thermal storage and gas transmission network.

[0043] It is understood that this application embodiment constructs a layout optimization model by integrating the target parameters of the compressed air energy storage power station components with the energy storage power station model. By optimizing the layout of components within the power station, the overall performance, operating efficiency, and economic benefits of the energy storage power station are improved. Based on this layout optimization model, the optimal component layout scheme can be found to achieve higher energy storage density, lower energy loss, and stronger system reliability and security.

[0044] In this embodiment of the application, optimizing the component layout of a compressed air energy storage power station based on a 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 the layout optimization result; and optimizing the component layout of the compressed air energy storage power station based on 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 understood that, in the layout optimization process of compressed air energy storage power stations, this application embodiment 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 results. By adjusting the layout of components within the power station, energy storage efficiency is improved, operating costs are reduced, and performance optimization is ensured.

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

[0048]

[0049] Among them, C air C represents the total cost of the gas pipeline network. heat The total cost of the heat transmission pipeline network, Let be the power of the i-th compressor. Let be the power of the i-th turbine. Cost per unit compressor power Cost per unit turbine power To characterize the slack variable of the length of the gas pipeline between the gas storage facility and the compressor, To characterize the slack variable of the length of the gas pipeline between the gas storage facility and the turbine, To characterize the slack variable of the length of the heat transfer pipeline between the thermal storage tank and the compressor, This is a slack variable characterizing the length of the heat transfer pipeline between the thermal storage facility and the turbine.

[0050] In this embodiment of the application, the layout optimization model is as follows:

[0051]

[0052] Where (x1, y1) represents the location of the thermal storage facility, d is the minimum allowable distance between the thermal storage facility and the gas storage facility, and P V P is the required value for the compressor. t The required value for the turbine. For the location of the compressor, c air c represents the unit cost of the gas pipeline network. heatThe unit cost of the heat transmission pipeline network, This indicates the location of the turbine.

[0053] According to the compressed air energy storage power station component layout optimization method proposed in this application, the scale of the compressed air energy storage power station is determined based on long-term load forecasting and other data. Then, a compressed air energy storage power station model including compressors, turbines, gas storage tanks, thermal storage tanks, and gas transmission pipelines is established. Considering capacity constraints, 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 final layout optimization results of each component of the compressed air energy storage power station are obtained. This solves the problems of grid acceptance challenges caused by the intermittency of wind and solar energy and the high cost of compressed air energy storage in existing technologies.

[0054] The following will combine Figure 2 The optimization method for the layout of compressed air energy storage power station components is described in detail below:

[0055] (1) Conduct capacity planning for compressed air energy storage power stations.

[0056] The scale of the compressed air energy storage power station is determined based on long-term load forecasting data. First, long-term load forecasting is conducted based on historical load data, and the energy storage capacity requirement is determined by considering the anticipated development of thermal power units, new energy units, and other power sources, as well as related requirements. Further, the power of the compressor and turbine, and the capacity of the thermal storage tank and air 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 comprehensive energy consumption method based on Gross Domestic Product (GDP). First, the energy required per unit of GDP in the next five or ten years is estimated based on the historical energy required per unit of GDP. Then, the GDP value in the next five or ten years is estimated based on historical GDP development patterns and local development potential. Finally, a long-term load forecast is made for the next five or ten years.

[0058] Among them, the projected future load values ​​are:

[0059] P f =GDP f ×p f

[0060] Among them, GDP f p is a projected value for future GDP. f P is the projected energy required per unit of GDP. f This represents the projected future load.

[0061] Similarly, the potential for renewable energy output in a region can be assessed based on its climate conditions. Then, based on load forecasts, renewable energy output figures, and relevant regulations, the required energy storage capacity is determined. Finally, based on the capacity of the compressed air energy storage power station, the power output of the compressor and turbine, as well as the capacity of the gas storage and thermal storage facilities, are predicted.

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

[0063] A compressed air energy storage power station model is established, including compressors, turbines, gas storage tanks, thermal storage tanks, and gas transmission pipelines. Considering capacity constraints, the total cost is used as the objective function, ultimately forming a second-order cone optimization model for layout optimization. The compressor's output can be transferred to the next stage compressor or to the gas storage tank, connected by the gas transmission pipeline network. Similarly, the gas in the storage tank supplies the turbine, and the turbine's output can be further depressurized by the next stage turbine or directly supplied to the synchronous generator for power generation, connected by the gas transmission pipeline network. The thermal storage tank is similar; the heat generated by the compressor is transferred to the thermal storage tank through the heat transmission pipeline network, and the heat required by the turbine is also supplied by the thermal storage tank through the heat transmission pipeline network. The objective function is the system cost, composed of the costs of the compressor, turbine, gas transmission pipeline, and thermal transmission pipeline.

[0064] Since the location of the gas storage facility is determined by salt caverns, mine shafts, etc., and its location is relatively limited, it can be considered fixed. Here, we set its location as (0,0). Then, the layout optimization model of the compressed air energy storage power station can be written in the following form:

[0065]

[0066] Where (x1, y1) represents the location of the thermal storage facility, d is the minimum allowable distance between the thermal storage facility and the gas storage facility, and P C P is the required value for the compressor. t The required value for the turbine. For the location of the compressor, c air c represents the unit cost of the gas pipeline network. heat The unit cost of the heat transmission pipeline network, This indicates the location of the turbine.

[0067] (3) Solve the model using the interior point method.

[0068] The interior-point method is used to solve this problem, ultimately obtaining the optimized layout of each component in the compressed air energy storage power station. 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] Because the optimization direction is to minimize, this relaxation is tight. The model is expressed as a form of second-order cone optimization, which can be solved quickly using the interior point method.

[0074] In summary, this application's embodiments, based on long-term load forecasts and power capacity requirements, determined the scale of the energy storage power station, including the power of the compressor and turbine, and the capacity of the thermal and gas storage tanks. Next, a power station model was established, including key components such as the compressor, turbine, gas storage tank, thermal storage tank, and gas pipeline network. A second-order cone optimization model with total cost as the objective function was set, considering the capacity constraints of each component and their interconnections. Finally, by applying the interior-point method to solve the model, the optimized layout results of each component of the compressed air energy storage power station were obtained, providing a scientific basis for achieving efficient and economical power station design.

[0075] Next, referring to the accompanying drawings, a compressed air energy storage power station component layout optimization device proposed according to an embodiment of this application is described.

[0076] Figure 3 This is a block diagram of a compressed air energy storage power station component layout optimization device according to an embodiment of this 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] The module 100 is used to acquire 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 based on 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 based on the target parameters of at least one component 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 foregoing explanation of the compressed air energy storage power station component layout optimization method embodiment also applies to the compressed air energy storage power station component layout optimization device of this embodiment, and will not be repeated here.

[0080] The compressed air energy storage power station component layout optimization device proposed in this application determines the scale of the compressed air energy storage power station based on long-term load forecast data. Then, a compressed air energy storage power station model including compressors, turbines, gas storage tanks, thermal storage tanks, and gas transmission pipelines is established. Considering capacity constraints, the total cost is used as the objective function to construct a second-order cone optimization model for layout optimization. Finally, the interior-point method is used to solve the problem, ultimately obtaining the layout optimization results for each component of the compressed air energy storage power station. This solves the problems of grid acceptance challenges caused by the intermittency of wind and solar energy and the high cost of compressed air energy storage in existing technologies.

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

[0082] The memory 401, the processor 402, and the computer program stored on the memory 401 and capable of running on the processor 402.

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

[0084] Furthermore, electronic devices also include:

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

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

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

[0088] If the memory 401, processor 402, and communication interface 403 are implemented independently, then the communication interface 403, memory 401, and processor 402 can be interconnected via a bus to complete communication between them. The bus can be an 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 address bus, data bus, control bus, etc. For ease of representation, Figure 4 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0089] 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.

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

[0091] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described method for optimizing the layout of compressed air energy storage power station components.

[0092] This application also provides a computer program product, including a computer program or instructions, which, when executed, implement the above-described compressed air energy storage power station component layout optimization method.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any of the following techniques known in the art, or a combination thereof: 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 (FPGAs), field-programmable gate arrays (FPGAs), etc.

[0097] Those skilled in the art will understand that all or part of the steps of the methods implementing 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.

[0098] 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 the layout of a compressed air energy storage power plant assembly, characterized in that, The method comprises the following steps: obtaining historical load data of a compressed air energy storage power station; predicting long-time load of the compressed air energy storage power station according to the historical load data; predicting target energy storage capacity of the compressed air energy storage power station according to the long-time load, and determining at least one component target parameter of the compressed air energy storage power station according to the target energy storage capacity, the at least one component target parameter comprising at least one of compressor power, turbine power, heat storage tank capacity and gas storage tank capacity; 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 a compressed air energy storage power station model, the layout optimization model being: in, The total cost of the gas pipeline network, The total cost of the heat transmission pipeline network, For the first The power of each compressor, For the first The power of a turbine, Cost per unit compressor power Cost per unit turbine power Location of the thermal storage facility. This represents the minimum permissible distance between the thermal storage facility and the gas storage facility. This is the required value for the compressor. The required value for the turbine. The location of the compressor. The unit cost of the gas pipeline network, The unit cost of the heat transmission pipeline network, The location of the turbine; optimizing component layout of the compressed air energy storage power station based on the layout optimization model, comprising: introducing a slack variable 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 an 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.

2. The compressed air energy storage power plant component layout optimization method of claim 1, wherein, The objective function of the layout optimization model is: wherein, is a relaxation variable characterizing the length of the gas pipe network between the gas storage and the compressor, is a relaxation variable characterizing the length of the gas pipe network between the gas storage and the turbine, is a relaxation variable characterizing the length of the heat pipe network between the heat storage and the compressor, is a relaxation variable characterizing the length of the heat pipe network between the heat storage and the turbine.

3. The compressed air energy storage power plant component layout optimization method of claim 1, wherein, The compressed air energy storage power station model comprises at least one component model of a compressor, a turbine, a gas storage, a heat storage and a gas pipeline network.

4. A compressed air energy storage power plant component layout optimization apparatus, characterized by, The method comprises: a obtaining module configured to obtain historical load data of a compressed air energy storage power station; a predicting module configured to predict long-time load of the compressed air energy storage power station according to the historical load data; a determining module configured to predict target energy storage capacity of the compressed air energy storage power station according to the long-time 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 at least one component target parameter comprising at least one of compressor power, turbine power, heat storage tank capacity and gas storage tank capacity; an optimizing module configured to establish 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 a compressed air energy storage power station model, the layout optimization model being: wherein, Ctotai is the total cost of the gas pipeline network, Ctotai is the total cost of the heat pipeline network, Pj is the power of the jth compressor, Pj is the power of the jth compressor, Pj is the power of the jth turbine, Pj is the power of the jth turbine, Cj is the cost required per unit of compressor power, Cj is the cost required per unit of turbine power, Ri is the location of the ith thermal reservoir, Rmin is the minimum value allowed for the distance between the thermal reservoir and the gas reservoir, Rj is the required value for the jth compressor, Rj is the required value for the jth turbine, Rj is the location of the jth compressor, Cj is the unit cost of the gas pipeline network, Cj is the unit cost of the heat pipeline network, Rj is the location of the jth turbine; optimize component layout of the compressed air energy storage power station based on the layout optimization model, comprising: introducing a slack variable 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 an 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.

5. An electronic device, comprising: The method comprises: a memory, a processor and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the compressed air energy storage power station component layout optimization method of any one of claims 1-3.

6. A computer readable storage medium having stored thereon a computer program or instructions, characterized in that, The computer program or instructions are executed to implement the compressed air energy storage power station component layout optimization method of any one of claims 1-3.

7. A computer program product comprising computer programs or instructions, characterized in that, The computer program or instructions are executed to implement the compressed air energy storage power station component layout optimization method of any one of claims 1-3.

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