GIS-based mountain compressed air energy storage power station site selection method and system

Abstract

The invention relates to the technical field of electric power energy storage, and discloses a GIS-based mountain compressed air energy storage power station site selection method and system, and the method comprises the steps: obtaining first-stage data, second-stage data and third-stage data in a target mountain region; obtaining an alternative region based on the first-level data; obtaining a geological available area based on the secondary data; screening out mountains meeting conditions based on the secondary data, and constructing contours and main ridge lines of the mountains meeting the requirements; determining a mountain reference region, and calculating the area of the reference region; solving an optimal internal rectangle of the mountain reference area, judging whether the mountain reference area meets the requirements of the optimal internal rectangle or not, and determining a target available mountain; the score of the target available mountain is determined by integrating the second-level data and the third-level data, and the area with the high score is used as the final site selection; according to the method, the problems of insufficient accuracy, low timeliness and poor visualization of an existing site selection method are solved.

Description

A GIS-based method and system for site selection of compressed air energy storage power stations in mountainous areas Technical Field

[0001] This invention relates to the field of power energy storage technology, and in particular to a GIS-based method and system for site selection of mountain compressed air energy storage power stations. Background Technology

[0002] According to statistics from CNESA DataLink's global energy storage database, by the end of 2024, the cumulative installed capacity of operational power storage projects in China reached 137.9 GW, accounting for 37.1% of the global market, representing a year-on-year increase of 59.9%. The cumulative installed capacity of new energy storage reached 78.3 GW, accounting for 57%, surpassing pumped hydro storage for the first time. New energy storage is increasingly becoming the mainstream technology for addressing the large-scale grid integration and consumption of new energy sources and improving the power system's security and supply capabilities. Compressed air energy storage is one such new energy storage method. Its construction can effectively alleviate the "randomness," "intermittency," and "fluctuation" problems of new energy sources, and it has advantages such as short construction cycles, large construction scale, safety and reliability, and greater environmental friendliness. It is a promising long-term, large-scale energy storage form among new energy storage methods. However, to achieve a reasonable layout and orderly construction of compressed air energy storage power stations, site selection and planning are the "most critical link" and the "first step," serving as an important prerequisite and fundamental guarantee for the overall construction work.

[0003] Currently, the planning and site selection for compressed air energy storage (CAES) primarily relies on traditional manual map-based methods. This approach is inefficient and struggles to systematically assess site conditions, often leading to the overlooking of potentially high-quality sites. Furthermore, the site selection results lack effective systematic integration and spatial visualization, hindering informed decision-making. With the rapid development of the energy storage industry, CAES site selection demands higher quality and efficiency. Traditional methods, lacking accuracy, timeliness, and visualization, are ill-suited to current requirements. This is particularly true for complex mountainous terrain, where traditional methods struggle to provide high-quality and efficient solutions. Therefore, a new CAES power plant site selection method is urgently needed to address the shortcomings of current methods and rapidly promote the scientific and orderly development of CAES. Summary of the Invention

[0004] This invention provides a GIS-based method and system for site selection of mountain compressed air energy storage power stations, in order to solve the problems of insufficient accuracy, low timeliness, and poor visualization in existing site selection methods.

[0005] To achieve the above objectives, the present invention provides the following technical solution: Firstly, the present invention provides a GIS-based method for site selection of a compressed air energy storage power station in mountainous areas, comprising: acquiring primary, secondary, and tertiary data of a target mountainous region; performing an erasure analysis on the target mountainous region based on the primary data, and using the erased target mountainous region containing the primary data as a candidate region; filtering the candidate region based on the secondary data, and selecting regions whose geological conditions meet the engineering requirements to obtain geologically usable regions; constructing contour lines in the geologically usable regions, identifying mountains in the geologically usable regions based on the contour lines, and selecting mountains that meet the conditions according to height requirements, constructing the outline and main ridgeline of the mountains that meet the conditions; determining the mountain reference region in the mountain outline based on relevant parameters of the compressed air energy storage power station, and calculating the area of ​​the reference region; solving for the optimal internal rectangle of the mountain reference region using a geometric algorithm, determining whether the mountain reference region meets the optimal internal rectangle requirements, and determining the target usable mountains; determining the score of the target usable mountains based on the secondary and tertiary data, and using the region with the highest score as the final site selection.

[0006] Optionally, the primary data includes: data on the three zones and three lines, data on mineral resources covered by the grid, and data on military facilities; the secondary data includes: geological data and topographic data; the tertiary data includes: river system data, road traffic data, grid corridor data, substation data, wind farm and photovoltaic power station data, data on existing energy storage power stations, and residential data.

[0007] Optionally, the step of filtering the candidate areas based on the secondary data to select areas whose geological conditions meet the engineering requirements and obtain geologically usable areas includes: filtering the candidate areas based on the geological data in the secondary data, and selecting areas with igneous rocks, a distance greater than a certain range from active faults, and relatively low seismic intensity as geologically usable areas.

[0008] Optionally, the step of constructing contour lines in the geologically available area and identifying mountains in the geologically available area based on the contour lines includes: generating contour lines in the geologically available area using a digital elevation model; finding the highest closed contour line among the generated contour lines; generating internal representative points in the highest closed contour line; and identifying all closed contour lines containing internal representative points as mountains in the geologically available area.

[0009] Optionally, the step of selecting suitable mountains based on height requirements and constructing the outlines and main ridgelines of these mountains includes: determining the required mountain height; selecting suitable mountains from the geologically available area based on these height requirements; using the lowest contour lines as the mountain outlines within the suitable mountains; performing flow direction analysis within the suitable mountains to calculate the flow direction; performing cumulative flow analysis based on the flow direction to generate a cumulative flow grid and identify flow convergence areas; determining the river network and obtaining the watershed boundary lines based on the flow convergence areas; and determining the ridgelines based on the watershed boundary lines.

[0010] Optionally, the determination of the mountain reference region based on relevant parameters of the compressed air energy storage power station within the mountain contour, and the calculation of the reference region area, includes: performing lateral, longitudinal, and overlay analyses on the mountain contour based on relevant parameters of the compressed air energy storage power station to determine the mountain reference region; wherein, the lateral analysis includes: performing an inward shrinkage analysis on the mountain contour according to the burial depth requirements in the relevant parameters of the compressed air energy storage power station to obtain the region that meets the lateral burial depth requirements; the longitudinal analysis includes: finding contour lines that meet the burial depth requirements upwards from the contour lines where the mountain contour is located, based on the burial depth requirements in the relevant parameters of the compressed air energy storage power station to obtain the region that meets the longitudinal burial depth requirements; the overlay analysis includes: performing an overlay analysis on the regions that meet the lateral and longitudinal burial depth requirements to obtain the mountain reference region; and calculating the area of ​​the reference region based on the mountain reference region, wherein the area is calculated as follows: In the formula, Indicates the area of ​​the reference region. The number of vertices of the polygon. For the first The x-coordinates of the vertices, For the first The y-coordinates of the vertices, For the first The x-coordinates of the vertices, For the first The ordinates of each vertex.

[0011] Optionally, the step of solving for the optimal internal rectangle of the mountain reference area using a geometric algorithm, determining whether the mountain reference area meets the requirements of the optimal internal rectangle, and identifying the target usable mountain includes: determining the total volume of the caverns in the relevant parameters of the compressed air energy storage power station. Length of the cavern Base multiple of cavern length Spacing between caverns , diameter of the cavern Requirement: Calculate the optimal length of the inner rectangle. , =Cave length Determine whether the length of the mountain body perpendicular to the main ridge line is greater than or equal to the length of the optimal internal rectangle. ×Multiple of the cavern length benchmark; if the conditions are met, then the mountain meets the optimal internal rectangular length requirement; calculate the number of caverns. , , And based on the number of caverns Calculate the optimal inner rectangle width , = +( ) Determine whether the width of the mountain body parallel to the main ridge line is greater than or equal to the width of the optimal internal rectangle. If the conditions are met, the mountain body meets the optimal internal rectangle width requirement; when the length of the mountain body perpendicular to the main ridge line and the width parallel to the main ridge line both meet the optimal internal rectangle requirement, then the mountain body is a target usable mountain body.

[0012] Optionally, determining the score of the target usable mountain based on the secondary and tertiary data, and selecting the area with the highest score as the final site selection, includes: analyzing and determining the score of the target usable mountain using geological data, topographic data, river system data, and road traffic data, wherein the location score is calculated according to the following formula: ; , =0.5, =0.3, =0.1, =0.1; In the formula, For geological scores, The baseline area of ​​the mountain is scored. The score is based on the distance from the river system. Scoring is based on distance from road traffic. For lithology score, The score is based on the distance from the active fracture. To score the earthquake intensity, , respectively, are the corresponding weights; the region with the highest location score is selected as the final location.

[0013] Secondly, embodiments of this application provide a GIS-based mountain compressed air energy storage power station site selection system, characterized in that it includes a processor and a memory; the memory is used to store computer programs; the processor is used to execute any of the steps described in the first aspect when executing the program stored in the memory.

[0014] Beneficial Effects: The GIS-based site selection method for compressed air energy storage power stations in mountainous areas provided by this invention introduces GIS spatial analysis methods into the field of compressed air energy storage site selection. Through the application of this technology, the accuracy of site selection is significantly improved, and the site selection cycle is effectively shortened. Simultaneously, by using a weighted assignment method to conduct in-depth analysis of compressed air energy storage sites, the optimal mountain locations can be scientifically and efficiently selected, providing a precise decision-making basis for site selection. Attached Figure Description

[0015] Figure 1 is a flowchart of the GIS-based mountain compressed air energy storage power station site selection method of preferred embodiment 1 of the present invention; Figure 2 is a schematic diagram of the mountain identification process of preferred embodiment of the present invention; Figure 3 is a schematic diagram of the ridgeline generation process of preferred embodiment of the present invention; Figure 4 is a schematic diagram of the benchmark area generation process of preferred embodiment of the present invention; Figure 5 is a flowchart of the GIS-based mountain compressed air energy storage power station site selection method of preferred embodiment 2 of the present invention. Detailed Implementation

[0016] The technical solution of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, the terms "an" or "a" and similar terms do not indicate a quantity limitation, but rather indicate the presence of at least one. The terms "connected" or "linked" and similar terms are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up," "down," "left," "right," etc., are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship also changes accordingly.

[0018] Example 1 (see Figure 1) provides a GIS-based method for site selection of a compressed air energy storage power station in mountainous areas. The method includes: acquiring primary, secondary, and tertiary data from a target mountainous region; performing an erasure analysis on the target mountainous region based on the primary data, and using the erased target mountainous region containing the primary data as a candidate region; filtering the candidate region based on the secondary data, selecting regions whose geological conditions meet engineering requirements to obtain geologically usable regions; constructing contour lines in the geologically usable regions, identifying mountains within the geologically usable regions based on the contour lines, and selecting mountains that meet the height requirements, constructing the contour lines and main ridgelines of the mountains that meet the requirements; determining the mountain reference region in the mountain contour based on relevant parameters of the compressed air energy storage power station, and calculating the area of ​​the reference region; solving for the optimal internal rectangle of the mountain reference region using a geometric algorithm, determining whether the mountain reference region meets the optimal internal rectangle requirements, and identifying the target usable mountains; determining the score of the target usable mountains based on the secondary and tertiary data, and using the region with the highest score as the final site selection.

[0019] Optionally, the first-level data includes: data on the three zones and three lines, data on mineral resources covered by the grid, and data on military facilities; the second-level data includes: geological data and topographic data; the third-level data includes: river system data, road traffic data, grid corridor data, substation data, wind farm and photovoltaic power station data, data on existing energy storage power stations, and residential data.

[0020] In the above embodiments, a multi-source spatial database is constructed and hierarchically classified. Various data types are collected and acquired, forming primary, secondary, and tertiary data. Primary data consists of decisive data, including data on the three zones and three lines (referring to waterways, railway lines, and highways), mineral resources, and military facilities. Secondary data consists of restrictive data, including geological and topographical data. Tertiary data consists of impact data, including river system data, road traffic data, power grid corridor data, substation data, wind farm and photovoltaic power station data, existing energy storage power station data, and residential data.

[0021] The data on the three zones and three lines, mineral resources, and military facilities in the first-level data, the geological data and topographic data in the second-level data, and the river system data, road traffic data, grid corridor data, substation data, wind farm and photovoltaic power station data, existing energy storage power station data, and residential data in the third-level data are only examples here and are not limited. Under other conditions or needs, the data types included in the first-level, second-level, and third-level data can be added according to the actual situation.

[0022] Optionally, based on the secondary data, the candidate areas are screened to select areas whose geological conditions meet the engineering requirements to obtain geologically usable areas. This includes: screening the candidate areas based on the geological data and topographic data in the secondary data, and selecting areas with igneous rocks, a distance greater than a certain range from active faults, and relatively low seismic intensity as geologically usable areas through the geological data and topographic data.

[0023] Optionally, contour lines are constructed in the geologically available area, and mountains in the geologically available area are identified based on the contour lines, including: generating contour lines in the geologically available area through a digital elevation model, finding the highest closed contour line among the generated contour lines; generating internal representative points in the highest closed contour line, and identifying all closed contour lines containing internal representative points as mountains in the geologically available area.

[0024] In the above embodiment, as shown in Figure 2, within the geologically usable area, contour lines are generated using a digital elevation model (with an accuracy better than 30m), and mountains are identified through the contour lines.

[0025] Optionally, mountains meeting the height requirements are selected, and their outlines and main ridgelines are constructed. This includes: determining the required mountain height; selecting mountains meeting the requirements from geologically available areas; using the lowest contour lines as the mountain outlines within the mountains meeting the requirements; calculating the water flow direction within the mountains meeting the requirements; generating a flow accumulation grid based on the flow direction; identifying water flow convergence areas through the flow accumulation grid; determining the river network and obtaining the watershed boundary line based on the water flow convergence area; and determining the ridgeline based on the watershed boundary line.

[0026] In the above embodiment, as shown in Figure 3, mountains within the region that meet the height requirements are selected by inputting the required height. For the mountains that meet the height requirements, the mountain outline and main ridgeline are drawn. The mountain outline is the lowest contour line in mountain identification. The main ridgeline needs to be generated by combining database data and using the GIS hydrological analysis module, sequentially executing flow direction analysis, flow accumulation, and the watershed tool.

[0027] Optionally, a benchmark region for the mountain is determined based on relevant parameters of the compressed air energy storage power station within the mountain contour, and the area of ​​the benchmark region is calculated. This includes: performing lateral, longitudinal, and overlay analyses on the mountain contour based on relevant parameters of the compressed air energy storage power station to determine the benchmark region; wherein, the lateral analysis includes: performing an inward shrinkage analysis on the mountain contour according to the burial depth requirements in the relevant parameters of the compressed air energy storage power station to obtain the region that meets the lateral burial depth requirements; the longitudinal analysis includes: finding contour lines that meet the burial depth requirements upwards from the contour lines where the mountain contour is located, based on the burial depth requirements in the relevant parameters of the compressed air energy storage power station to obtain the region that meets the longitudinal burial depth requirements; the overlay analysis includes: performing an overlay analysis on the regions that meet the lateral and longitudinal burial depth requirements to obtain the benchmark region of the mountain; and calculating the area of ​​the benchmark region based on the benchmark region of the mountain, wherein the area calculation satisfies the following relationship: In the formula, Indicates the area of ​​the reference region. The number of vertices of the polygon. For the first The x-coordinates of the vertices, For the first The y-coordinates of the vertices, For the first The x-coordinates of the vertices, For the first The ordinates of each vertex.

[0028] In the above embodiment, as shown in Figure 4, relevant limiting parameters (such as total cave volume, cave length, cave length benchmark multiple, cave spacing, cave diameter, and cave burial depth) are input. Based on the cave burial depth requirements, the mountain contour is analyzed laterally, longitudinally, and superimposed to obtain the benchmark area. The area of ​​the benchmark area is then calculated using the shoelace formula.

[0029] Optionally, a geometric algorithm is used to solve for the optimal internal rectangle of the mountain reference area, determine whether the mountain reference area meets the requirements for the optimal internal rectangle, and identify the target usable mountain. This includes: solving for the optimal internal rectangle of the mountain reference area based on its area and shape using a geometric algorithm; and determining the total volume of the caverns in the relevant parameters of the compressed air energy storage power station. Length of the cavern Base multiple of cavern length Spacing between caverns , diameter of the cavern Requirement: Calculate the optimal length of the inner rectangle. , =Cave length Determine whether the length of the mountain body perpendicular to the main ridge line is greater than or equal to the length of the optimal internal rectangle. ×Multiple of the cavern length benchmark; if the conditions are met, then the mountain meets the optimal internal rectangular length requirement; calculate the number of caverns. , , And based on the number of caverns Calculate the optimal inner rectangle width , = +( ) Determine whether the width of the mountain body parallel to the main ridge line is greater than or equal to the width of the optimal internal rectangle. If the conditions are met, the mountain body meets the optimal internal rectangle width requirement; when the length of the mountain body perpendicular to the main ridge line and the width parallel to the main ridge line both meet the optimal internal rectangle requirement, then the mountain body is a target usable mountain body.

[0030] Optionally, the scores of available target mountains are determined based on secondary and tertiary data, and areas with high scores are selected as the final site selection. This includes: using geological data, topographic data, river system data, and road traffic data to analyze and determine the scores of available target mountains, wherein the location score is calculated according to the following relationship: ; , =0.5, =0.3, =0.1, =0.1; In the formula, For geological scores, The baseline area of ​​the mountain is scored. The score is based on the distance from the river system. Scoring is based on distance from road traffic. For lithology score, The score is based on the distance from the active fracture. To score the earthquake intensity, , which are the corresponding weights; the area with the highest location score is selected as the final location.

[0031] Example 2 is shown in Figure 5, which is a schematic diagram of the method flow of the present invention. The calculation results of Example 1 are shown in Tables 1 and 2.

[0032] Step 1: In the site selection planning of compressed air energy storage in a certain area, retrieve data from the database on the three zones and three lines, mineral resources covered by the area, military facilities, geological data, topographic data, river system data, transportation data, grid corridor data, substation data, wind farm and photovoltaic power station data, existing energy storage power station data, and residential area data.

[0033] Step 2: Through erasure analysis, exclude areas involving data related to the three zones and three lines, mineral resources, and military facilities.

[0034] Step 3: In the areas selected in Step 2, use open-source geological data to select areas with granite lithology, a distance greater than 1 km from active faults, and seismic intensity of 6 or 7 degrees.

[0035] Step 4: Using terrain data (accuracy better than 30m), generate contour lines at 10m intervals, and identify mountains using these contour lines. Input a mountain height of 200m, identify mountains within the area, and filter out mountains with a height greater than 200m.

[0036] Step 5: For mountains with a height greater than 200m, draw the mountain outline and main ridgeline.

[0037] Step 6: Input the relevant limiting parameters, namely the total volume of the cavern: 300,000 m³ 3 The cavern length is 600m, the baseline multiple of the cavern length is 2, the cavern spacing is 100m, the cavern diameter is 10m, and the cavern depth is 150m. Based on the 150m cavern depth requirement, the mountain contour is analyzed laterally, longitudinally, and through superposition, to determine the baseline area and calculate its area.

[0038] Step 7: Based on the total volume of the caverns, the length of the caverns, the reference multiple of the cavern length, the distance between caverns, and the required diameter of the caverns, solve for the ideal optimal internal rectangle using a geometric algorithm, and determine whether the optimal internal rectangle of the mountain meets the requirements (① determine whether the length perpendicular to the main ridge line is greater than or equal to the reference multiple of the cavern length; ② determine whether the width parallel to the main ridge line is greater than or equal to the calculated minimum width). If the requirements are met, then the mountain is the target usable mountain.

[0039] Step 8: Use impact data, such as river system data and road traffic data, to conduct spatial analysis. The analysis target can be the mountain and its distance. Use weighting methods to optimize the final site selection.

[0040] Table 1. Partial Parameter Table of Example 2

[0041] Table 2 Calculation Results of Example 2

[0042] The site accuracy rate was: ,in The total number of stations, This refers to the number of sites that do not meet the criteria during manual review.

[0043] The calculation results show that the program can greatly improve the site selection speed, and the site selection accuracy meets the requirements of engineering applications.

[0044] This application also provides a GIS-based mountain compressed air energy storage power station site selection system, characterized in that it includes a processor and a memory; the memory is used to store computer programs; the processor is used to execute the program stored in the memory to implement any of the method steps described in the GIS-based mountain compressed air energy storage power station site selection method.

[0045] The aforementioned GIS-based mountain compressed air energy storage power station site selection system can implement various embodiments of the aforementioned GIS-based mountain compressed air energy storage power station site selection method and achieve the same beneficial effects, which will not be elaborated here.

[0046] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A GIS-based site selection method for mountain compressed air energy storage power stations, characterized in that, include: Acquire primary, secondary, and tertiary data for the target mountainous region; Based on the primary data, the target mountainous area is subjected to erasure analysis, and the target mountainous area containing the primary data is erased is used as a candidate area. Based on the secondary data, the candidate areas are filtered to select areas whose geological conditions meet the engineering requirements, thus obtaining geologically usable areas. Contour lines are constructed within the geologically available area. Mountains within the geologically available area are identified based on the contour lines, and mountains that meet the height requirements are selected. The outlines and main ridgelines of the mountains that meet the requirements are constructed. A reference area for the mountain is determined within the mountain outline based on relevant parameters of the compressed air energy storage power station, and the area of ​​the reference area is calculated. The optimal internal rectangle of the reference area is solved using a geometric algorithm. It is then determined whether the reference area meets the requirements for the optimal internal rectangle, and the target available mountain is identified. Based on the secondary and tertiary data, the scores of the target available mountains are determined, and the areas with high scores are selected as the final sites.

2. The GIS-based site selection method for mountain compressed air energy storage power stations according to claim 1, characterized in that, The primary data includes: data on the three zones and three lines, data on mineral resources covered by the grid, and data on military facilities; the secondary data includes: geological data and topographic data; the tertiary data includes: river system data, road traffic data, grid corridor data, substation data, wind farm and photovoltaic power station data, data on existing energy storage power stations, and residential data.

3. The GIS-based site selection method for mountain compressed air energy storage power stations according to claim 2, characterized in that, The step of filtering the candidate areas based on the secondary data to select areas whose geological conditions meet the engineering requirements and obtain geologically usable areas includes: filtering the candidate areas based on the geological data in the secondary data, and selecting areas with igneous rocks, distances from active faults greater than a certain range, and seismic intensity lower than the regional average as geologically usable areas.

4. The GIS-based site selection method for mountain compressed air energy storage power stations according to claim 2, characterized in that, The step of constructing contour lines in the geologically usable area and identifying mountains in the geologically usable area based on the contour lines includes: generating contour lines in the geologically usable area using a digital elevation model; finding the highest closed contour line among the generated contour lines; generating internal representative points in the highest closed contour line; and identifying all closed contour lines containing internal representative points as mountains in the geologically usable area.

5. The GIS-based site selection method for mountain compressed air energy storage power stations according to claim 2, characterized in that, The process of selecting suitable mountains based on height requirements and constructing their outlines and main ridgelines includes: determining the required mountain height; selecting suitable mountains from the geologically available area based on these requirements; using the lowest contour lines as the mountain outlines within the suitable mountains; performing flow direction analysis within the suitable mountains to calculate the flow direction; conducting cumulative flow analysis based on the flow direction to generate a cumulative flow grid and identify flow convergence areas; determining the river network and watershed boundaries based on the flow convergence areas; and determining the ridgelines based on the watershed boundaries.

6. The GIS-based site selection method for mountain compressed air energy storage power stations according to claim 2, characterized in that, The process of determining a benchmark region for the mountain based on relevant parameters of the compressed air energy storage power station within the mountain contour and calculating the area of ​​the benchmark region includes: performing lateral, longitudinal, and overlay analyses on the mountain contour based on relevant parameters of the compressed air energy storage power station to determine the benchmark region; wherein, the lateral analysis includes: performing an inward shrinkage analysis on the mountain contour according to the burial depth requirements in the relevant parameters of the compressed air energy storage power station to obtain areas that meet the lateral burial depth requirements; the longitudinal analysis includes: finding contour lines that meet the burial depth requirements upwards from the contour lines where the mountain contour is located, based on the burial depth requirements in the relevant parameters of the compressed air energy storage power station to obtain areas that meet the longitudinal burial depth requirements; the overlay analysis includes: performing an overlay analysis on the areas that meet the lateral and longitudinal burial depth requirements to obtain the benchmark region; and calculating the area of ​​the benchmark region based on the benchmark region, wherein the area is calculated using the following formula: In the formula, Indicates the area of ​​the reference region. The number of vertices of the polygon. For the first The x-coordinates of the vertices, For the first The y-coordinates of the vertices, For the first The x-coordinates of the vertices, For the first The ordinates of each vertex.

7. The GIS-based site selection method for mountain compressed air energy storage power stations according to claim 2, characterized in that, The process of solving for the optimal internal rectangle of the mountain's reference area using geometric algorithms, determining whether the mountain's reference area meets the requirements for the optimal internal rectangle, and identifying the target usable mountain includes: determining the total volume of the caverns in the relevant parameters of the compressed air energy storage power station. Length of the cavern Base multiple of cavern length Spacing between caverns , diameter of the cavern Requirement: Calculate the optimal length of the inner rectangle. ， =Cave length Determine whether the length of the mountain body perpendicular to the main ridge line is greater than or equal to the length of the optimal internal rectangle. ×Multiple of the cavern length benchmark; if the conditions are met, then the mountain meets the optimal internal rectangular length requirement; calculate the number of caverns. ， ， And based on the number of caverns Calculate the optimal inner rectangle width ， = +( ) Determine whether the width of the mountain body parallel to the main ridge line is greater than or equal to the width of the optimal internal rectangle. If the conditions are met, the mountain body meets the optimal internal rectangle width requirement; when the length of the mountain body perpendicular to the main ridge line and the width parallel to the main ridge line both meet the optimal internal rectangle requirement, then the mountain body is a target usable mountain body.

8. The GIS-based site selection method for mountain compressed air energy storage power stations according to claim 2, characterized in that, The process of determining the score of the target usable mountain based on the secondary and tertiary data, and selecting the area with the highest score as the final site selection, includes: using geological data, topographic data, river system data, and road traffic data to analyze and determine the score of the target usable mountain, wherein the location score is calculated according to the following relationship: ； ， =0.5， =0.3， =0.1， =0.1； In the formula, For geological scores, The baseline area of ​​the mountain is scored. The score is based on the distance from the river system. Scoring is based on distance from road traffic. For lithology score, The score is based on the distance from the active fracture. To score the earthquake intensity, , respectively, are the corresponding weights; the region with the highest location score is selected as the final location.

9. A GIS-based site selection system for mountain compressed air energy storage power stations, characterized in that, It includes a processor and a memory; the memory is used to store computer programs; the processor, when executing the program stored in the memory, implements the steps of the method described in any one of claims 1-8.

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