A GIS-based site selection method for pumped storage power stations

Abstract

This invention discloses a GIS-based site selection method for pumped storage power stations, solving the problems of low comprehensiveness and accuracy of traditional site selection methods, difficulties and certain dangers in field surveys, high manpower and material costs, and low efficiency. This invention uses a statistical method of undulation to obtain raster data of undulation, resulting in n potential reservoir surfaces. It calculates the distance between each feature in the reservoir surface feature layer and its neighboring features, obtaining a statistical table in .dbf format. By judging and calculating the elevation difference and distance-to-elevation ratio, it obtains a statistical table of the upper and lower reservoirs of the pumped storage power station and the locations of the upper and lower reservoirs. This optimizes the site selection process for pumped storage power stations, significantly reducing manpower and material costs, improving site selection efficiency, reducing the incompleteness and inaccuracy of manual identification, indirectly reducing the dangers of field surveys, and meeting the need for accurate and rapid site selection for pumped storage power stations in large areas.

Description

Technical Field

[0001] This invention relates to the fields of energy storage and geographic information science and technology, specifically to a GIS-based method for selecting the site of a pumped storage power station. Background Technology

[0002] Pumped storage is currently the most technologically mature, economically optimal, and best suited for large-scale development of a green, low-carbon, clean, and flexible power source for the power system. It works well in conjunction with wind power, solar power, nuclear power, and thermal power. A pumped storage power station utilizes electricity generated during off-peak hours to pump water to an upper reservoir, and then releases the water to a lower reservoir to generate electricity during peak hours. It is also known as a pumped-storage hydropower station. It can convert excess electricity during periods of low grid load into high-value electricity during peak periods. It is also suitable for frequency and phase regulation, stabilizing the frequency and voltage of the power system, and serves as a backup power source in case of emergencies.

[0003] Site selection for pumped storage power stations is a crucial step in the early planning stages. Traditional site selection methods often rely on visual assessment using topographic maps, followed by field surveys for verification. However, medium- to long-term pumped storage planning frequently requires site selection at the provincial (autonomous region) level, covering vast areas where visual assessment alone is insufficient in terms of comprehensiveness and accuracy. Furthermore, most proposed power station areas have harsh natural environments, making field surveys difficult and potentially dangerous. Therefore, traditional methods are extremely resource-intensive and inefficient. Summary of the Invention

[0004] The purpose of this invention is to improve the efficiency of existing work through geographic information science and technology. It provides a GIS-based method for selecting the site of pumped storage power stations. Based on topographic maps, it quickly and accurately selects point pairs that meet the requirements for reservoir formation, upper and lower reservoir pairing, and altitude, which can then be used as the upper and lower reservoirs of a usable pumped storage power station. This solves the technical problem that existing methods cannot meet the requirements for accurate and rapid site selection of pumped storage power stations. GIS (Geographic Information System or Geo-Information System) is sometimes also called a geoscience information system.

[0005] To address the aforementioned technical problems, this invention provides a GIS-based site selection method for pumped storage power stations, comprising the following steps:

[0006] Step 1: Obtain raster data of relief from the DEM topographic map using statistical methods; process the raster data of relief to obtain n potential reservoir surfaces; DEM Digital Elevation Model;

[0007] Step 2: Obtain the average elevation within each feature of the n potential reservoir surfaces to obtain a statistical data table. Calculate the distance between each feature in the reservoir surface feature layer and its neighboring features to obtain a statistical table in the format of .dbf in the database file. Process the .dbf format statistical table and obtain the target result table of the upper and lower reservoir combination by judging and calculating the elevation difference and distance-to-elevation ratio.

[0008] Step 3: Obtain the average elevation of the target surface features from the target results table. Average elevation of adjacent surface features By performing discrimination calculations, we obtained the statistical table of the upper and lower reservoirs of the pumped storage power station and the location of the upper and lower reservoirs of the pumped storage power station.

[0009] Preferably, step 1 involves obtaining raster data of undulation from the DEM topographic map using statistical methods; processing the raster data of undulation to obtain n potential reservoir surfaces, and further includes the following steps:

[0010] Step 101: Calculate the undulation within a radius of R meters centered on each grid point of the DEM topographic map. Calculate the grid data of topographic undulation by the cell value. The R value is set according to experience.

[0011] Step 102: Extract grid points with undulation less than the preset value K from the undulation grid data generated in Step 101; select grid cells that meet the conditions to generate target grid data, wherein the preset value K is determined based on empirical values.

[0012] Step 103: Convert the target raster data extracted in Step 102 into vector polygon features;

[0013] Step 104: Create buffer polygons at an outward distance of R meters for the surface features generated in step 103 to obtain all potential library surfaces that meet the library requirements.

[0014] Preferably, step 101 further includes obtaining the DEM topographic map through remote sensing aerial survey processing, where the raster cell value represents the average elevation value of the corresponding land surface; calculating the cell elevation range within a circular area with a radius of R meters around each raster cell using a statistical method of topographic relief; and generating raster data with cell values ​​representing topographic relief by calculating the difference between the maximum and minimum cell elevations within the circular area: the formula for calculating topographic relief S is:

[0015] S = H max -H min

[0016] Among them, H max H min These are the maximum and minimum elevation values ​​within the analysis range, respectively.

[0017] Preferably, step 2 further includes the following steps:

[0018] Step 201: Calculate the average elevation within each surface feature generated in Step 1, and the statistical results are presented in a data table; the template for calculating the average elevation of the generated surface features defines a field named MEAN, and the MEAN field is applicable to the average elevation of all surface features;

[0019] Step 202: Connect the statistical data table generated in step 201 with the attribute table of the database features generated in step 104 according to the feature number OBJECTID to generate a connection table; the connection table is attached to the attribute table of the database feature layer, and the associated data can be accessed through the selected features or records in the layer or statistical data table to form the target database feature attribute table.

[0020] Step 203: Convert the target surface feature attribute table processed in Step 202 into a .dbf format file;

[0021] Step 204: Using the target database feature attribute table (.dbf format file) generated in Step 203, calculate the distance L between each feature in the database feature layer and its neighboring features using the distance calculation method. p The distance refers to determining the distance between each feature in the input features and a specified number of neighboring features within the search radius, and outputting the results as a statistical table in .dbf format; wherein, the search radius and the specified number of neighboring features are preset based on experience;

[0022] Step 205: Delete duplicate matching records from the statistical table generated in step 204;

[0023] Step 206: Add the average elevation of the target surface features to the target statistics table in .dbf format after step 205. Average elevation of adjacent surface features Elevation difference ΔH(x) i ,x j ), Distance-to-height ratio ΔV(x) i ,x j The target data statistics table is generated after the four calculation formula templates are used.

[0024] Step 207: Assign the average elevation value of the surface feature to the average elevation value of the target surface feature.

[0025] Convert the target data statistics table processed in step 206 and the database surface feature attribute table generated in step 203 into .dbf format files, and sort them by target surface feature number q. i After connection, assign the MEAN field of the surface feature generated in step 201 to the target surface feature average elevation. Then delete the MEAN field that has been assigned a value;

[0026] Step 208: Assign the average elevation value of adjacent surface features to the average elevation value of adjacent surface features. The nearest neighbor analysis statistics table processed in step 206 and the average elevation of the surface features generated in step 203.dbf file are then sorted by the nearest surface feature number q. j After connecting, assign the MEAN field of the polygon feature generated in step 201 to the average elevation of the current neighboring feature. Then delete the MEAN field that has been assigned a value;

[0027] Step 209: Based on the average elevation of the target surface elements Average elevation of adjacent surface elements Calculate the elevation difference ΔH(x) i ,x j ) and height ratio ΔV(x) i ,x j ); Obtain a statistical table of target data with assigned values ​​for elevation difference and distance-to-height ratio;

[0028] Step 210: Select upper and lower warehouse combinations that meet the requirements of upper and lower warehouse pairing based on preset conditions.

[0029] Preferably, obtaining the target data statistics table with assigned values ​​for elevation difference and distance-to-height ratio further includes the following steps:

[0030] Step 301: First, calculate the elevation difference ΔH(x) i ,x j Calculate, the formula is:

[0031]

[0032] Where: ΔH(xi,xj) represents the elevation difference. For element x i Average elevation, For element x j Average elevation;

[0033] Step 302 then adjusts the distance-to-height ratio ΔV(x) i ,x j Calculate, the formula is:

[0034]

[0035] Where: x i xj represents a feature of each face feature, and xj represents a feature of a neighboring face feature; ΔV(xi,xj) represents the distance-to-height ratio, and L p (xi,xj) represents the distance between each feature and its neighboring features, and ΔH(xi,xj) represents the elevation difference; thus, a statistical table of target data with elevation difference and distance-to-elevation ratio assigned values ​​is obtained.

[0036] Preferably, the distance calculation method in step 204 refers to nearest neighbor data processing, where distance L... p The calculation formula is:

[0037]

[0038] in,

[0039] Preferably, the conditional filtering method in step 210 is as follows: Add a field `zonghefen` to the target data statistics table after assigning values ​​to the height difference and distance-to-height ratio in step 209, and then filter the height difference ΔH(x) by... i ,x j ) and height ratio ΔV(x) i ,x j Perform a discrimination calculation and assign a value to the zonghefen field. The discrimination calculation conditions are as follows:

[0040] If the condition M1 < ΔV(x) is satisfied i ,x j If )≤M2, then further determine that the elevation difference h1≤ΔH(x) i ,x j )≤h2;

[0041] If the elevation difference result meets the condition, the zonghefen field value is 1; otherwise, the zonghefen field value is 0.

[0042] If the condition M1 < ΔV(x) is not met i ,x j If M1 ≤ M2, then the value of the zonghefen field is 0; where M1 and M2 are determined based on experience; h1 and h2 are also determined based on experience.

[0043] Finally, the rows with a total value of 1 are selected, and the final target data statistics result table is generated.

[0044] Preferably, step 205 involves deleting duplicate matching records from the statistical table in .dbf format generated in step 204. Specifically, this refers to deleting duplicate matching records from the target surface feature number q. i Sum of neighboring face element numbers q j To perform discriminant calculations, add a field named "uniq" to the statistics table in .dbf format, assign values ​​to the "uniq" field, and perform the discriminant calculations as follows:

[0045] If q i j Then C = q j -q i

[0046] Otherwise, C = q​i -q j

[0047] When the uniq field value C > 0, it is determined that there are duplicate matching records in the statistics table in .dbf format.

[0048] Where: q i Representative target surface element x i The number, q j Represents adjacent face element x j The number, where C represents the uniq field value;

[0049] Finally, delete rows in the target data statistics table that have duplicate uniq field values, resulting in a target statistics table in .dbf format.

[0050] Preferably, step 3 involves obtaining the average elevation of the target surface features and the average elevation of adjacent surface features from the target result table, performing discrimination calculations, and obtaining the upper and lower reservoir statistics table of the pumped storage power station and the upper and lower reservoir positions of the pumped storage power station. This refers to adding a field `result` to the target data statistics table after the elevation difference and distance-to-elevation ratio were assigned in step 209. This is done by adjusting the average elevation of the target surface features. Average elevation of adjacent surface features Perform a discriminant calculation and assign a value to the result field. The discriminant calculation is as follows:

[0051] like and Then D = 1

[0052] Otherwise, D = 0

[0053] in: For element x i Average elevation, For element x j The average elevation, D represents the value of the result field; t is a value taken based on experience.

[0054] Filter out the records with a result value of 1 and generate a statistical table of the upper and lower reservoirs of the target pumped storage power station.

[0055] Preferably, the generated statistical table of the upper and lower reservoirs of the target pumped storage power station is connected with the reservoir surface feature attribute table processed in step 202 by feature number OBJECTID, and the final selected locations of the upper and lower reservoirs of the target pumped storage power station are displayed on the geographic information system (GIS) mapping platform.

[0056] This invention provides a GIS-based site selection method for pumped storage power stations, solving the problems of traditional methods that use provinces (autonomous regions) as site selection units, suffer from low comprehensiveness and accuracy of manual visual judgment, face difficulties and certain dangers in field surveys, and are costly in terms of manpower and resources. This invention employs a statistical method for undulation to obtain raster data of undulation; obtains n potential reservoir surfaces; calculates the distance between each feature in the reservoir surface feature layer and its neighboring features, obtaining a statistical table in .dbf format; and obtains a target result table of upper and lower reservoir combinations by discriminative calculation of elevation difference and distance-to-elevation ratio. The average elevation of the target surface features is then obtained from the target result table. Average elevation of adjacent surface features By performing discrimination calculations, statistical tables of the upper and lower reservoirs of pumped storage power stations and their locations were obtained. This optimized the site selection process for pumped storage power stations, which can greatly reduce the manpower and material resources required for site selection, improve site selection efficiency, reduce the incompleteness and inaccuracy of manual identification, indirectly reduce the danger of field surveys, and meet the needs for accurate and rapid site selection of pumped storage power stations in large areas. Attached Figure Description

[0057] Figure 1 This is a schematic diagram of an embodiment of a GIS-based site selection method for pumped storage power stations;

[0058] Figure 2 This is a schematic flowchart of an embodiment of a GIS-based method for selecting pumped storage power stations that meet reservoir formation requirements.

[0059] Figure 3 This is a schematic flowchart of an embodiment of a GIS-based method for selecting pumped storage power stations that meet the requirements of upper and lower reservoir pairings.

[0060] Figure 4 This is a schematic diagram illustrating an embodiment of a GIS-based method for selecting pumped storage power stations using nearest neighbor analysis and discrimination calculation.

[0061] Figure 5 This is a schematic diagram of an embodiment of a GIS-based method for determining the elevation difference and distance-to-elevation ratio in the site selection of pumped storage power stations.

[0062] Figure 6 This is a schematic flowchart of an embodiment of a GIS-based method for selecting pumped storage power stations that meet altitude requirements.

[0063] Figure 7 This is a statistical example of the undulation of the area within the center radius of a GIS-based pumped storage power station site selection method, as shown in the diagram.

[0064] Figure 8This is a GIS-based site selection method for pumped storage power stations. Step 104 generates a result map of all potential reservoir surface examples that meet the reservoir formation requirements.

[0065] Figure 9 This is a result diagram of an embodiment of a GIS-based method for selecting the location of pumped storage power stations, connected by element number OBJECTID.

[0066] Figure 10 This is a GIS-based method for selecting the location of pumped storage power stations. The result of the NEAR_DIST example calculation is shown in the figure.

[0067] Figure 11 This is a result diagram of an embodiment of a GIS-based site selection method for pumped storage power stations, which involves deleting duplicate matching records.

[0068] Figure 12 This is a result diagram of an embodiment of a GIS-based site selection method for pumped storage power stations, which selects the combination of upper and lower reservoirs.

[0069] Figure 13 This is a flowchart of a GIS-based site selection method for pumped storage power stations. Detailed Implementation

[0070] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention.

[0071] This invention provides a GIS-based site selection method for pumped storage power stations, such as... Figure 13 As shown, the method includes the following steps:

[0072] Step 1: Obtain raster data of undulation from the DEM topographic map using statistical methods; process the raster data of undulation to obtain n potential reservoir surfaces;

[0073] Step 2: Obtain the average elevation within each feature of the n potential reservoir surfaces to obtain a statistical data table. Calculate the distance between each feature in the reservoir surface feature layer and its neighboring features to obtain a statistical table in the format of .dbf in the database file. Process the .dbf format statistical table and obtain the target result table of the upper and lower reservoir combination by judging and calculating the elevation difference and distance-to-elevation ratio.

[0074] Step 3: Obtain the average elevation of the target surface features from the target results table. Average elevation of adjacent surface features By performing discrimination calculations, we obtained the statistical table of the upper and lower reservoirs of the pumped storage power station and the location of the upper and lower reservoirs of the pumped storage power station.

[0075] In one embodiment, step 1 involves obtaining raster data of undulation from the DEM topographic map using statistical methods; processing the raster data of undulation to obtain n potential reservoir surfaces, and further includes the following steps:

[0076] Step 101: Calculate the undulation within a radius of R meters centered on each grid point of the DEM topographic map. Calculate the grid data of topographic undulation by the cell value. The R value is set according to experience.

[0077] Here's an example: R is 253m;

[0078] Step 102: Extract grid points with undulation less than the preset value K from the undulation grid data generated in Step 101; select grid cells that meet the conditions to generate target grid data, wherein the preset value K is determined based on empirical values.

[0079] Here's an example: K is 100m;

[0080] Step 103: Convert the target raster data extracted in Step 102 into vector polygon features;

[0081] Step 104: Create buffer polygons at an outward distance of R meters for the surface features generated in step 103 to obtain all potential library surfaces that meet the library requirements.

[0082] In one embodiment, step 101 further includes obtaining the DEM topographic map through remote sensing aerial surveying, where the raster cell values ​​represent the average elevation values ​​of the corresponding land surface; calculating the cell elevation range within a circular area with a radius of R meters around each raster cell using a statistical method of topographic relief; and generating raster data with cell values ​​representing topographic relief by calculating the difference between the maximum and minimum cell elevations within the circular area: the formula for calculating topographic relief S is as follows:

[0083] S = H max -H min

[0084] Among them, H max H min These are the maximum and minimum elevation values ​​within the analysis range, respectively.

[0085] In one embodiment, step 2 further includes the following steps:

[0086] Step 201: Calculate the average elevation within each surface feature generated in Step 1, and the statistical results are presented in a data table; the template for calculating the average elevation of the generated surface features defines a field named MEAN, and the MEAN field is applicable to the average elevation of all surface features;

[0087] Step 202: Connect the statistical data table generated in step 201 with the attribute table of the database features generated in step 104 according to the feature number OBJECTID to generate a connection table; the connection table is attached to the attribute table of the database feature layer, and the associated data can be accessed through the selected features or records in the layer or statistical data table to form the target database feature attribute table.

[0088] Step 203: Convert the target surface feature attribute table processed in Step 202 into a .dbf format file;

[0089] Step 204: Using the target database feature attribute table (.dbf format file) generated in Step 203, calculate the distance L between each feature in the database feature layer and its neighboring features using the distance calculation method. p The distance refers to determining the distance between each feature in the input features and a specified number of neighboring features within the search radius, and outputting the results as a statistical table in .dbf format; wherein, the search radius and the specified number of neighboring features are preset based on experience;

[0090] Step 205: Delete duplicate matching records from the statistical table generated in step 204;

[0091] In the .dbf format statistical table, the target polygon feature number q is used. i and adjacent face feature number q j Perform a discrimination calculation, define the discrimination calculation formula template as a uniq field, and assign values ​​to the uniq field;

[0092] If the target polygon feature number is less than the adjacent polygon feature number:

[0093] If the judgment result is yes, the uniq field value = adjacent face feature number q j -Target surface element number q i ;

[0094] If the judgment result is negative, the uniq field value = target polygon feature number q. i - Adjacent face feature number q j ;

[0095] If the uniq field values ​​of two records are the same, the result is that there are two duplicate matching records. If duplicate matching records are found in the .dbf format statistics table, the duplicate matching records are deleted to obtain the target statistics table in .dbf format.

[0096] Step 206: Add the average elevation of the target surface features to the target statistics table in .dbf format after step 205. (IN_Z), Average elevation of adjacent surface features (NEAR_Z), Elevation difference ΔH(x) i ,x j (gaocha), Distance-to-height ratio ΔV(x) i ,x j The target data statistics table is formed by using four calculation formula templates corresponding to the four fields (jugaobi).

[0097] Step 207: Assign the average elevation value of the surface feature to the average elevation field of the target surface feature. (IN_Z);

[0098] Convert the target data statistics table processed in step 206 and the database surface feature attribute table generated in step 203 into .dbf format files, and sort them by target surface feature number q. i After connecting (IN_FID), assign the MEAN field of the surface feature generated in step 201 to the target surface feature's average elevation field. (IN_Z), then delete the assigned MEAN field;

[0099] Step 208: Assign the average elevation value of adjacent face features to the average elevation field of adjacent face features. (NEAR_Z); Combine the nearest neighbor analysis statistics table processed in step 206 with the average elevation of the surface features generated in step 203, categorized by the nearest surface feature number q. j After connecting (NEAR_FID), the MEAN field of the polygon feature generated in step 201 is assigned to the current neighboring feature's average elevation field. (NEAR_Z), then delete the assigned MEAN field;

[0100] Step 209: Based on the average elevation of the target surface elements Average elevation of adjacent surface elements Calculate the elevation difference ΔH(x) i ,x j (gaocha field) and distance-to-height ratio ΔV(x) i ,x j (jugaobi field); This yields a statistical table of target data with assigned values ​​for elevation difference and distance-to-elevation ratio;

[0101] Step 210: Select upper and lower warehouse combinations that meet the requirements of upper and lower warehouse pairing based on preset conditions.

[0102] In one embodiment, obtaining the target data statistics table with assigned values ​​for elevation difference and distance-to-elevation ratio further includes the following steps:

[0103] Step 301: First, calculate the elevation difference ΔH(x) i ,x jThe (gaocha field) is used for calculation and assignment. The calculation formula is:

[0104]

[0105] Where: ΔH(x) i ,x j The ) represents the elevation difference (gaocha field). For element x i Average elevation, For element x j Average elevation;

[0106] Step 302, then adjust the distance-to-height ratio ΔV(x) i ,x j The (jugaobi field) is used for calculation and assignment. The calculation formula is:

[0107]

[0108] Where: x i xj represents a feature of each face feature, and xj represents a feature of a neighboring face feature; ΔV(x i ,x j ) represents the distance-to-height ratio (jugaobi), L p (x i ,x j ) represents the distance (NEAR_DIST) between each feature and its neighboring features, ΔH(x) i ,x j ) represents the elevation difference (gaocha); the target data statistics table with the elevation difference and distance-to-elevation ratio respectively is obtained.

[0109] In one embodiment, the distance calculation method in step 204 refers to nearest neighbor data processing, where distance L... p The calculation formula is:

[0110]

[0111] in,

[0112] If no search radius value is specified, all neighboring features will be considered in the calculation, which will result in a large computational load; if no maximum number of nearest-neighbor matches is specified, the computation will also be large.

[0113] In one embodiment, the conditional filtering method in step 210 is as follows: Add a field `zonghefen` to the target data statistics table after assigning values ​​to the height difference and distance-to-height ratio in step 209, and then filter the height difference ΔH(x) by... i ,x j(gaocha) and distance-to-height ratio ΔV(x) i ,x j The discriminant calculation is performed using (jugaobi), and a value is assigned to the zonghefen field. The discriminant calculation conditions are as follows:

[0114] If the condition M1 < ΔV(x) is satisfied i ,x j If )≤M2, then further determine that the elevation difference h1≤ΔH(x) i ,x j )≤h2;

[0115] If the elevation difference result meets the condition, the zonghefen field value is 1; otherwise, the zonghefen field value is 0.

[0116] If the condition M1 < ΔV(x) is not met i ,x j If M1 ≤ M2, then the value of the zonghefen field is 0; where M1 and M2 are determined based on experience; h1 and h2 are also determined based on experience.

[0117] Here are some examples: M1 is 2, M2 is 10; h1 is 300m, h2 is 700m;

[0118] If condition 2 < ΔV(x) is satisfied i ,x j If )≤10, then further determine that the elevation difference 300m≤ΔH(x) i ,x j ≤700m;

[0119] If the elevation difference result meets the condition, the zonghefen field value is 1; otherwise, the zonghefen field value is 0.

[0120] If condition 2 < ΔV(x) is not satisfied i ,x j If )≤10, then the value of the zonghefen field = 0;

[0121] Finally, the rows with a total value of 1 are selected, and the final target data statistics result table is generated.

[0122] In one embodiment, step 205 deletes duplicate matching records from the .dbf format statistical table generated in step 204. Specifically, this means that in the results of the nearest neighbor analysis, feature A matches target polygon feature B, and target polygon feature B also matches neighboring polygon feature A, resulting in duplicates. Therefore, duplicate record rows need to be deleted. A field uniq is added to the table, representing the current polygon feature number q. i (IN_FID) and q are the feature numbers of adjacent faces. jThe (NEAR_FID) field is used for discrimination calculation, and a value is assigned to the uniq field. The calculation for determining whether a record is duplicated is as follows:

[0123] If q i j Then C = q j -q i

[0124] Otherwise, C = q i -q j

[0125] When the uniq field value C > 0, it is determined that there are duplicate matching records in the statistics table in .dbf format.

[0126] Where: q i Representative target surface element x i The number (IN_FID), q j Represents adjacent face element x j The number (NEAR_FID), where C represents the uniq field value;

[0127] Finally, delete rows in the target data statistics table that have duplicate uniq field values, resulting in a target statistics table in .dbf format.

[0128] In one embodiment, step 3 obtains the average elevation of the target surface features and the average elevation of adjacent surface features from the target result table, performs discrimination calculations, and obtains the upper and lower reservoir statistics table of the pumped storage power station and the upper and lower reservoir positions of the pumped storage power station. This refers to adding the field "result" to the target data statistics table after the elevation difference and distance-to-elevation ratio were assigned in step 209. This is done by adjusting the average elevation of the target surface features. (IN_Z) and average elevation of adjacent surface features The (NEAR_Z) value is used for discrimination calculation, and a value is assigned to the result field. The discrimination calculation is as follows:

[0129] like and Then D = 1

[0130] Otherwise, D = 0

[0131] Here's an example: t is 4500m;

[0132] like and Then D = 1

[0133] Otherwise, D = 0

[0134] in: For element x i Average elevation, For element x​j The average elevation, where D represents the value of the result field;

[0135]

[0136] Filter out the records with a result value of 1 and generate a statistical table of the upper and lower reservoirs of the target pumped storage power station.

[0137] In one embodiment, the generated target pumped storage power station upper and lower reservoir statistics table is connected to the reservoir surface feature attribute table processed in step 202 by feature number OBJECTID, and the finally selected target pumped storage power station upper and lower reservoir locations are displayed on the geographic information system GIS mapping platform.

[0138] This invention provides a GIS-based site selection method for pumped storage power stations, solving the problems of traditional methods that use provinces (autonomous regions) as site selection units, suffer from low comprehensiveness and accuracy of manual visual judgment, face difficulties and certain dangers in field surveys, and are costly and inefficient. This invention uses a statistical method of undulation to obtain raster data of undulation; obtains n potential reservoir surfaces; calculates the distance between each feature in the reservoir surface feature layer and its neighboring features, obtaining a statistical table in .dbf format; and obtains a target result table of upper and lower reservoir combinations by judging and calculating the elevation difference and distance-to-elevation ratio. The average elevation of the target surface features is then obtained from the target result table. Average elevation of adjacent surface features By performing discrimination calculations, statistical tables of the upper and lower reservoirs of pumped storage power stations and their locations were obtained. This optimized the site selection process for pumped storage power stations, which can greatly reduce the manpower and material resources required for site selection, improve site selection efficiency, reduce the incompleteness and inaccuracy of manual identification, indirectly reduce the danger of field surveys, and meet the needs for accurate and rapid site selection of pumped storage power stations in large areas.

[0139] Example 1

[0140] This invention proposes a GIS-based method for selecting pumped storage power station sites. This method utilizes topographic elevation and location information, employing GIS information technology to perform a series of geospatial and attribute analyses to select upper and lower reservoir sites that meet a series of limiting conditions. In this method, the threshold values ​​for parameters such as reservoir area, elevation difference between the highest and lowest points of the reservoir area, elevation difference between the upper and lower reservoirs, the ratio of the distance between the upper and lower reservoirs to their respective elevations, and the reservoir area's altitude are reasonable empirical values ​​derived from extensive practical experience.

[0141] The basic attributes of a pumped storage power station site include: latitude and longitude coordinates, average elevation, topographic relief (elevation difference between the highest and lowest points in the reservoir area), reservoir area, elevation difference between the upper and lower reservoirs, distance between the upper and lower reservoirs, and the ratio of horizontal distance to elevation difference between the upper and lower reservoirs.

[0142] This invention provides a GIS-based site selection method for pumped storage power stations, such as... Figure 1 As shown, it includes the following steps:

[0143] Step 1: Select a reservoir from the topographic map that meets the requirements for reservoir formation (reservoir area ≥ 200,000 m²). 2 And points where the elevation difference between the highest and lowest points in the reservoir area is ≤100m, such as Figure 2 As shown, it includes the following steps:

[0144] Step 101: Calculate the undulation within a radius of 253m centered on each grid point of the topographic map (DEM); for example... Figure 7 As shown; in practical engineering, topographic maps (DEMs) can be obtained through remote sensing aerial surveying, with raster cell values ​​representing the average elevation of the corresponding land surface. The statistical method for topographic relief is focal point statistics. The principle of focal point statistics is to calculate the elevation range (difference between the maximum and minimum values) of cells within a circular area with a radius of 253m around each raster cell. This calculation generates raster data with cell values ​​representing topographic relief. The formula for calculating topographic relief S is:

[0145] S = H max -H min

[0146] Among them, H max H min These are the maximum and minimum elevation values ​​within the analysis range, respectively.

[0147] Step 102: Extract grid points with an undulation of less than 100m from the grid generated in Step 101;

[0148] In practical engineering, the extraction method is to filter raster cells that meet the condition based on a raster cell value ≤ 100m, and then select the raster cells that meet the condition to generate new raster data.

[0149] Step 103: Convert the raster data extracted in step 102 into vector polygon features;

[0150] Step 104: Create a buffer polygon 253m outward from the surface features generated in Step 103, thus obtaining all potential library surfaces that meet the library requirements; for example... Figure 8 The diagram shows the generated results of all potential storage surfaces that meet the requirements for forming a storage system.

[0151] The principle of creating buffer polygons is as follows: for a class of vector geographic features, polygonal entities with a certain range are formed around this group of features according to the set distance conditions.

[0152] Step 2: From the results obtained in Step 1, select points that meet the requirements for upper and lower storage group pairing (300m ≤ height difference between upper and lower storage ≤ 700m and 2 < height ratio between upper and lower storage ≤ 10), such as... Figure 3 As shown, it includes the following steps:

[0153] Step 201: Calculate the average elevation within each reservoir surface element generated in Step 1; the statistical results are presented in a data table.

[0154] Step 202: Connect the statistical table generated in Step 201 with the database feature attribute table generated in Step 1 by feature ID (OBJECTID); the OBJECTID field is consecutive and starts from ID 0 in the geodatabase data; for example... Figure 9 As shown.

[0155] Step 203: Convert the database surface feature attribute table processed in step 202 into a .dbf file;

[0156] Step 204: Calculate the distance NEAR_DIST between each feature in the database feature layer and its neighboring features using the .dbf file generated in step 203; for example... Figure 10 As shown.

[0157] The distance calculation method is nearest neighbor analysis. The principle of nearest neighbor analysis is to calculate the distance between each point in one feature class and the nearest point or line feature in another feature class. In practical engineering, within the search radius (if no search radius is specified, all neighboring features will be considered in the calculation, resulting in a large computational load), the distance between each feature in the input features and a specified number of nearest neighbor features (if no maximum number of nearest neighbor matches is specified, the computational load will be large). The results are output as a statistical table in .dbf format.

[0158] Distance L p The calculation formula is:

[0159]

[0160] in,

[0161] Step 205: Delete duplicate matching records from the statistical table generated in step 204; for example... Figure 11 As shown,

[0162] In the nearest neighbor analysis results, feature A matched B, and B also matched A, resulting in duplicates. Therefore, it's necessary to delete these duplicate records. In practical engineering, a `uniq` field is added to the table. Then, a value is assigned to the `uniq` field by calculating the difference between the current face feature ID (IN_FID) and the neighboring face feature ID (NEAR_FID). The calculation process is described below. Figure 4Finally, delete rows in the table with the same uniq field value.

[0163] A field is a smaller unit than a record. A set of fields makes up a record. Each field describes a certain feature of the document, i.e., a data item, and has a unique field identifier that can be recognized by a computer.

[0164] If q i j Then C = q j -q i

[0165] Otherwise, C = q i -q j

[0166] Where: q i Represents the current face element x i The number (IN_FID), q j Represents adjacent face element x j The number (NEAR_FID) is used, where C represents the uniq field value.

[0167] Step 206: Add four fields to the statistical table processed in step 205: average elevation of current face features IN_Z, average elevation of neighboring face features NEAR_Z, elevation difference gaocha, and distance-to-elevation ratio jugaobi.

[0168] Step 207: Assign the average elevation value of the polygon feature to the IN_Z field of the current polygon feature;

[0169] In a specific engineering project, after the statistical table processed in step 206 is connected with the average elevation of the polygon features .dbf file generated in step 203 according to the current polygon feature number IN_FID, the value of the average elevation of the polygon features field MEAN is assigned to the average elevation of the current polygon features field IN_Z, and then the assigned MEAN field is deleted.

[0170] Step 208: Assign the average elevation value of adjacent face features to the NEAR_Z field of adjacent face feature average elevation;

[0171] In actual engineering projects, after connecting the nearest neighbor analysis statistics table processed in step 206 with the average elevation of the polygon features .dbf file generated in step 203 according to the neighboring polygon feature number NEAR_FID, the average elevation of the polygon features field value MEAN is assigned to the current average elevation of the neighboring features field NEAR_Z, and then the assigned MEAN field is deleted.

[0172] Step 209: Based on the current face elements and adjacent face elements Calculate the elevation difference ΔH(x) using the average elevation.​i ,x j (gaocha) and distance-to-height ratio ΔV(x) i ,x j (jugaobi);

[0173] In practical engineering, first, ΔH(x) i ,x j The gaocha field is used for calculation and assignment. The calculation formula is:

[0174]

[0175] Where: ΔH(x) i ,x j ) represents elevation difference (gaocha), For the current face element x i Average elevation, For adjacent face elements x j The average elevation.

[0176] Next, calculate and assign a value to the jugaobi field. The calculation formula is:

[0177]

[0178] Where: ΔV(x) i ,x j ) represents the distance-to-height ratio (jugaobi), L p (x i ,x j ) represents the distance (NEAR_DIST) between each feature and its neighboring features, ΔH(x) i ,x j ) represents elevation difference (gaocha);

[0179] Step 210: Based on the conditions of 300m ≤ height difference ≤ 700m and 2 < distance-to-height ratio ≤ 10, select upper and lower storage combinations that meet the requirements for upper and lower storage group pairing; such as Figure 12 As shown;

[0180] In practical engineering, the screening method is as follows: Add a field `zonghefen` to the result table after processing in step 209, and then filter the results based on the height difference ΔH(x). i ,x j (gaocha) and distance-to-height ratio ΔV(x) i ,x j (jugaobi) performs a discrimination calculation and assigns a value to the zonghefen field. The discrimination calculation process is as follows: Figure 5 .

[0181] If condition 2 < ΔV(x) is satisfied i ,x j If )≤10, then further determine that the elevation difference 300m≤ΔH(x) i ,x j ≤700m;

[0182] If the elevation difference result meets the condition, the zonghefen field value is 1; otherwise, the zonghefen field value is 0.

[0183] If condition 2 < ΔV(x) is not satisfied i ,x j If )≤10, then the value of the zonghefen field = 0;

[0184] Finally, the rows with a total value of 1 are selected, and a new result table is generated.

[0185] Step 3: Select points from the results obtained in Step 2 that meet the altitude requirements (reservoir area altitude ≤ 4500m);

[0186] In practical engineering, the selection method is as follows: add a field named "result" to the result table after processing in step 209, and then calculate the average elevation of the current surface features. (IN_Z) and average elevation of adjacent surface features Perform a discrimination calculation on (NEAR_Z) and assign a value to the result field. See the discrimination calculation process below. Figure 6 As shown.

[0187] Determine the average elevation of the current surface elements If the judgment result is that the condition is not met, then D(result field value) = 0;

[0188] If the judgment result is that the conditions are met, further determine the average elevation of the adjacent surface features. If the judgment result meets the condition, then D(result field value) = 1; otherwise, D(result field value) = 0.

[0189] in: For element x i Average elevation, For element x j The average elevation, where D represents the value of the result field;

[0190] Finally, the records with a result value of 1 are selected to generate a statistical table of the upper and lower reservoirs of the pumped storage power station that meets all the requirements.

[0191] Connect the statistical table generated in step 3 with the reservoir surface feature attribute table processed in step 202 by feature number OBJECTID, as shown in Table 1. Then, the final selected locations of the upper and lower reservoirs of the pumped storage power station that meet all the requirements can be displayed on the geographic information system (GIS) mapping platform.

[0192] Table 1. Results of Step 3 (Selecting points that meet the altitude requirements from the results obtained in Step 2)

[0193]

[0194] This invention provides a GIS-based site selection method for pumped storage power stations, solving the problems of traditional methods that use provinces (autonomous regions) as site selection units, suffer from low comprehensiveness and accuracy of manual visual judgment, face difficulties and dangers in field surveys, and are costly and inefficient. This invention uses a statistical method of undulation to obtain raster data of undulation; obtains n potential reservoir surfaces; calculates the distance between each feature in the reservoir surface feature layer and its neighboring features, obtaining a statistical table in DBF format; and obtains a target result table of upper and lower reservoir combinations by judging and calculating the height difference and distance-to-height ratio. The average elevation of the target surface features is then obtained from the target result table. Average elevation of adjacent surface features By performing discrimination calculations, statistical tables of the upper and lower reservoirs of pumped storage power stations and their locations were obtained. This optimized the site selection process for pumped storage power stations, which can greatly reduce the manpower and material resources required for site selection, improve site selection efficiency, reduce the incompleteness and inaccuracy of manual identification, indirectly reduce the danger of field surveys, and meet the needs for accurate and rapid site selection of pumped storage power stations in large areas.

[0195] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principle of the present invention, including changes to the threshold values ​​of parameters such as reservoir area, the difference between the highest and lowest points of the reservoir area, the difference between the upper and lower reservoirs, the ratio of the distance between the upper and lower reservoirs, and the altitude of the reservoir area. These improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A site selection method for pumped storage power stations based on GIS, characterized in that, The method includes the following steps: Step 1: Obtain raster data of undulation from the DEM topographic map using statistical methods; process the raster data of undulation to obtain n potential reservoir surfaces; The specific steps are as follows: Step 101: Calculate the undulation within a radius of R meters centered on each grid point of the DEM topographic map. Calculate the grid data of topographic undulation by the cell value. The R value is set according to experience. Step 102: Extract grid points with undulation less than the preset value K from the undulation grid data generated in Step 101; select grid cells that meet the conditions to generate target grid data, wherein the preset value K is determined based on empirical values. Step 103: Convert the target raster data extracted in Step 102 into vector polygon features; Step 104: Create buffer polygons at an outward distance of R meters for the surface features generated in step 103 to obtain all potential library surfaces that meet the library requirements; Step 2: Obtain the average elevation within each feature of the n potential reservoir surfaces to obtain a statistical data table. Calculate the distance between each feature in the reservoir surface feature layer and its neighboring features to obtain a statistical table in .dbf format. Process the .dbf format statistical table, and calculate the elevation difference and distance-to-elevation ratio to obtain the target result table for the upper and lower reservoir combination. The specific steps are as follows: Step 201: Calculate the average elevation within each surface feature generated in Step 1, and the statistical results are presented in a data table; the template for calculating the average elevation of the generated surface features defines a field named MEAN, and the MEAN field is applicable to the average elevation of all surface features; Step 202: Connect the statistical data table generated in step 201 with the attribute table of the database features generated in step 104 according to the feature number OBJECTID to generate a connection table; the connection table is attached to the attribute table of the database feature layer, and the associated data can be accessed through the selected features or records in the layer or statistical data table to form the target database feature attribute table. Step 203: Convert the target surface feature attribute table processed in Step 202 into a .dbf format file; Step 204: Using the target database feature attribute table (.dbf format file) generated in Step 203, calculate the distance L between each feature in the database feature layer and its neighboring features using the distance calculation method. p The distance refers to determining the distance between each feature in the input features and a specified number of neighboring features within the search radius, and outputting the results as a statistical table in .dbf format; wherein, the search radius and the specified number of neighboring features are preset based on experience; Step 205: Delete duplicate matching records from the statistical table generated in step 204; Step 206: Add the average elevation of the target surface features to the target statistics table in .dbf format after step 205. Average elevation of adjacent surface elements Elevation difference Distance to height ratio The target data statistics table is generated after using four calculation formula templates; x i x represents a feature for each face feature. j A feature representing a neighboring face feature; Step 207: Assign the average elevation value of the surface feature to the average elevation value of the target surface feature. ; Convert the target data statistics table processed in step 206 and the database surface feature attribute table generated in step 203 into .dbf format files, and sort them by target surface feature number q. i After connection, assign the MEAN field of the surface feature generated in step 201 to the target surface feature average elevation. Then delete the MEAN field that has been assigned a value; Step 208: Assign the average elevation value of adjacent surface features to the average elevation value of adjacent surface features. The nearest neighbor analysis statistics table processed in step 206 and the average elevation of the surface features generated in step 203.dbf file are then sorted by the neighboring surface feature number q. j After connecting, assign the MEAN field of the polygon feature generated in step 201 to the average elevation of the current neighboring feature. Then delete the MEAN field that has been assigned a value; Step 209: Based on the average elevation of the target surface elements Average elevation of adjacent surface elements Calculate elevation difference Sum of height and distance ; Obtain a statistical table of target data with assigned values ​​for elevation difference and distance-to-height ratio; Step 210: Select upper and lower warehouse combinations that meet the requirements of upper and lower warehouse pairing based on preset conditions; Step 3: Obtain the average elevation of the target surface features from the target results table. Average elevation of adjacent surface features By performing discrimination calculations, we obtained the statistical table of the upper and lower reservoirs of the pumped storage power station and the location of the upper and lower reservoirs of the pumped storage power station.

2. The site selection method for a GIS-based pumped storage power station according to claim 1, characterized in that, Step 101 also includes obtaining the DEM topographic map through remote sensing aerial survey processing, where the raster cell values ​​represent the average elevation values ​​of the corresponding land surface; calculating the cell elevation range within a circular area with a radius of R meters around each raster cell using statistical methods of topographic relief; and generating raster data with cell values ​​representing topographic relief S by calculating the difference between the maximum and minimum elevation values ​​of the cells within the circular area: the formula for calculating topographic relief S is as follows: Among them, H max H min These are the maximum and minimum elevation values ​​within the analysis range, respectively.

3. The site selection method for a GIS-based pumped storage power station according to claim 1, characterized in that, Obtaining the target data statistics table with assigned values ​​for elevation difference and distance-to-elevation ratio also includes the following steps: Step 301: First, determine the elevation difference. The calculation formula is: in: ; Step 302: Adjust the distance-to-height ratio The calculation formula is: in, This represents the distance between each feature and its neighboring features. Represents the elevation difference; obtain a statistical table of target data with assigned values ​​for elevation difference and distance-to-elevation ratio.

4. The site selection method for a GIS-based pumped storage power station according to claim 1, characterized in that, The distance calculation method in step 204 refers to nearest neighbor data processing, where distance L... p The calculation formula is: in, .

5. The site selection method for a GIS-based pumped storage power station according to claim 1, characterized in that, The conditional filtering method in step 210 is as follows: Add a field `zonghefen` to the target data statistics table after assigning values ​​to the height difference and distance-to-height ratio in step 209, and then filter the data based on the height difference... Sum of height and distance Perform a discrimination calculation and assign a value to the zonghefen field. The discrimination calculation conditions are as follows: If condition M1 < If M2 ≤ M2, then further determine the elevation difference h1 ≤ M2. ≤h2; If the elevation difference result meets the condition, the zonghefen field value is 1; otherwise, the zonghefen field value is 0. If condition M1 < is not met If M1 ≤ M2, then the value of the zonghefen field is 0; where M1 and M2 are determined based on experience; h1 and h2 are also determined based on experience. Finally, the rows with a total value of 1 are selected, and the final target data statistics result table is generated.

6. The site selection method for a GIS-based pumped storage power station according to claim 1, characterized in that, Step 205: Delete duplicate matching records from the statistical table in .dbf format generated in step 204. Specifically, this involves deleting duplicate matching records from the target polygon feature number q. i Sum of neighboring face element numbers q j To perform discriminant calculations, add a field named "uniq" to the statistics table in .dbf format, assign values ​​to the "uniq" field, and perform the discriminant calculations as follows: like ,but otherwise, When the uniq field value C > 0, it is determined that there are duplicate matching records in the statistics table in .dbf format. Where: q i Representative target surface elements x i The number, q j Representing adjacent face elements x j The number, where C represents the uniq field value; ; Finally, delete rows in the target data statistics table that have duplicate uniq field values, resulting in a target statistics table in .dbf format.

7. The site selection method for a GIS-based pumped storage power station according to claim 1, characterized in that, Step 3: Obtain the average elevation of the target surface features and the average elevation of adjacent surface features from the target result table, perform discrimination calculations, and obtain the upper and lower reservoir statistics table of the pumped storage power station. The location of the upper and lower reservoirs of the pumped storage power station refers to the elevation difference and distance-to-elevation ratio assigned in step 209. Add the field "result" to the target data statistics table, and calculate the average elevation of the target surface features. Average elevation of adjacent surface features Perform a discriminant calculation and assign a value to the result field. The discriminant calculation is as follows: like ,and Then D=1 Otherwise, D=0 Where: D represents the value of the result field, and t is a value determined based on experience; ； Filter out the records with a result value of 1 and generate a statistical table of the upper and lower reservoirs of the target pumped storage power station.

8. The site selection method for a GIS-based pumped storage power station according to claim 7, characterized in that, The generated statistical table of the upper and lower reservoirs of the target pumped storage power station is connected with the reservoir surface feature attribute table processed in step 202 by feature number OBJECTID, and the final selected locations of the upper and lower reservoirs of the target pumped storage power station are displayed on the geographic information system mapping platform.

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