Site selection method for photo-thermal power station

Abstract

The invention relates to a site selection method for a photo-thermal power station, and the method comprises the following steps: S1, simulating to select a target region on a map, and obtaining a topographic data set and an environment data set of the target region; s2, gridding the target area to obtain a plurality of grids; s3, extracting topographic data and environmental data of each grid according to the topographic data set and the environmental data set; s4, screening the plurality of grids by combining a plurality of preset threshold conditions according to the topographic data and the environmental data corresponding to each grid to obtain one or more candidate areas; s5, according to the topographic data and the environmental data of the grids contained in each candidate area, obtaining a comprehensive score of each candidate area; and S6, carrying out secondary site selection on the candidate region with the highest comprehensive score, and obtaining and outputting a site selection result. According to the site selection method of the photo-thermal power station, the optimal construction position of the photo-thermal power station can be obtained more scientifically.

Description

Technical Field

[0001] The present invention belongs to the technical field of site selection for a solar thermal power station, and in particular relates to a site selection method for a solar thermal power station. Background Art

[0002] In existing technologies, in order to select a site for a solar thermal power station, a professional energy assessment team is usually required to conduct field surveys and collect data, including information on topography, climate conditions, solar radiation intensity, and other aspects.

[0003] When collecting data, a lot of field visits and manual measurements are required, which is not only time-consuming and labor-intensive, but also costly. In addition, incomplete or inaccurate data collection may lead to deviations in site selection results and affect the final performance of the power plant. Summary of the invention

[0004] The main purpose of the present invention is to solve the defects existing in the site selection stage of the related technology.

[0005] In order to solve the above technical problems, the present invention provides a method for selecting a site for a solar thermal power station, which comprises the following steps:

[0006] S1: Select a target area on a map, and obtain a terrain dataset and an environment dataset of the target area;

[0007] S2: gridding the target area to obtain a plurality of grids;

[0008] S3: extracting terrain data and environmental data of each of the grids according to the terrain data set and the environmental data set;

[0009] S4: screening the plurality of grids according to the terrain data and environmental data corresponding to each of the grids and combining a plurality of preset threshold conditions to obtain one or more candidate areas;

[0010] S5: obtaining a comprehensive score of each candidate area according to the terrain data and environmental data of the grids contained in each candidate area;

[0011] S6: Perform secondary site selection on the candidate area with the highest comprehensive score, and obtain and output the site selection result.

[0012] Further, step S4 includes the following steps:

[0013] S41: according to the terrain data and environmental data corresponding to each of the grids, including qualified grids that simultaneously meet all threshold conditions among all the grids into a qualified group;

[0014] S43: Obtain one or more candidate regions according to the qualified group.

[0015] The terrain data includes elevation data, and the threshold condition includes a first threshold condition related to the elevation data; the threshold condition also includes at least one of a second threshold condition and a third threshold condition related to the elevation data; the threshold condition also includes a fourth threshold condition related to the slope; the terrain data includes distribution data of avoidance areas; the threshold condition includes a fifth threshold condition related to the distribution data of the avoidance areas; the environmental data includes solar radiation, and the threshold condition includes a sixth threshold condition related to the solar radiation; the environmental data includes meteorological data, and the threshold condition includes a seventh threshold condition related to the meteorological data;

[0016] Specifically, step S41 determines whether the first threshold condition is satisfied, including:

[0017] S411: according to the elevation data of each grid, combining formula b and formula c, obtain the elevation gradient of each grid;

[0018] The formula b and formula c are:

[0019]

[0020] Among them, i represents the pixel position of the grid in the horizontal direction, j represents the pixel position of the grid in the vertical direction, and z i,j represents the elevation of the grid at position (i, j), x and y represent the real space position of the grid in the horizontal and vertical directions respectively, Δx and Δy represent the spatial resolution in the x and y directions respectively, and Represents the elevation z i,j elevation gradients in the x and y directions;

[0021] S412: Determine whether the elevation gradient of each of the grids satisfies the first threshold condition.

[0022] Specifically, step S41 determines whether the second threshold condition and / or the third threshold condition is satisfied, including:

[0023] The step of determining whether the second threshold condition is met includes:

[0024] S413: Calculate the slope of each grid according to the elevation gradient of each grid; the slope θ is calculated according to formula d, and the formula d is:

[0025]

[0026] Among them, θ represents the slope;

[0027] S414: Determine whether the slope of each of the grids meets the second threshold condition;

[0028] The step of determining whether the third threshold condition is met includes:

[0029] S415: Calculate the slope direction of each grid according to the elevation gradient of each grid; the slope direction is calculated according to formula e, and the formula e is:

[0030]

[0031] Among them, A represents the slope direction, which means the angle with the north direction;

[0032] S416: Determine whether the slope direction of each of the grids satisfies the third threshold condition.

[0033] Specifically, step S41 determines whether the fourth threshold condition is satisfied, including:

[0034] S417: Divide all the grids into a plurality of grid groups, with a preset number of adjacent grids as one group; calculate the flatness coefficient of each grid group according to the slope of each grid group; the flatness coefficient is calculated according to formula f, and the formula f is:

[0035]

[0036] Where k represents the number of grids contained in a grid group. Represents the average slope of all grids contained in a grid group, x k is the slope of the kth grid, β represents the flatness of a grid group;

[0037] S418: Determine whether the flatness coefficient of each of the grid groups is less than a preset threshold, and determine the grids in the grid group whose flatness coefficient is less than the preset threshold as satisfying the fourth threshold condition.

[0038] Specifically, step S41 determines whether the sixth threshold condition is satisfied, including:

[0039] S421: Calculate the daily average solar radiation value of each grid according to the solar radiation amount of each grid; the daily average solar radiation value is calculated according to formula g, and the formula g is:

[0040]

[0041] Among them, H i represents the solar radiation in the ith hour of the day, and E represents the daily average of the solar radiation;

[0042] S422: Determine whether the daily average solar radiation value of each of the grids meets the sixth threshold condition.

[0043] Specifically, step S41 determines whether the seventh threshold condition is satisfied, including:

[0044] S423: Obtain and calculate the normalized value of the meteorological data of each grid according to the preset meteorological suitability range value; the normalized value is calculated according to formula h, and the formula h is:

[0045]

[0046] Among them, S' represents the normalized value of meteorological data, S represents the actual measured meteorological data, and S min and S max are the meteorological suitability range values, representing the minimum and maximum values ​​in the meteorological suitability range respectively;

[0047] S424: Determine whether the normalized value of the meteorological data of each of the grids satisfies the seventh threshold condition.

[0048] Secondly, step S5 is included after step S4, and step S5 includes the following sub-steps:

[0049] S51: Taking the optimal site selection of a CSP plant as a goal and based on multiple factors mapped to the terrain data and environmental data, construct a multi-factor comprehensive evaluation model;

[0050] S52: Obtaining relative importance scores of the multiple factors;

[0051] S53: Obtaining an evaluation score for each of the factors in each of the grids, wherein the evaluation score is made based on the terrain data and the environmental data;

[0052] S54: inputting the relative importance scores of the multiple factors and the evaluation scores of the factors in each grid into the multi-factor comprehensive evaluation model to obtain a comprehensive score of each grid;

[0053] S55: weighting the comprehensive scores of the grids belonging to the same candidate area according to the practical surface areas of the grids, and summing up the weighted comprehensive scores of the grids belonging to the same candidate area, so as to obtain a comprehensive score of each candidate area.

[0054] The step of constructing the multi-factor comprehensive evaluation model includes the following sub-steps:

[0055] S511: Construct a pairwise comparison matrix A according to the multiple factors, wherein the pairwise comparison matrix A is:

[0056]

[0057] Among them, a ij Represents the importance of factor i relative to factor j, i∈[1,n], j∈[1,n], n represents the total number of rows or columns of the matrix;

[0058] S512: normalizing the pairwise comparison matrix A to obtain a weight matrix w;

[0059] The weight matrix w is expressed as:

[0060]

[0061] S513: constructing a scoring matrix R based on the evaluation score as a framework, wherein one of the scoring matrices R corresponds to one of the candidate regions, and any of the scoring matrices R is expressed as:

[0062]

[0063] Among them, r ij Represents the evaluation score of the jth factor of the i-th grid in a candidate area, j∈[1,n];

[0064] S514: According to the weight matrix w and the rating matrix R, a multi-factor comprehensive evaluation model is established.

[0065] Step S6 is included after step S5, and step S6 includes the following sub-steps:

[0066] S61: Obtaining preset construction information of the CSP power station;

[0067] S62: Screening out one or more pre-selected groups that match the preset construction information from among the candidate areas with the highest comprehensive scores; each of the pre-selected groups includes one grid, or includes a plurality of connected grids;

[0068] S63: taking the location information of the pre-selected group with the highest total comprehensive score of the grid as the site selection result, and outputting the site selection result.

[0069] The present invention has the following beneficial effects: the site selection method of the solar thermal power station performs grid processing on the target area to be selected, and screens out qualified grids based on the terrain data set and the environmental data set, and then determines the site selection result in combination with the scores of the screened grids. It can be seen that the site selection method of the solar thermal power station of the present application can obtain the preferred construction location of the solar thermal power station more scientifically, and the obtained construction location can be more suitable for the construction of the solar thermal power station, avoiding a series of problems caused by artificial site selection. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] The present invention will be further described below with reference to the accompanying drawings and embodiments, in which:

[0071] Figure 1 It is a flow chart of a method for selecting a site for a solar thermal power station according to the present invention;

[0072] Figure 2 It is a general analysis diagram of a site selection method for a solar thermal power station according to the present invention;

[0073] Figure 3 It is an analysis flow chart of a site selection method for a solar thermal power station of the present invention;

[0074] Figure 4 Schematic diagram of annual solar radiation in a proposed region according to an embodiment of the present invention;

[0075] Figure 5 It is a candidate area in the embodiment of the present invention. DETAILED DESCRIPTION

[0076] In order to have a clearer understanding of the technical features, purposes and effects of the present invention, specific embodiments of the present invention are now described in detail with reference to the accompanying drawings.

[0077] It should be noted that the flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor do they have to be executed in the order described. For example, some operations / steps can be decomposed, while some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0078] The block diagrams shown in the accompanying drawings are only functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices. The present invention provides a method for selecting a site for a solar thermal power station, such as Figure 1 As shown, the site selection method of a CSP power station may include the following steps:

[0079] S1: Select a target area on the map and obtain a terrain dataset and an environmental dataset of the target area;

[0080] S2: gridding the target area to obtain multiple grids;

[0081] S3: extracting terrain data and environmental data of each grid according to the terrain data set and the environmental data set;

[0082] S4: screening multiple grids according to the terrain data and environmental data corresponding to each grid and combining several preset threshold conditions to obtain one or more candidate areas;

[0083] S5: Obtaining a comprehensive score for each candidate area according to the terrain data and environmental data of the grids contained in each candidate area;

[0084] S6: Perform secondary site selection on the candidate area with the highest comprehensive score, and obtain and output the site selection result.

[0085] It can be understood that in step S1, the target area can refer to Figure 4 The target area can be selected by drawing graphics (such as rectangles, polygons, etc.), by combining compound conditions (such as combining multiple conditions through logical operators such as AND, OR, IN), by attributes (such as selecting a specific area through field values ​​in the attribute table), or by entering longitude and latitude.

[0086] A terrain dataset is a dataset that describes the terrain features of the earth's surface; an environmental dataset is a dataset that describes and evaluates the status, changing trends, and potential impacts of the natural environment. Secondly, datasets can be obtained through government and public institutions, professional data platforms, academic research databases, GIS systems (Geographic Information System), traffic flow datasets, environmental assessment datasets, etc.

[0087] The terrain datasets include DEM (Digital Elevation Model) datasets, water resources datasets, land cover datasets, traffic network datasets, and environmental restricted area datasets; the environmental datasets include solar radiation datasets, meteorological datasets, etc.

[0088] In step S2, gridding the target area means evenly dividing the target area into grids, wherein the grids may be rectangles, squares, hexagons, triangles, irregular polygons, and the like.

[0089] In step S3, after gridding, the terrain data and environmental data corresponding to each grid are extracted according to the terrain data set and the environmental data set. The terrain data includes at least one of elevation data and distribution data of avoidance areas; the distribution data of avoidance areas referred to here may include at least one of spatial distribution data of water resources, spatial distribution data of environmental restriction areas, spatial distribution data of artificial surface areas (reference to the ground feature coverage data set), and spatial distribution data of traffic network. The terrain data may also include at least one of slope data, aspect data, and flatness data obtained according to the elevation data. The environmental data may include at least one of solar radiation data and climate data; the climate data referred to here may include at least one of temperature data, precipitation data, and wind speed data.

[0090] In step S4, since the terrain data and environmental data include various data, it is necessary to refer to these data to screen each grid to obtain grids that meet the conditions and piece them together into candidate areas that meet the conditions. Therefore, step S4 may specifically include the following steps:

[0091] S41: according to the terrain data and environmental data corresponding to each grid, the qualified grids that simultaneously meet all the threshold conditions among all the grids are included into the qualified group;

[0092] S42: Obtain one or more candidate regions according to the qualified group.

[0093] Further optionally, in step S41, data analysis may be performed on the terrain data and environmental data corresponding to each grid. Figure 2 The analysis category diagram is shown. For the specific analysis process, please refer to Figure 3 The analysis flow chart is shown.

[0094] Data analysis includes at least one of terrain analysis, natural resource analysis, regional compatibility analysis, and meteorological condition analysis. Terrain analysis includes elevation analysis, and terrain analysis also includes at least one of slope analysis, aspect analysis, and flatness analysis; natural resource analysis includes solar energy resource analysis; regional compatibility analysis includes at least one of environmental restriction area analysis, land feature coverage analysis, traffic network analysis, and water resource analysis; and meteorological analysis includes at least one of temperature analysis, precipitation analysis, and wind speed analysis.

[0095] Among them, elevation analysis refers to excluding grids whose elevation gradient does not meet the first threshold condition, because places where the elevation changes too quickly are not suitable for the site selection area of ​​a solar thermal power station; the slope analysis and aspect analysis are similar, and grids whose slopes do not meet the second / third threshold conditions are excluded; the flatness analysis is similar, and grids that do not meet the fourth threshold condition are excluded.

[0096] Environmental restriction area analysis refers to excluding the grids in the first spatial area along the extension of the environmental restriction area by the preset first distance, because the environmental restriction area is not suitable for the site selection area of ​​the solar thermal power station. The same is true for the ground feature coverage analysis, excluding the grids in the second spatial area along the extension of the artificial surface area by the preset second distance. The same is true for the traffic network analysis, excluding the grids in the third spatial area along the extension of the traffic network by the preset third distance. The same is true for the water resources analysis, excluding the grids in the fourth spatial area along the extension of the water resources by the preset fourth distance.

[0097] Solar resource analysis refers to excluding grids whose daily average solar radiation does not meet the sixth threshold condition, because places with insufficient solar radiation are not suitable for site selection of solar thermal power stations. Similarly, meteorological analysis excludes grids whose normalized values ​​of meteorological data do not meet the seventh threshold condition.

[0098] The steps of the above-mentioned various analyses are illustrated below: In some embodiments, the elevation analysis may include the following steps:

[0099] S411: according to the elevation data of each grid, the elevation gradient of each grid is obtained by combining formula b and formula c;

[0100] Formula b and formula c are:

[0101]

[0102] Among them, i represents the pixel position of the grid in the horizontal direction, j represents the pixel position of the grid in the vertical direction, and z i,j represents the elevation of the grid at position (i, j), x and y represent the real space position of the grid in the horizontal and vertical directions respectively, Δx and Δy represent the spatial resolution in the x and y directions respectively, and Represents the elevation z i,j The gradient of elevation in the x and y directions.

[0103] S412: Determine whether the elevation gradient of each grid satisfies a first threshold condition.

[0104] In some embodiments, the slope analysis may include the following steps:

[0105] S413: According to the elevation gradient of each grid, the slope of each grid is calculated; the slope θ is calculated according to formula d, and formula d is:

[0106]

[0107] Preferably, in order to ensure the overall power generation and land utilization rate, a suitable slope should be set according to different power generation technologies. Different second threshold conditions are set according to the type of CSP station. For example, if the CSP station is a trough or linear Fresnel CSP station, the second threshold condition may be a slope of less than 3%; if the CSP station is a tower CSP station, the second threshold condition may be a slope of less than 7%.

[0108] S414: Determine whether the slope of each grid satisfies a second threshold condition.

[0109] In some embodiments, aspect analysis may include the following steps:

[0110] S415: Calculate the slope direction of each grid according to the elevation gradient of each grid; the slope direction is calculated according to formula e, which is:

[0111]

[0112] A is the slope direction, which refers to the angle with respect to the north direction.

[0113] Preferably, in order to ensure the utilization rate of solar energy resources, appropriate slopes should be set according to different geographical locations. Different third threshold conditions are set according to different geographical locations. For example, in the northern hemisphere, a south-facing slope can ensure that the reflector and collector can receive good solar radiation most of the day, and a south-facing slope can be selected as the best slope; similarly, a north-facing slope can be selected as the best slope in the southern hemisphere.

[0114] S416: Determine whether the slope aspect of each grid satisfies a third threshold condition.

[0115] In some embodiments, the flatness analysis may include the following steps:

[0116] S417: Divide all grids into multiple grid groups with a preset number of adjacent grids as one group; calculate the flatness coefficient of each grid group according to the slope of each grid group;

[0117] The flatness coefficient is calculated according to formula f, which is:

[0118]

[0119] Where k represents the number of grids contained in a grid group. Represents the average slope of all grids contained in a grid group, x k is the slope of the kth grid, and β represents the flatness of a grid group.

[0120] S418: Determine whether the flatness coefficient of each mesh group is less than a preset threshold, and determine the meshes in the mesh group whose flatness coefficient is less than the preset threshold as meeting the fourth threshold condition.

[0121] In some embodiments, the regional compatibility analysis may include the following steps:

[0122] S419: Obtaining a buffer zone extending outward from the avoidance zone by a preset distance according to the distribution data of the avoidance zone;

[0123] S420: Determine whether each grid belongs to the buffer zone, and determine the grids outside the buffer zone as satisfying the fifth threshold condition.

[0124] It can be understood that the avoidance area may include at least one of an environmental restriction area, an artificial surface area, a traffic network area, and a water resource area.

[0125] Step S419 may specifically include:

[0126] According to the spatial distribution data of the environmental restriction area, a first spatial area extending along the environmental restriction area by a preset first distance is obtained;

[0127] and / or, obtaining, based on the distribution data of the artificial surface area, a second spatial area extending along the artificial surface area by a preset second distance;

[0128] and / or, obtaining a third spatial area extending along the traffic network area by a preset third distance according to the spatial distribution data of the traffic network area;

[0129] And / or, based on the spatial distribution data of the water resource area, a fourth spatial area extending along the water resource area by a preset fourth distance is obtained.

[0130] It can be understood that the environmental restriction area can be a restricted area such as a military restricted area, and the first distance can be set according to relevant regulations. The preset distance extended along the traffic network area can be an extended distance road boundary range of 100m-300m, preferably 150m, so as to eliminate the interference of roads on the site selection of the solar thermal power station. The boundary along the water-rich area is expanded outward by 50m-150m, preferably 100m, to more accurately reflect the actual water area impact range. In addition, when selecting the location of the solar thermal power generation project, the sustainable relationship between the solar thermal power generation project and other water needs is also evaluated. For example, agricultural, industrial and residential water use, to ensure that the project will not have a negative impact on other normal water use in the local area; at the same time, the appropriate cooling technology can be selected according to the specific environmental conditions to flexibly adjust the water consumption.

[0131] In some embodiments, the solar resource analysis may include the following steps:

[0132] S421: Calculate the daily average solar radiation value of each grid according to the solar radiation amount of each grid; the daily average solar radiation value is calculated according to formula g, which is:

[0133]

[0134] Among them, H i represents the radiation in the i-th hour of the day, and E represents the daily average solar radiation.

[0135] S422: Determine whether the daily average solar radiation value of each grid satisfies the sixth threshold condition.

[0136] In some embodiments, the meteorological analysis may include the following steps:

[0137] S423: Obtain and calculate the normalized value of the meteorological data of each grid according to the preset meteorological suitability range value; the normalized value is calculated according to formula h, and formula h is:

[0138]

[0139] Among them, S' represents the normalized value of meteorological data, S represents the actual measured meteorological data, and S min and S max are the meteorological suitability range values, representing the minimum and maximum values ​​in the meteorological suitability range respectively;

[0140] S424: Determine whether the normalized value of the meteorological data of each grid satisfies the seventh threshold condition.

[0141] The actual measured meteorological data S can be one of temperature, precipitation, and wind speed, and the others S', S max , S min These are all corresponding data for temperature / precipitation / wind speed.

[0142] In some embodiments, temperature analysis may include the following steps:

[0143] Obtain and calculate the normalized value of the temperature data of each grid according to the preset temperature suitability range value; the normalized value is calculated according to the formula h t Calculated, formula h t for:

[0144]

[0145] Where T represents the normalized value of temperature data, X T Represents the actual measured temperature data, T min and T max are the temperature suitability range values, representing the minimum and maximum values ​​in the temperature suitability range respectively.

[0146] In some embodiments, precipitation analysis may include the following steps:

[0147] Obtain and calculate the normalized value of precipitation data for each grid based on the preset precipitation suitability range value; the normalized value is calculated according to the formula h p Calculated, formula h p for:

[0148]

[0149] Among them, P represents the normalized value of precipitation data, XP Represents the actual measured precipitation data, p min and p max are the precipitation suitability range values, representing the minimum and maximum values ​​in the precipitation suitability range respectively.

[0150] Optionally, the precipitation suitability range value can be between 180mm-500mm.

[0151] In some embodiments, wind speed analysis may include the following steps:

[0152] Obtain and calculate the normalized value of the wind speed data of each grid according to the preset wind speed suitability range value; the normalized value is calculated according to the formula h v Calculated, formula h v for:

[0153]

[0154] Where W represents the normalized value of wind speed data, X V Represents the actual measured wind speed data, v min and v max are wind speed suitability range values, respectively representing the minimum and maximum values ​​in the wind speed suitability range. It can be understood that based on the seasonal changes in meteorological conditions and the impact of extreme weather events on CSP plants, areas with lower wind speeds can be selected as site selection areas. It can be seen that through the above analysis, one or more candidate areas can be screened on the proposed target area. For example, see Figure 5 ,in Figure 5 The areas marked with numbers are multiple candidate areas that have been screened out.

[0155] Continuing with step S5, in some embodiments, step S5 may include the following sub-steps:

[0156] S51: Taking the best CSP site selection as the goal and building a multi-factor comprehensive evaluation model based on multiple factors mapped with terrain data and environmental data;

[0157] S52: Obtaining relative importance scores of multiple factors relative to each other;

[0158] S53: Obtaining an evaluation score for each factor in each grid, where the evaluation score is made based on terrain data and environmental data;

[0159] S54: inputting the relative importance scores of the multiple factors and the evaluation scores of the various factors in each grid into the multi-factor comprehensive evaluation model to obtain a comprehensive score for each grid;

[0160] S55: weighting the comprehensive scores of the grids belonging to the same candidate area according to the practical surface areas of the grids, and summing up the weighted comprehensive scores of the grids belonging to the same candidate area, so as to obtain a comprehensive score of each candidate area.

[0161] In step S51, the multiple factors mapped between terrain data and environmental data can be understood as data types of terrain data and environmental data, and the factors can be elevation gradient, slope, aspect, flatness, solar energy resources, temperature, precipitation, wind speed, etc.

[0162] Then, in step S52, the relative importance score can be understood as being used to evaluate the relative importance of different factors during the operation of the photovoltaic thermal station. The relative importance score can refer to the historical fault data and historical operation data of other photovoltaic thermal stations that have been built to find the relative importance of each factor. The relative importance score can be obtained based on artificial formulation, and the relative importance score can be an integer from 1 to 9.

[0163] In step S53, each grid has corresponding terrain data and environmental data, such as the elevation gradient of the grid, the solar radiation, etc. Then, the evaluation score is the score given for the specific performance of the terrain and environment of each grid. Optionally, according to the preset preferred interval for different factors, the closer the terrain data / environmental data is to the preferred interval, the higher the evaluation score is, and when the terrain data / environmental data is in the preferred interval, the evaluation score is the highest. Optionally, the evaluation score for each factor in each grid can be directly given manually to obtain the evaluation score.

[0164] Looking at step S55 again, among the grids belonging to the same candidate area, there may be areas that are not suitable for construction. The reasons for not being suitable for construction may be soft soil, insufficient bearing capacity of the foundation, etc. There may also be situations where the edge of the candidate area only occupies a part of the grid, etc. Then at this time, the comprehensive scores of the grids belonging to the same candidate area can be weighted according to the surface area that can actually be used in the grid, that is, the practical surface area, to improve the accuracy of the candidate area score. The weighting rule is specifically that when the percentage of the practical surface area of ​​the grid to the area of ​​the entire grid is greater, the weight is greater, and vice versa. Subsequently, the weighted comprehensive scores of the grids belonging to the same candidate area can be summed up to obtain the comprehensive score of the corresponding candidate area.

[0165] Optionally, step S51 includes the following sub-steps:

[0166] S511: Construct a pairwise comparison matrix A based on multiple factors. The pairwise comparison matrix A is:

[0167]

[0168] Among them, a ij Represents the importance of factor i relative to factor j, i∈[1,n], j∈[1,n], n represents the total number of rows or columns of the matrix;

[0169] S512: normalize the pairwise comparison matrix A to obtain a weight matrix w;

[0170] The weight matrix w can be expressed as:

[0171]

[0172] In some embodiments, the normalization process may include the following steps:

[0173] According to the pairwise comparison matrix A, the normalized weights are obtained Normalized weights for:

[0174]

[0175] Normalize all weights Accumulate and get the sum of normalized weights for:

[0176]

[0177] According to the normalized weight and the sum of normalized weights The weight matrix w is calculated. The elements in the weight matrix w are calculated according to the formula q. The formula q is:

[0178]

[0179] Secondly, after step S512, step S51 further includes the following steps: S513: constructing a scoring matrix R based on the evaluation score as a framework, wherein one scoring matrix R corresponds to one candidate region, and any scoring matrix R is expressed as:

[0180]

[0181] Among them, r ij Represents the evaluation score of the jth factor of the i-th grid in a candidate area, j∈[1,n];

[0182] S514: Based on the weight matrix w and the rating matrix R, a multi-factor comprehensive evaluation model is established.

[0183] It can be understood that in step S513, in a scoring matrix R, each element corresponds to a scoring score of a grid for an element; the number of candidate regions is equal to the number of scoring matrices R. After the evaluation score of each factor in each grid is input into the multi-factor comprehensive evaluation model value, the evaluation score is filled into the scoring matrix R for subsequent calculations.

[0184] The following is an example of solar energy resources: According to GB / T 37526-2019 "Solar Energy Resource Assessment Method", the abundance of solar energy resources can be divided into four levels: the most abundant (A), very abundant (B), abundant (C), and general (D), forming a solar energy resource classification table. The solar energy resource classification table is:

[0185]

[0186] Based on this, the solar energy resource classification table can be used to obtain the evaluation score of each grid for solar energy resources. Similarly, other factors can be evaluated by referring to the corresponding classification table or by using relevant technologies, which is not limited here.

[0187] In step S514, after the weight matrix w and the score matrix R are obtained, a multi-factor comprehensive evaluation model can be obtained. The expression of the multi-factor comprehensive evaluation model can be expressed as:

[0188] S=w·R

[0189] Among them, S represents the matrix of comprehensive scores of grids in a single candidate area. It can be understood that since the weight matrix w and the score matrix R are both presented in the form of matrices, the calculated result S is also presented in the form of a matrix; in the matrix S, each element represents the comprehensive score of a grid.

[0190] In some embodiments, step S6 may include the following sub-steps:

[0191] S61: Obtaining preset construction information of the CSP power station;

[0192] S62: Among the candidate areas with the highest comprehensive scores, one or more pre-selected groups that match the preset construction information are screened out; each pre-selected group includes one grid, or includes multiple connected grids.

[0193] It is understandable that the preset information may include scale, shape, etc. For example, according to the scale, the number of grids required is determined; according to the building shape, the specific arrangement of the grids is determined. Then, among the candidate areas with the highest comprehensive scores, the grids that meet the grid number and arrangement are selected as the pre-selected group.

[0194] S63: The location information of the pre-selected group with the highest total comprehensive score of the grid is taken as the site selection result, and the site selection result is output.

[0195] It can be understood that the score of the pre-selected group can be understood as the total score of the comprehensive scores of the grids in the pre-selected group. Preferably, the score of the pre-selected group can be calculated by using the weighted comprehensive score of the grid to improve the accuracy of the score of the pre-selected group. Secondly, the location information can be presented in a box selection on the map, or specific latitude and longitude information can be output, which is not limited here.

[0196] It can be understood that the above embodiments only express the preferred implementation modes of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the patent scope of the present invention. It should be pointed out that, for ordinary technicians in this field, the above technical features can be freely combined without departing from the concept of the present invention, and several deformations and improvements can be made, which all belong to the protection scope of the present invention. Therefore, all equivalent changes and modifications made to the scope of the claims of the present invention should belong to the coverage of the claims of the present invention.

Claims

1. A method for selecting a site for a solar thermal power station, characterized in that: The method comprises the following steps: S1: Select a target area on a map, and obtain a terrain dataset and an environment dataset of the target area; S2: gridding the target area to obtain a plurality of grids; S3: extracting terrain data and environmental data of each of the grids according to the terrain data set and the environmental data set; S4: screening the plurality of grids according to the terrain data and environmental data corresponding to each of the grids and combining a plurality of preset threshold conditions to obtain one or more candidate areas; S5: obtaining a comprehensive score of each candidate area according to the terrain data and environmental data of the grids contained in each candidate area; S6: Perform secondary site selection on the candidate area with the highest comprehensive score, and obtain and output the site selection result.

2. The site selection method for a CSP power station according to claim 1, characterized in that: The step S4 comprises the following steps: S41: according to the terrain data and environmental data corresponding to each of the grids, including qualified grids that simultaneously meet all threshold conditions among all the grids into a qualified group; S43: Obtain one or more candidate regions according to the qualified group.

3. The site selection method for a CSP power station according to claim 2, characterized in that: The terrain data includes elevation data, and the threshold condition includes a first threshold condition associated with the elevation data; The step of determining whether the first threshold condition is met includes: S411: according to the elevation data of each grid, combining formula b and formula c, obtain the elevation gradient of each grid; The formula b and formula c are: Among them, i represents the pixel position of the grid in the horizontal direction, j represents the pixel position of the grid in the vertical direction, and z i,j represents the elevation of the grid at position (i, j), x and y represent the real space position of the grid in the horizontal and vertical directions respectively, Δx and Δy represent the spatial resolution in the x and y directions respectively, and Represents the elevation z i,j elevation gradients in the x and y directions; S412: Determine whether the elevation gradient of each of the grids satisfies the first threshold condition.

4. The site selection method for a CSP power station according to claim 3, characterized in that: The threshold condition further includes at least one of a second threshold condition and a third threshold condition related to the elevation data; The step of determining whether the second threshold condition is met includes: S413: Calculate the slope of each grid according to the elevation gradient of each grid; the slope θ is calculated according to formula d, and the formula d is: Among them, θ represents the slope; S414: Determine whether the slope of each of the grids meets the second threshold condition; The step of determining whether the third threshold condition is met includes: S415: Calculate the slope direction of each grid according to the elevation gradient of each grid; the slope direction is calculated according to formula e, and the formula e is: Among them, A represents the slope direction, which means the angle with the north direction; S416: Determine whether the slope direction of each of the grids satisfies the third threshold condition.

5. The site selection method for a CSP power station according to claim 4, characterized in that: The threshold conditions further include a fourth threshold condition related to the slope; The step of determining whether the fourth threshold condition is met includes: S417: Divide all the grids into a plurality of grid groups, with a preset number of adjacent grids as one group; calculate the flatness coefficient of each grid group according to the slope of each grid group; the flatness coefficient is calculated according to formula f, and the formula f is: Where k represents the number of grids contained in a grid group. Represents the average slope of all grids contained in a grid group, x k is the slope of the kth grid, β represents the flatness of a grid group; S418: Determine whether the flatness coefficient of each of the grid groups is less than a preset threshold, and determine the grids in the grid group whose flatness coefficient is less than the preset threshold as satisfying the fourth threshold condition.

6. The site selection method for a CSP power station according to claim 2, characterized in that: The environmental data includes solar radiation, and the threshold condition includes a sixth threshold condition related to the solar radiation; The step of determining whether the sixth threshold condition is met includes: S421: Calculate the daily average solar radiation value of each grid according to the solar radiation amount of each grid; the daily average solar radiation value is calculated according to formula g, and the formula g is: Among them, H i represents the solar radiation in the ith hour of the day, and E represents the daily average of the solar radiation; S422: Determine whether the daily average solar radiation value of each of the grids meets the sixth threshold condition.

7. The site selection method for a CSP power station according to claim 2, characterized in that: The environmental data includes meteorological data, and the threshold condition includes a seventh threshold condition related to the meteorological data; The step of determining whether the seventh threshold condition is met includes: S423: Obtain and calculate the normalized value of the meteorological data of each grid according to the preset meteorological suitability range value; the normalized value is calculated according to formula h, and the formula h is: Among them, S' represents the normalized value of meteorological data, S represents the actual measured meteorological data, and S min and S max are the meteorological suitability range values, representing the minimum and maximum values ​​in the meteorological suitability range respectively; S424: Determine whether the normalized value of the meteorological data of each of the grids satisfies the seventh threshold condition.

8. The site selection method for a CSP power station according to any one of claims 1 to 7, characterized in that: The step S5 comprises the following sub-steps: S51: Taking the optimal site selection of a CSP plant as a goal and based on multiple factors mapped to the terrain data and environmental data, construct a multi-factor comprehensive evaluation model; S52: Obtaining relative importance scores of the multiple factors; S53: Obtaining an evaluation score for each of the factors in each of the grids, wherein the evaluation score is made based on the terrain data and the environmental data; S54: inputting the relative importance scores of the multiple factors and the evaluation scores of the factors in each grid into the multi-factor comprehensive evaluation model to obtain a comprehensive score of each grid; S55: weighting the comprehensive scores of the grids belonging to the same candidate area according to the practical surface areas of the grids, and summing up the weighted comprehensive scores of the grids belonging to the same candidate area, so as to obtain a comprehensive score of each candidate area.

9. The site selection method for a CSP power station according to claim 8, characterized in that: The steps of constructing the multi-factor comprehensive evaluation model include: S511: Construct a pairwise comparison matrix A according to the multiple factors, wherein the pairwise comparison matrix A is: Among them, a ij Represents the importance of factor i relative to factor j, i∈[1,n], j∈[1,n], n represents the total number of rows or columns of the matrix; S512: normalizing the pairwise comparison matrix A to obtain a weight matrix w; The weight matrix w is expressed as: S513: constructing a scoring matrix R based on the evaluation score as a framework, wherein one of the scoring matrices R corresponds to one of the candidate regions, and any of the scoring matrices R is expressed as: Among them, r ij Represents the evaluation score of the jth factor of the i-th grid in a candidate area, j∈[1,n]; S514: According to the weight matrix w and the rating matrix R, a multi-factor comprehensive evaluation model is established.

10. The site selection method for a CSP power station according to claim 8, characterized in that: The step S6 comprises the following sub-steps: S61: Obtaining preset construction information of the CSP power station; S62: Screening out one or more pre-selected groups that match the preset construction information from among the candidate areas with the highest comprehensive scores; each of the pre-selected groups includes one grid, or includes a plurality of connected grids; S63: taking the location information of the pre-selected group with the highest total comprehensive score of the grid as the site selection result, and outputting the site selection result.

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