A photovoltaic array optimization design method under airspace constraints

Abstract

This invention relates to the field of photovoltaic technology and discloses a method for optimizing the design of photovoltaic arrays under airspace constraints. The steps include: constructing a predicted elevation model for the target area, screening feasible point sets for photovoltaic placement, extracting boundary contours and drawing new boundaries that meet airspace requirements, calculating the initial layout based on the horizontal line method, optimizing the layout using a genetic algorithm, and automatically drawing photovoltaic modules. This invention establishes a generalized automatic photovoltaic layout model for airports, which can fully utilize land resources, maximize power generation efficiency, and achieve optimal design of photovoltaic arrays under airspace constraints.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic technology, and in particular to a method for optimizing the design of photovoltaic arrays under airspace constraints. Background Technology

[0002] With the rapid development of new energy photovoltaic power generation technology, the construction of photovoltaic power stations is increasingly being integrated with airport construction. To fully utilize land resources and maximize power generation efficiency, airport photovoltaic projects are often located in irregular terrain areas such as airport slopes and open spaces. Clear airspace refers to the unobstructed space within a certain radius of the airport runway. Since photovoltaic modules typically require a certain installation height and area to obtain sufficient sunlight, the construction of photovoltaic systems in airport areas must fully consider airspace restrictions to avoid impacting aviation safety.

[0003] In actual airport photovoltaic (PV) layout design, it is necessary to draw the layout in CAD software based on topographic survey maps, PV module size parameters, and spacing parameters, taking into account the layout area. Current PV module designs in airport areas are mostly based on simple, regular geometric assumptions, making it difficult to adapt to complex terrain under airspace restrictions. Traditional manual layout or simple array layout methods have the following problems: First, they cannot accurately handle changes in terrain elevation, and some PV panels may be built in airspace-restricted areas, posing aviation safety hazards; second, they lack the ability to adapt to irregular boundaries, resulting in low site utilization and affecting power generation efficiency; third, PV module layout relies on manual experience and manual adjustments, lacking automated means for global optimization.

[0004] In industrial applications such as sheet metal cutting and glass cutting, the minimum horizontal line method is often used to solve layout problems and maximize material utilization. Maximizing the arrangement of photovoltaic modules within a limited area can also be viewed as a two-dimensional rectangular layout problem. However, the conventional minimum horizontal line method uses fixed parameters, making it difficult to achieve adaptive parameter updates to maximize space utilization.

[0005] In summary, a photovoltaic array optimization design method under airspace constraints can be proposed by combining the minimum horizontal line method with evolutionary algorithms, taking full account of airspace limitations. Summary of the Invention

[0006] This invention discloses a photovoltaic array optimization design method under airspace restriction conditions, and establishes a generalized automatic layout design model for airport photovoltaics that meets airspace restriction conditions. This method can make full use of land resources, maximize power generation efficiency, and achieve the optimal design of airport photovoltaics.

[0007] The technical implementation scheme of the present invention is as follows:

[0008] A method for optimizing the design of a photovoltaic array under airspace constraints includes the following steps:

[0009] S1. Elevation Model Construction: Obtain the target area survey data and original boundary polygon vertex data, generate a discrete grid point set based on the horizontal coordinate range of the boundary and the preset grid resolution, and construct a predicted elevation model through a scatter interpolation algorithm.

[0010] S2. Feasible point set screening: Based on the maximum allowable elevation preset by the clearance limit, screen out grid points whose elevation meets the constraints and are located within the original boundary as the feasible point set for photovoltaic layout;

[0011] S3. Boundary contour extraction: The feasible point set is processed using a geometric reconstruction algorithm to extract its boundary contour;

[0012] S4. New Boundary Drawing: Combine the boundary contour vertex data and the predicted elevation model to determine the vertex elevation, output the new boundary data and draw the new boundary polygon that meets the clearance requirements;

[0013] S5. Initial layout calculation: Using the new boundary polygon as a constraint, establish a photovoltaic layout model using the horizontal line method, and calculate the location and total number of photovoltaic modules that do not cross the boundary or overlap within the feasible area.

[0014] S6. Algorithm parameter settings: Encode the photovoltaic layout input parameters as chromosomes, define the fitness function for evaluating area coverage, and set the genetic algorithm running parameters;

[0015] S7. Layout optimization iteration: Initialize the genetic algorithm population and iterate, decode chromosomes, call the layout model to calculate the results, and output the position of the photovoltaic module with the highest fitness through selection, crossover and mutation operations;

[0016] S8. Automatic Component Drawing: The program reads the optimized positions of the photovoltaic modules and automatically draws the photovoltaic modules in the drawing software.

[0017] As a further description of the above technical solution, the geometric reconstruction algorithm in step S3 is the Alpha Shape algorithm, and the formula for calculating the radius parameter of the Alpha Shape algorithm is:

[0018] ;

[0019] In the formula, The grid resolution of the digital elevation model. This is the adjustment coefficient.

[0020] As a further description of the above technical solution, in step S4, the method for determining the elevation value of the vertex of the new boundary polygon is as follows: traverse each vertex on the new boundary polygon and calculate the Euclidean distance between it and the vertex in the original boundary polygon that meets the screening conditions; if the Euclidean distance is less than the preset tolerance, the elevation value of the corresponding vertex in the original boundary polygon is directly used; otherwise, the elevation value of the vertex is calculated using the predicted elevation model.

[0021] As a further description of the above technical solution, the specific implementation process of establishing the horizontal line method photovoltaic layout model in step S5 includes:

[0022] S5-1: Initialize the lowest horizontal line of the current component layout;

[0023] S5-2: Based on the new boundary polygon The intersection with the current horizontal line determines the effective layout interval;

[0024] S5-3: Within the effective range, place the photovoltaic modules according to the photovoltaic module size and lateral spacing parameters;

[0025] S5-4: Detect the elevation of the four corner points of the photovoltaic module, determine whether they are all located within the feasible region boundary polygon and meet the elevation constraints. If they are met, record the coordinates of the photovoltaic module and update the lowest horizontal line.

[0026] S5-5: Repeat steps S5-2 to S5-4 until no photovoltaic modules can be placed in the feasible area. The output parameters are the set of coordinates of photovoltaic modules that do not cross the boundary or overlap in the feasible area and the total number of photovoltaic modules.

[0027] As a further description of the above technical solution, in step S6, the operating parameters of the genetic algorithm include population size, crossover probability, mutation probability, and maximum number of iterations.

[0028] In step S6, the fitness function is calculated using the following formula:

[0029] ;

[0030] In the formula, F represents the area coverage of the photovoltaic module. This refers to the total coverage area of ​​the photovoltaic modules. The area of ​​the polygonal region is the feasible layout area.

[0031] As a further description of the above technical solution, the input parameters encoded in step S6 are divided into two cases:

[0032] Case 1: The input parameter is only the offset of the starting point in the photovoltaic layout model, which is suitable for scenarios where the photovoltaic module size parameters are fixed and the number of photovoltaic modules to be arranged needs to be maximized;

[0033] Scenario 2: The input parameters include the starting point offset, the photovoltaic module width scaling factor, and the photovoltaic module length scaling factor. This is suitable for scenarios that require maximizing photovoltaic module coverage and reducing wasted land.

[0034] As a further description of the above technical solution, the program in step S8 is an Autolisp program, and the drawing software is CAD software.

[0035] As a further description of the above technical solution, the target area is the airport area, and the airspace restriction condition is the airspace requirement for the construction of photovoltaic modules in the airport area.

[0036] As a further description of the above technical solution, the scatter interpolation algorithm in step S1 is used to construct a continuous grid-form predicted elevation model covering the target area based on the elevation data of the discrete grid point set.

[0037] The present invention has the following advantages:

[0038] 1. This invention, by establishing a predictive elevation model based on discrete grids, can effectively screen ultra-clear airspace areas and extract feasible area boundaries that meet airspace requirements. Based on the minimum horizontal line method, it realizes the rapid and automatic arrangement of photovoltaic modules within irregular polygons, improving design efficiency. Furthermore, by combining genetic algorithms, it optimizes and adjusts the panel layout design parameters. Through effect comparison, the optimized photovoltaic module layout scheme can further improve the photovoltaic module area coverage, make full use of land resources, maximize power generation benefits, and achieve the optimal design of airport photovoltaics. Attached Figure Description

[0039] Figure 1 This is a flowchart of the present invention.

[0040] Figure 2 This is a region map formed by connecting the original terrain point set of this invention.

[0041] Figure 3 This is a newly generated boundary polygon diagram that meets the clearance restriction requirements of this invention.

[0042] Figure 4 This is a diagram illustrating the layout effect of the present invention based on the minimum horizontal line method.

[0043] Figure 5 This is a diagram illustrating the optimized layout of the photovoltaic modules under the condition of fixed dimensions according to the present invention.

[0044] Figure 6 This is a diagram illustrating the optimized layout effect of the photovoltaic module under the optimized size conditions of the present invention. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings. It is hereby declared that the directional terms such as up, down, left, right, front, back, inside, and outside used in this text are based solely on the accompanying drawings and are not intended to specifically limit the invention.

[0046] Example 1:

[0047] The first step is to generate a topographic data file based on the topographic survey map. This data file contains several rows of topographic point data, with each row having the following format: Extract the minimum value of all X coordinates based on all terrain point data. and maximum value The minimum value of the Y-coordinate and maximum value .

[0048] The second step is to determine the grid resolution. ,exist Generate a regular grid point matrix within a rectangular area Using a scattered interpolation algorithm, based on the original terrain point set Constructing interpolation functions Calculate each point in the grid matrix Corresponding elevation value This allows us to obtain a complete digital elevation model.

[0049] It should be noted that, This represents the original number of terrain points; This represents the row number of the corresponding node in the grid matrix. This represents the column number of the corresponding node in the grid matrix.

[0050] The third step is to set elevation constraints, specifically the maximum allowable clearance elevation in this embodiment. Traversing all grid points in the digital elevation model will satisfy... The corresponding grid nodes are marked as feasible points. Using the ray casting method, all feasible points located outside the polygon enclosed by the original terrain points are eliminated. The remaining point set is denoted as... Among them, point set It is a discrete set of scattered points, and it is difficult to achieve precise arrangement when connecting them to generate new boundaries. The Alpha Shape algorithm is used to reconstruct its boundaries, and the algorithm radius adjustment coefficient is taken. =2, the formula for calculating the radius parameter is:

[0051] Formula 1;

[0052] In the formula, The grid resolution of the digital elevation model. This is the adjustment coefficient.

[0053] Step 4, for the point set The Alpha Shape algorithm is executed to obtain a set of line segments that form a closed shape. The outer contour of this shape is extracted, and the vertices are arranged in a clockwise order to form a new boundary polygon that meets the clearance requirements. For each vertex on the new boundary polygon, determine its current elevation value if it belongs to the original terrain point set; otherwise, use the interpolation function. Recalculate its elevation value, as shown in the attached figure. Figure 2 The attached map shows the region formed by connecting the original set of terrain points. Figure 3 This is the newly generated boundary polygon diagram that meets the clearance restriction requirements.

[0054] The fifth step is to apply the minimum horizontal line method to calculate the arrangement of photovoltaic modules. For example, the width of a single photovoltaic module is... The length of a single photovoltaic module Horizontal spacing of photovoltaic modules =0.1m, longitudinal spacing of photovoltaic modules With new boundary polygon middle Lowest point in the axial direction Initialize the lowest horizontal line for the baseline Specifically, it includes the following steps:

[0055] First, along the current horizontal line Axis direction search and The two intersection points are such that the distance between the intersection points is greater than the length of the photovoltaic module. Then, attempt to place photovoltaic modules within the intersection interval, with the placement location based on the new boundary polygon. middle Leftmost point along the axis and lateral spacing of photovoltaic modules To determine this, define the placement coordinates using the bottom left corner of the photovoltaic module. The calculation formula is:

[0056] = Formula 2;

[0057] = Formula 3;

[0058] In the formula, To place the first on the current horizontal line One photovoltaic module j represents the current horizontal line as the j-th lowest horizontal line. When j=1, it is the lowest horizontal line.

[0059] Secondly, after placing the photovoltaic panels in the previous steps, the X-ray method is used to check whether all four corners of the photovoltaic module are within range. Internally, if the check is successful, the current coordinates of the photovoltaic module placement are recorded, the current horizontal line is added to the list of feasible horizontal lines, and then the process is updated. Continue trying; if the check fails, update directly. Keep trying; when Exceeding Stop updating after the boundary is reached.

[0060] Next, after the current horizontal line is placed, move to the next lowest horizontal line and repeat the above process until all horizontal lines have been processed. The formula for updating the horizontal lines is:

[0061] Formula 4;

[0062] Repeat steps S1-S3 until... , For the new boundary polygon middle After all the photovoltaic modules have been placed at the highest point along the axis, the total number of photovoltaic modules arranged in this layout is counted. And area coverage rate F.

[0063] Finally, a photovoltaic module block was created, and the photovoltaic modules were batch-placed based on the placement coordinates using the Autolisp command (command "_.-INSERT"). The photovoltaic module placement was based on the minimum horizontal line method, as shown below. Figure 4 As shown, the total number of photovoltaic modules N=340, and the photovoltaic module area coverage F=66.03%; the minimum horizontal line method can realize the rapid and automatic arrangement of photovoltaic modules based on the arrangement area, which can save labor costs and improve the arrangement efficiency.

[0064] Example 2:

[0065] When arranging photovoltaic modules using the minimum horizontal line method in Example 1, the rows and columns of the arranged photovoltaic modules are aligned, which cannot achieve staggered arrangement of rows and columns and easily leads to wasted arrangement space. This example introduces a starting point longitudinal offset coefficient. And the lateral offset coefficient of the starting point As the parameter variable to be optimized, , They are mutually independent, and for each m=1,2,3 All satisfy , Maximum value = , This indicates that the calculation result is rounded down, starting at the m-th row. Axis offset , Axis offset .

[0066] Define the placement coordinates using the bottom left corner of the photovoltaic module. The calculation formula is:

[0067] = Formula 5;

[0068] = Formula 6;

[0069] In the formula, The first one placed on the current horizontal line One photovoltaic module j represents the current horizontal line as the j-th lowest horizontal line. When j=1, it is the lowest horizontal line.

[0070] The parameters to be optimized are encoded as chromosomes in the genetic algorithm, and the fitness function is defined as follows:

[0071] Formula 7;

[0072] In the formula, F represents the area coverage of the photovoltaic module. This refers to the total coverage area of ​​the photovoltaic modules. The area of ​​the polygonal region is the feasible layout area.

[0073] To maximize the coverage of photovoltaic modules Using a genetic algorithm as the objective function, optimization was performed with a population size of 50, a crossover probability of 0.8, a mutation probability of 0.2, and a maximum number of iterations of 200. The optimization calculation was repeated five times under the same conditions. The optimal fitness function value for all five calculations was 68.75%, resulting in a total photovoltaic module arrangement of N=354, which is better than the arrangement effect in Example 1, demonstrating the effectiveness of the optimization method.

[0074] Using the photovoltaic module location coordinates from the first optimization calculation, the Autolisp program's (command "_.-INSERT") command was used to batch arrange the modules based on their location coordinates and the photovoltaic module blocks. The arrangement result is shown below. Figure 5 As shown.

[0075] It should be noted that, based on Example 2, the photovoltaic module width scaling factor is adjusted. and photovoltaic module length scaling factor As input parameters for encoding, , The optimization objective at this point is to explore more suitable photovoltaic module size parameters to maximize the utilization of the installation area and improve the photovoltaic module area coverage. The formula for calculating the width of a single photovoltaic module is:

[0076] Formula 8;

[0077] The formula for calculating the length of a single photovoltaic module is:

[0078] Formula 9;

[0079] In the formula, This is the scaling factor for the width of the photovoltaic module; This is the scaling factor for the length of the photovoltaic module;

[0080] The remaining parameters and fitness function are the same as in Example 2. The optimization calculation was repeated 5 times, and the results are shown in Table 1:

[0081] Table 1 Optimization Calculation Results

[0082] name Photovoltaic module size ( m) Quantity (units) Area coverage rate F (%) Calculate 1 2.2434×1.5088 134 73.41 Calculate 2 1.8714×1.5003 162 73.61 Calculate 3 2.6415×1.5000 114 73.10 Calculate 4 1.3064×1.4709 234 72.77 Calculate 5 1.7789×1.5044 170 73.63

[0083] The table above shows that the fitness function value F after five optimization calculations is better than 68.75% of the optimization result in Example 2, indicating that the optimization method is effective. The optimal fitness function value among the five optimization calculations is F=73.63%, corresponding to a photovoltaic module size of 1.7789m × 1.5044m and a total of 170 photovoltaic modules. The optimization calculation results from calculation 5 in Table 1 are used to plot the arrangement of the photovoltaic modules, as shown in the figure. Figure 6 As shown.

[0084] It should be further noted that the grid resolution when constructing the predicted elevation model can be selected according to the actual situation. The photovoltaic module size parameters and spacing parameters selected in the embodiment are all example values ​​and can be changed according to the actual situation of the project and the actual equipment parameters.

[0085] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

Claims

1. A photovoltaic array optimization design method under airspace constraints, characterized in that, Includes the following steps: S1. Elevation Model Construction: Obtain the target area survey data and original boundary polygon vertex data, generate a discrete grid point set based on the horizontal coordinate range of the boundary and the preset grid resolution, and construct a predicted elevation model through a scatter interpolation algorithm. S2. Feasible point set screening: Based on the maximum allowable elevation preset by the clearance limit, screen out grid points whose elevation meets the constraints and are located within the original boundary as the feasible point set for photovoltaic layout; S3. Boundary contour extraction: The feasible point set is processed using a geometric reconstruction algorithm to extract its boundary contour; S4. New Boundary Drawing: Combine the boundary contour vertex data and the predicted elevation model to determine the vertex elevation, output the new boundary data and draw the new boundary polygon that meets the clearance requirements; S5. Initial layout calculation: Using the new boundary polygon as a constraint, establish a photovoltaic layout model using the horizontal line method, and calculate the location and total number of photovoltaic modules that do not cross the boundary or overlap within the feasible area. S6. Algorithm parameter settings: Encode the photovoltaic layout input parameters as chromosomes, define the fitness function for evaluating area coverage, and set the genetic algorithm running parameters; S7. Layout optimization iteration: Initialize the genetic algorithm population and iterate, decode chromosomes, call the layout model to calculate the results, and output the position of the photovoltaic module with the highest fitness through selection, crossover and mutation operations; S8. Automatic Component Drawing: The program reads the optimized positions of the photovoltaic modules and automatically draws the photovoltaic modules in the drawing software. In step S5, the specific implementation process of establishing the horizontal line method photovoltaic layout model includes: S5-1: Initialize the lowest horizontal line of the current component layout; S5-2: Based on the new boundary polygon The intersection with the current horizontal line determines the effective layout interval; S5-3: Within the effective range, place the photovoltaic modules according to the photovoltaic module size and lateral spacing parameters; S5-4: Detect the elevation of the four corner points of the photovoltaic module, determine whether they are all located within the feasible region boundary polygon and meet the elevation constraints. If they are met, record the coordinates of the photovoltaic module and update the lowest horizontal line. S5-5: Repeat steps S5-2 to S5-4 until no photovoltaic modules can be placed in the feasible area. The output parameters are the set of coordinates of photovoltaic modules that do not cross the boundary or overlap in the feasible area and the total number of photovoltaic modules.

2. The photovoltaic array optimization design method under airspace constraints according to claim 1, characterized in that: The scattered interpolation algorithm in step S1 is used to construct a continuous grid-form elevation prediction model covering the target area based on the elevation data of discrete grid point sets. The specific implementation process of the predicted elevation model includes: S1-1: Determine the target area where photovoltaic modules need to be installed based on topographic survey data; S1-2: Obtain the vertex data of the original boundary polygon of the target area, wherein the vertex data includes horizontal coordinates (X, Y) and elevation values ​​(Z). S1-3: Generate a discrete set of grid points covering the horizontal coordinate range of the original boundary polygon and the preset grid resolution; S1-4: Construct a grid-based predicted elevation model using a scatter interpolation algorithm.

3. The photovoltaic array optimization design method under airspace constraints according to claim 1, characterized in that, The geometric reconstruction algorithm in step S3 is the Alpha Shape algorithm, and the formula for calculating the radius parameter of the Alpha Shape algorithm is: In the formula, The grid resolution of the digital elevation model. This is the adjustment coefficient.

4. The photovoltaic array optimization design method under airspace constraints according to claim 1, characterized in that, In step S4, the method for determining the elevation values ​​of the vertices of the new boundary polygon is as follows: traverse each vertex on the new boundary polygon and calculate the Euclidean distance between it and the vertices in the original boundary polygon that meet the screening conditions; if the Euclidean distance is less than the preset tolerance, the elevation value of the corresponding vertex in the original boundary polygon is directly used; otherwise, the elevation value of the vertex is calculated using the predicted elevation model.

5. A photovoltaic array optimization design method under airspace constraints as described in claim 1, characterized in that, In step S6, the operating parameters of the genetic algorithm include population size, crossover probability, mutation probability, and maximum number of iterations. In step S6, the fitness function is calculated using the following formula: In the formula, F represents the area coverage of the photovoltaic module. This refers to the total coverage area of ​​the photovoltaic modules. The area of ​​the polygonal region is the feasible layout area.

6. A photovoltaic array optimization design method under airspace constraints as described in claim 5, characterized in that, The input parameters encoded in step S6 fall into two categories: Case 1: The input parameter is only the offset of the starting point in the photovoltaic layout model, which is suitable for scenarios where the photovoltaic module size parameters are fixed and the number of photovoltaic modules to be arranged needs to be maximized; Scenario 2: The input parameters include the starting point offset, the photovoltaic module width scaling factor, and the photovoltaic module length scaling factor. This is suitable for scenarios that require maximizing photovoltaic module coverage and reducing wasted land.

7. A photovoltaic array optimization design method under airspace constraints as described in claim 1, characterized in that, The program running in step S8 is Autolisp, and the drawing software is CAD software.

8. A photovoltaic array optimization design method under airspace constraints as described in claim 1, characterized in that, The target area is the airport area, and the airspace restriction conditions are the airspace requirements for the construction of photovoltaic modules in the airport area.

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