Multi-tower heliostat field layout optimization method and system based on multi-objective genetic algorithm and computer readable storage medium thereof

Abstract

This invention provides a method, system, and computer-readable storage medium for optimizing the layout of a multi-tower heliostat field based on a multi-objective genetic algorithm. The method includes: determining the initial data required for the field layout optimization, including geographic data, time data, equipment parameters, and layout control parameters; generating coordinate information of each heliostat in the multi-tower system based on the layout control parameters; calculating the projection power of each heliostat to the solar collector tower and the mutual shading loss between heliostats at each time point based on the initial data and the heliostat coordinate information; determining the comprehensive performance index of the field based on the projection power and mutual shading loss; iteratively optimizing the layout control parameters using a multi-objective genetic algorithm, with the layout control parameters as the optimization variable and the comprehensive performance index as the optimization objective; and outputting the optimized solution set of the multi-tower heliostat field layout when a preset termination condition is met. This achieves a comprehensive balance between the cost and stability of the multi-tower heliostat field.

Description

Technical Field

[0001] This invention relates to the field of solar thermal power generation technology, and more specifically, to a method, system, and computer-readable storage medium for optimizing the layout of multi-tower heliostat fields based on a multi-objective genetic algorithm. Background Technology

[0002] Tower solar thermal power generation technology focuses solar radiation onto a receiver at the top of a central tower using a heliostat array. This heats the working medium to produce high-temperature, high-pressure steam or gas, which then drives a steam turbine or gas turbine—a clean energy technology. With the addition of an energy storage system, stable power output can be achieved.

[0003] Currently, traditional single-tower heliostat field layout methods mainly include the following steps: First, estimate the number of heliostats required for a set power output, and generate a preliminary heliostat field based on the estimation results; second, calculate the solar radiation received by the power station and the power generation efficiency based on the generated heliostat field; finally, compare the calculation results with the design target, and remove or add heliostats in the generated heliostat field until the design requirements are met. Another common optimization method is to generate the heliostat field within a given range and pursue the highest average heliostat field cosine efficiency through optimization algorithms.

[0004] However, the above methods have the following drawbacks: First, the trial-and-error approach of adding or removing heliostats may waste site space and cannot positively impact reducing mutual shading between heliostats; second, the method of pursuing the highest cosine efficiency cannot provide a comprehensive solution for the fully stable operation of the power plant, because there is an upper limit to the solar radiation that a single collector can withstand. Exceeding this limit may lead to decreased system stability or force a large number of heliostats to stop working, while there is also a lower limit to the acceptable solar radiation. Below this limit, the normal operation of the power plant will be affected; third, existing methods are mainly designed for single-tower systems and are difficult to apply directly to the complex layout of multi-tower heliostat fields, failing to achieve optimized allocation and reuse of heliostats among multiple towers. Therefore, how to provide a heliostat field layout optimization method that can adapt to multi-tower systems and comprehensively consider construction costs, operational stability, and power generation accuracy is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a multi-tower heliostat field layout optimization method based on a multi-objective genetic algorithm, which addresses the above-mentioned technical defects in the existing technology.

[0006] The technical solution adopted by this application to solve its technical problem is: constructing a multi-tower heliostat field layout optimization method based on a multi-objective genetic algorithm, including the following steps: Step S1: Determine the initial data required for mirror field layout optimization. The initial data includes geographical data, time data, equipment parameters, and layout control parameters. Step S2: Generate the coordinate information of each heliostat in the multi-tower system according to the layout control parameters; Step S3: Based on the initial data and heliostat coordinate information, calculate the projection power of each heliostat to the solar collector tower at each time point and the mutual shading loss between heliostats; Step S4: Determine the comprehensive performance index of the mirror field based on the projection power and the mutual occlusion loss; Step S5: Using the layout control parameters as optimization variables and the comprehensive performance index as optimization objectives, a multi-objective genetic algorithm is used to iteratively optimize the layout control parameters. When the preset termination condition is met, the optimized solution set of the multi-tower heliostat field layout is output.

[0007] Furthermore, the layout control parameters include a lateral parameter X and a longitudinal parameter. Where i represents the region number; the step S2 of generating heliostat coordinate information includes: based on the horizontal parameter X and the vertical parameter... A radial staggered layout and geometric mapping screening method are adopted to divide the mirror field area with the heat collection tower as the center and generate the coordinate information of each heliostat.

[0008] Furthermore, the radial staggered layout and geometric mapping filtering method includes the following steps: The initial layout radius is determined with the heat collection tower as the center, according to the preset restricted area; The center-to-center distance between adjacent heliostats is calculated based on the diagonal length and lateral parameter X of the heliostats. Based on the center spacing and longitudinal parameters Determine the ring spacing for each region; Based on the initial arrangement radius and ring spacing, heliostat coordinates are generated ring by ring, and heliostats are evenly arranged in the same ring according to azimuth intervals. When the gap between adjacent heliostats on the same ring is large enough to insert another heliostat, the current area division is completed, and the arrangement of the next area begins.

[0009] Furthermore, the center-to-center distance DM between adjacent heliostats is calculated using the following formula: in, The length of the diagonal of the heliostat. X represents the length of the heliostat, and X is the lateral parameter.

[0010] Furthermore, the diagonal length DH of the heliostat is calculated using the following formula: in, The width of the heliostat, This is the length of the heliostat.

[0011] Furthermore, the ring spacing of each region Calculated using the following formula: in, For vertical parameters, This represents the center-to-center distance between adjacent heliostats.

[0012] Furthermore, the azimuth interval ᵢ is calculated using the following formula: in, The distance between the centers of adjacent heliostats. The initial layout radius is 'i', where 'i' represents the area number.

[0013] Furthermore, the condition for determining whether the region division is complete is: the number n of heliostats currently arranged on the ring satisfies n×DM≈ Where R is the radius of the current ring; after the current region is divided, the starting radius of the next region is... .

[0014] Furthermore, the calculation of the projection power of each heliostat onto the solar collector tower in step S3 includes: Based on the geographical data and the time data, calculate the solar irradiance (DNI) and solar altitude angle at each time point. Sun azimuth and the sun's position vector S; For each heliostat, calculate its projection power onto each collector tower, and select the collector tower with the highest projection power as the actual projection tower for that heliostat.

[0015] Furthermore, the solar position vector S is calculated using the following formula: Furthermore, for coordinates ( , , A heliostat with coordinates () , , For a solar collector tower, the projected power is calculated using the following steps: Calculate the distance from the heliostat to the solar collector: in, This refers to the height of the heliostat base; Calculate the reflected sunlight vector: Calculate the unit normal vector of the heliostat: Where S is the sun's position vector; Calculate the cosine efficiency of the heliostat: .

[0016] Furthermore, the atmospheric attenuation factor is also included when calculating the projected power. The atmospheric attenuation factor Calculated using the following formula: .

[0017] Furthermore, when calculating the projected power of the heliostat onto the solar collector, a two-dimensional Gaussian distribution is used to describe the energy distribution of the reflected light spot on the collector surface. The two-dimensional Gaussian distribution function F(x,y) is: in, For the reflectivity of the heliostat, For shadow occlusion rate, ( , () represents the coordinates of the light spot center. This represents the standard deviation of the total beam error.

[0018] Furthermore, the two-dimensional Gaussian distribution function is numerically integrated on the collector receiving surface using the two-dimensional Simpson integration method to calculate the total power I projected onto the collector by the heliostat.

[0019] Furthermore, the calculation of the mutual shading loss between heliostats in step S3 includes: The sampling point ray tracing method is used to uniformly select multiple sampling points on the reflecting surface of the main heliostat; A search circle is set with the main heliostat as the center, and other heliostats that fall within the range of the search circle are regarded as auxiliary heliostats; Determine whether the incident and reflected rays passing through each sampling point are blocked by the secondary heliostat; The shading rate of the primary heliostat is calculated based on the ratio of the number of obscured sampling points to the total number of sampling points. .

[0020] Furthermore, determining whether light is blocked by the secondary heliostat includes: Transform the coordinates of the sampling points from the local coordinate system of the primary heliostat to the global coordinate system; Establish the parameter equations for the incident ray and the reflected ray passing through the sampling point in the global coordinate system; Calculate the coordinates of the intersection points of the incident ray and the reflected ray with the plane containing the secondary heliostat; Transform the coordinates of the intersection point from the global coordinate system to the local coordinate system of the secondary heliostat; Determine whether the coordinates of the intersection point are within the mirror surface of the secondary heliostat. If so, determine that the corresponding ray of the sampling point is blocked.

[0021] Furthermore, the comprehensive performance indicators in step S4 include: First objective: Total number of heliostats; Second objective: To determine the number of time points within the preset calculation time points that meet the preset design power range; The third objective is to calculate the deviation between the average power and the target average power.

[0022] Furthermore, the iterative optimization using a multi-objective genetic algorithm in step S5 includes: The population is initialized using the layout control parameters as the individual codes; Substitute each individual into steps S2 to S4 in sequence to calculate its corresponding first target value, second target value and third target value; Based on the first target value, the second target value, and the third target value, a non-dominated ranking is performed to determine the Pareto level of each individual; Based on the Pareto hierarchy, a new generation of population is generated through selection, crossover, and mutation operations; Repeat the iteration until the preset termination condition is met, and output the Pareto optimal solution set.

[0023] The present invention also provides a multi-tower heliostat field layout optimization system based on a multi-objective genetic algorithm, including a processor and a memory storing a computer program. When the processor executes the computer program, it implements the steps of the multi-tower heliostat field layout optimization method based on the multi-objective genetic algorithm described above.

[0024] The present invention also provides a computer-readable storage medium storing a computer program adapted to be loaded and executed by a processor to implement the steps of the multi-tower heliostat field layout optimization method based on a multi-objective genetic algorithm described above.

[0025] The beneficial effects of this invention are that it provides a method for optimizing the layout of a multi-tower heliostat field based on a multi-objective genetic algorithm, comprising the following steps: Step S1: Determine the initial data required for the optimization of the heliostat field layout, the initial data including geographical data, time data, equipment parameters, and layout control parameters; Step S2: Generate the coordinate information of each heliostat in the multi-tower system according to the layout control parameters; Step S3: Calculate the projection power of each heliostat to the solar collector tower and the mutual shading loss between heliostats at each time point according to the initial data and the coordinate information of the heliostats; Step S4: Determine the comprehensive performance index of the heliostat field according to the projection power and the mutual shading loss; Step S5: Using the layout control parameters as optimization variables and the comprehensive performance index as optimization objectives, iteratively optimize the layout control parameters using a multi-objective genetic algorithm, and output the optimized solution set of the multi-tower heliostat field layout when the preset termination condition is met. This invention can meet the needs of complex layouts of multi-tower mirror fields. By using a multi-objective genetic algorithm to iteratively optimize the layout control parameters, it achieves a comprehensive balance between construction cost, operational stability, and power generation accuracy. While reducing the number of heliostats, it improves the stability and power generation accuracy of the system. Attached Figure Description

[0026] The present invention will be further described below with reference to the accompanying drawings and embodiments. In the accompanying drawings: Figure 1 This is a flowchart of the multi-tower heliostat field layout optimization method based on multi-objective genetic algorithm of the present invention; Figure 2 This is a schematic diagram of a double-tower heliostat field generated using the method of this invention; Figure 3 This is a schematic diagram of a three-tower heliostat field generated using the method of this invention; Figure 4 This is a schematic diagram of a four-tower heliostat field generated using the method of this invention; Figure 5 This is a flowchart illustrating the calculation of heliostat coordinate information in one embodiment of the present invention; Figure 6 This is a detailed flowchart of the solar module calculation logic in one embodiment of the present invention; Figure 7 This is a flowchart illustrating the calculation logic of the heliostat projection power and tower selection information in a dual-tower system according to an embodiment of the present invention. Figure 8 This is a flowchart illustrating the calculation logic of the heliostat projection power and tower selection information in a three-tower system according to an embodiment of the present invention. Figure 9 This is a flowchart illustrating the calculation logic of the heliostat projection power and tower selection information in a four-tower system according to an embodiment of the present invention. Figure 10This is a flowchart illustrating the calculation of the heliostat shadow occlusion rate in one embodiment of the present invention; Figure 11 This is a flowchart of the overall optimization calculation based on a multi-objective genetic algorithm in one embodiment of the present invention. Detailed Implementation

[0027] To provide a clearer understanding of the technical features, objectives, and effects of the present invention, specific embodiments of the invention are now described in detail with reference to the accompanying drawings. In the following description, specific details such as particular structures and techniques are set forth for illustrative purposes and not for limitation, so as to provide a thorough understanding of the embodiments of the invention. However, those skilled in the art will understand that the invention can also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known devices, circuits, and methods are omitted so as not to obscure the description of the invention with unnecessary detail.

[0028] like Figure 1 As shown, Figure 1 This is a flowchart illustrating a multi-tower heliostat field layout optimization method based on a multi-objective genetic algorithm according to an embodiment of the present invention. The method of this embodiment includes the following steps: Step S1: Determine the initial data required for the heliostat field layout optimization. The initial data includes geographical data, time data, equipment parameters, and layout control parameters. Step S2: Generate the coordinate information of each heliostat in the multi-tower system based on the layout control parameters. Step S3: Calculate the projection power of each heliostat to the solar collector tower and the mutual shading loss between heliostats at each time point based on the initial data and heliostat coordinate information. Step S4: Determine the comprehensive performance index of the heliostat field based on the projection power and mutual shading loss. Step S5: Using the layout control parameters as optimization variables and the comprehensive performance index as the optimization objective, iteratively optimize the layout control parameters using a multi-objective genetic algorithm. When the preset termination condition is met, output the optimized solution set of the multi-tower heliostat field layout.

[0029] like Figure 1 As shown, the method in this embodiment includes the five core steps described above. Specifically, in step S1, the geographical data includes the latitude and longitude of the power station location, the coordinates of the center of the heliostat field, the number of solar collectors and their coordinates, the radius of the heliostat field, and the restricted area; the time data includes the calculation date (such as typical days like the spring equinox, summer solstice, autumn equinox, and winter solstice) and the calculation time points (such as taking a time point every hour from sunrise to sunset); the equipment parameters include the geometric dimensions of the heliostat (length, width, base height), optical performance parameters (reflectivity, cleanliness), the integral boundary and grid division degree of the solar collector, and error factors such as solar shape error, mirror shape error, and tracking error; the layout control parameters are variables for subsequent optimization, used to control the arrangement of the heliostats in the heliostat field. In this embodiment, the parameters include the lateral parameter X and the longitudinal parameter X. (i represents the region number). In step S2, the coordinate information of each heliostat is generated based on the layout control parameters, with the solar collector tower as the center. In step S3, the solar irradiance, solar altitude angle, solar azimuth angle, and solar position vector are first calculated. Then, the projection power of each heliostat onto each solar collector tower is calculated, and the actual projection tower is selected. Finally, the shading rate between the heliostats is calculated. In step S4, the comprehensive performance index is determined based on the projection power and mutual shading loss. In step S5, the layout control parameters are used as optimization variables, and the comprehensive performance index is used as the optimization objective. A multi-objective genetic algorithm is used for iterative optimization, and the Pareto optimal solution set is finally output. This invention achieves automated optimization of the layout of a multi-tower heliostat field using the above method, taking into account construction cost, operational stability, and power generation accuracy.

[0030] Furthermore, the layout control parameters include the lateral parameter X and the longitudinal parameter. Where i represents the region number; step S2 generates heliostat coordinate information including: based on the horizontal parameter X and the vertical parameter A radial staggered layout and geometric mapping screening method are adopted to divide the mirror field area with the heat collection tower as the center and generate the coordinate information of each heliostat.

[0031] In this invention, the lateral parameter X is used to control the spacing between adjacent heliostats in the same ring, and the longitudinal parameter... This method controls the spacing between adjacent rings within the same region. The radially staggered layout arranges heliostats along radial rings, with adjacent rings staggered, effectively reducing mutual shading between heliostats. Geometric mapping filtering is a heliostat field region division method based on the region multiplication principle. It divides the heliostat field into multiple geometrically regular regions through a region multiplication strategy. A virtual layout is created centered on the solar collector tower, and then a real layout is generated according to the defined regions. The coordinates of each heliostat need to be checked to see if it falls within the defined region, and only heliostats within that region are retained. This layout generation method provides flexible and feasible space for subsequent optimization.

[0032] Figures 2 to 4 The schematic diagrams of the double-tower, triple-tower, and quadruple-tower heliostat field layouts generated using the method of this invention are shown respectively. Figure 2 This is a dual-tower system. The figure shows the distribution of the two solar collectors and the surrounding heliostats. Figure 3 It is a three-tower system, with the heat collection towers arranged in a triangular pattern; Figure 4 This is a four-tower system with the heat collection towers arranged in a rectangular pattern. These diagrams illustrate the layout generation capability of this method in multi-tower scenarios.

[0033] Furthermore, the radial staggered layout and geometric mapping screening method includes the following steps: determining the initial layout radius based on the solar collector tower and a preset restricted area; calculating the center-to-center distance between adjacent heliostats based on the diagonal length of the heliostats and the lateral parameter X; and calculating the center-to-center distance based on the center-to-center distance and the longitudinal parameter X. Determine the ring spacing for each region; based on the initial arrangement radius and ring spacing, generate heliostat coordinates ring by ring, and arrange heliostats evenly within the same ring according to azimuth intervals; when the gap between adjacent heliostats on the same ring is sufficient to insert another heliostat, the current region division is completed, and the arrangement of the next region begins.

[0034] Specifically, the restricted area refers to the region within a certain radius around the solar collector tower where heliostats are not placed, to avoid light damage caused by the tower's projection. The initial placement radius is determined based on the restricted area and is the radius of the first heliostat ring. The center-to-center distance between adjacent heliostats is calculated based on the diagonal length of the heliostat and the lateral parameter X; the larger the lateral parameter X, the larger the center-to-center distance. The ring spacing between each region is based on the center-to-center distance and the longitudinal parameter. The calculated longitudinal parameters The larger the radius, the greater the spacing between rings. When generating heliostat coordinates ring by ring, heliostats are evenly distributed within the same ring according to azimuth intervals to ensure uniform distribution. When the gap between adjacent heliostats on the same ring is large enough to insert another heliostat, it indicates that the current area cannot be effectively utilized by increasing the number of rings. At this point, the current area is divided, and the next area is arranged. The starting radius of the next area is determined according to the area doubling principle; generally, four areas can completely cover the layout area.

[0035] Furthermore, the center-to-center distance DM between adjacent heliostats is calculated using the following formula: (1) Where DH is the diagonal length of the heliostat. X represents the length of the heliostat, and X is the lateral parameter.

[0036] In this formula, the center-to-center distance DM consists of the diagonal length DH of the heliostats and an additional spacing. The diagonal length DH ensures that there is no mechanical collision between adjacent heliostats, while the additional spacing provides extra space to reduce mutual shading between heliostats and to reserve space for equipment maintenance. The lateral parameter X is an adjustable parameter; by optimizing the value of X, a balance can be achieved between mirror field density and shading loss.

[0037] The diagonal length DH of the heliostat is further calculated using the following formula: (2) in, The width of the heliostat, This is the length of the heliostat.

[0038] Heliostats are typically rectangular, and their diagonal length is calculated by taking the square root of the square of their width and length. This formula reflects the geometry of the heliostat, and the diagonal length is a fundamental parameter for determining the center-to-center distance between adjacent heliostats, ensuring that they do not collide when rotating to track the sun. Additional spacing distance. Calculated using the following formula: (3) =X·L h Based on the geometric relationship of the radially staggered layout, the minimum spacing between the rings can be derived. Approximately: (4) Furthermore, the ring spacing between each region Calculated using the following formula: (5) in, is the longitudinal parameter, and DM is the center-to-center distance between adjacent heliostats.

[0039] Ring spacing It determines the radial distance between adjacent rings within the same region. It is the cosine of the standard staggered angle for a radially staggered arrangement. This angle allows the heliostats of adjacent rings to form a staggered arrangement, effectively reducing radial shading. Longitudinal parameters It is an adjustable parameter; different parameters can be used in different regions. The values ​​are adjusted to accommodate the occlusion characteristics of different regions of the mirror field. Typically, the mirror field is divided into four regions, each with its own independent longitudinal parameter.

[0040] Furthermore, azimuth interval Calculated using the following formula: (6) Where DM is the center-to-center distance between adjacent heliostats. The initial layout radius is 'i', where 'i' represents the area number.

[0041] Azimuth interval The angular spacing between adjacent heliostats within the same ring is determined. As the region number i increases, the denominator increases, leading to a decrease in the azimuth spacing. This means that more heliostats can be placed on rings farther from the solar collector. This design conforms to the geometry of the heliostat field, making the distribution of heliostats more rational. The initial radius of region i... Calculated using the following formula: (7) Number of rings between heliostats in the same area Calculated using the following formula: (8) Number of heliostats in the k-th ring of region i Calculated using the following formula: (9) Furthermore, the condition for determining whether the region division is complete is: the number of heliostats n arranged on the current ring satisfies n× ≈ Where R is the radius of the current ring; after the current region is divided, the starting radius of the next region is... .

[0042] This judgment condition is based on the region doubling strategy in topology: when the product of the number of heliostats n on the current ring and the center-to-center distance DM approaches the ring's circumference, the gap between adjacent heliostats is just large enough to insert another heliostat. At this point, the current region can no longer effectively utilize space by increasing the number of rings, and the region division is complete. The starting radius of the next region is determined by... This process ensures that the radius of the heliostats in different regions is doubled, maintaining good geometric regularity. Each generated heliostat coordinate needs to be checked to determine if it falls within the defined region, and only heliostats within that region are retained.

[0043] In one embodiment, taking tower 1 in a four-tower system as an example, its coordinates are set as (0, 500, 200), the restricted area radius R_excl = 50m, and the length of the heliostat L_h = 10m and the width W_h = 8m. First, the diagonal length of the heliostat is calculated. ≈12.81m. If the lateral parameter X of the current individual is 1.5, then the additional spacing... =X×L_h=15m, the center-to-center distance between adjacent heliostats DM=DH+ =27.81m. Minimum spacing between rings ≈ DM × cos30° = 27.81 × 0.866 ≈ 24.08m. If the longitudinal parameter =1.2, then the ring spacing of region 1 is... ×DM×cos30°=1.2×24.08≈28.90m. The initial radius of region 1 is 50m, and the radius of the first ring is also 50m. The azimuth interval... =DM / (1× =27.81 / 50 = 0.5562 rad ≈ 31.87°, the number of heliostats that can be placed in the first ring is floor ( / )=11 faces. Generate ring by ring using this method until region 1 is completed, then proceed to region 2, with a starting radius of 2× =100m, and the new longitudinal parameter is used to calculate the ring spacing of region 2, and so on to complete the generation of heliostat coordinates for all regions. Figure 5 The process of generating heliostat coordinates using the heliostat field layout module is demonstrated, including: determining the initial layout radius with the solar collector tower as the center; calculating the center-to-center distance based on the diagonal length of the heliostats and the lateral parameter X; and calculating the center-to-center distance based on the longitudinal parameter X. Determine the ring spacing, generate heliostat coordinates ring by ring, and determine whether the region division is complete.

[0044] Furthermore, step S3, calculating the projected power of each heliostat onto the solar collector tower, includes: Based on geographical and temporal data, calculate the solar irradiance (DNI) and solar altitude angle at each time point. Sun azimuth and the sun's position vector S; For each heliostat, calculate its projection power onto each collector tower, and select the collector tower with the highest projection power as the actual projection tower for that heliostat.

[0045] Solar Irradiance (DNI), Solar Altitude Angle Sun azimuth The solar position vector S is a fundamental parameter of solar geometry and radiation, calculated using a solar model. For each heliostat in a multi-tower system, its projection power to each collector tower needs to be calculated separately, and the actual projection tower is determined by comparison. This tower selection strategy ensures that each heliostat always reflects sunlight to the collector tower that can obtain the maximum power output, achieving optimized scheduling of the multi-tower system.

[0046] Specifically, the solar module determines the input latitude φ, date n, and solar time. The above parameters are calculated using a solar model. The solar hour angle ω is calculated using the following formula: (10) The solar declination angle δ is calculated using the following formula: (11) Solar altitude angle Calculated using the following formula: (12) Sun azimuth Calculated using the following formula: (13) The solar position vector S is calculated using the following formula: (14) Solar irradiance DNI is calculated using the following formula: (15) Figure 6 This demonstrates the calculation process of the solar module, taking latitude φ, date n, and solar time τ as inputs, and outputting solar irradiance DNI and solar azimuth angle. Solar altitude angle The solar position vector S. Taking 12 noon on the vernal equinox as an example, with latitude φ=40°, date n=81, and solar time τ=12, the calculated solar hour angle ω=0, solar declination angle δ=0, and solar altitude angle... =50°, solar azimuth angle =180°, solar position vector S=(0,-0.643,0.766), DNI≈850W / m².

[0047] For different numbers of heat collection towers, this invention provides a corresponding tower selection calculation logic flowchart. Figure 7 The calculation process for heliostat projection power and tower selection information under a dual-tower system is demonstrated. Figure 8 The corresponding process for the three-tower system; Figure 9 The flowcharts describe the corresponding process for a four-tower system. They detail how to traverse all the heat collection towers, calculate power, and determine the optimal tower location.

[0048] Furthermore, the solar position vector S is calculated using the following formula: (14) The solar position vector S is a three-dimensional unit vector pointing in the direction of the sun. This vector is determined by the solar altitude angle. and solar azimuth The calculations obtained through trigonometric functions form the basis for subsequent calculations of the heliostat normal vector and cosine efficiency.

[0049] Furthermore, for coordinates ( , , A heliostat with coordinates () , , For a solar collector tower, the projected power is calculated using the following steps: First, calculate the distance from the heliostat to the solar collector: (16 in, This refers to the height of the heliostat base.

[0050] Next, calculate the vector of reflected sunlight: (17) Then, calculate the unit normal vector of the heliostat: (18) Wherein, the magnitude of the normal vector Calculated using the following formula: (19) S is the vector of the sun's position.

[0051] Next, the cosine efficiency of the heliostat is calculated: (twenty two) In addition, it also includes the calculation of other related parameters: The heliostat rotation azimuth angle θ is calculated using the following formula: (20) The heliostat rotation elevation angle is calculated using the following formula: (twenty one) The angle λ between the line connecting the heliostat and the receiver and the vertical direction is calculated using the following formula: (twenty three) Astigmatism error σ ast Calculated using the following formula: (25) in, This is the focal length of the heliostat.

[0052] Total beam error standard deviation σ tot Calculated using the following formula: (26) ) in, This is the square of the error in the shape of the sun. This is the square of the mirror shape error. This is the square of the tracking error.

[0053] Of the parameters mentioned above, This is the distance from the heliostat to the solar collector; , , The three-dimensional coordinates of the collector center in the global coordinate system; , , Here are the coordinates of the heliostat base; T is the vector of reflected sunlight. This refers to the height of the heliostat base; Let S be the magnitude of the normal vector; S is the sun's position vector. This is the northward component of the normal vector; This is the eastward component of the normal vector; The perpendicular component of the normal vector; The heliostat is rotated by an azimuth angle (horizontal rotation, with due east as 0 degrees). The elevation angle of the heliostat rotation (rotation up and down, with 0 degrees directly above). For the cosine efficiency of the heliostat; The angle between the line connecting the heliostat and the receiver and the vertical direction; This is for astigmatism error.

[0054] Furthermore, the atmospheric attenuation factor is also included when calculating the projected power. Atmospheric attenuation factor Calculated using the following formula: (twenty four) The atmospheric attenuation factor reflects the energy loss caused by atmospheric absorption and scattering during the propagation of sunlight from the heliostat to the collector. This factor is related to the propagation distance. Correlation is stronger with increasing distance; the greater the distance, the greater the attenuation. When When the value is ≤1000m, a quadratic polynomial fitting is used. For distances >1000m, an exponential decay model is used. This piecewise function can accurately describe the atmospheric decay characteristics at different distances.

[0055] In one embodiment, for the heliostat with coordinates (50, 500, 0) generated in step S2, its projected power onto the four solar collector towers is calculated. Taking tower 1 (0, 500, 200) as an example, the calculated power is... ≈201.3m, ≈0.933, ≈0.9586. Assuming initial shadow occlusion rate. =1 (i.e., mutual obstruction is not considered for now), so the projection power is 58.3kW. Similarly, the power for towers 2, 3, and 4 is calculated to be 12.7kW, 8.5kW, and 12.7kW respectively. Comparison shows that the power for tower 1 is the highest. Therefore, tower 1 is selected as the actual projection tower for this heliostat, with an initial projection power of 58.3kW.

[0056] Furthermore, when calculating the projected power of the heliostat onto the solar collector, a two-dimensional Gaussian distribution is used to describe the energy distribution of the reflected light spot on the collector surface. The two-dimensional Gaussian distribution function F(x,y) is: (27) in, For the reflectivity of the heliostat, For shadow occlusion rate, ( , () represents the coordinates of the light spot center. It is the square of the standard deviation of the total beam error.

[0057] Due to factors such as solar shape errors, mirror shape errors, and tracking errors, the light spot reflected by the heliostat is not uniformly distributed on the collector surface, but rather approximates a two-dimensional Gaussian distribution. This distribution function comprehensively considers the DNI (difference in solar density) and the heliostat's reflectivity. Cosine efficiency Atmospheric attenuation factor Shadow occlusion rate and light spot diffusion characteristics (through standard deviation) The description can accurately describe the energy distribution reaching the surface of the solar collector.

[0058] Furthermore, the two-dimensional Simpson integral method is used to numerically integrate the two-dimensional Gaussian distribution function on the collector receiving surface to calculate the total power I projected onto the collector by the heliostat.

[0059] The total power I projected onto the solar collector by the heliostat is obtained by double integral of the energy distribution function F(x,y) over the receiving surface Ω of the solar collector: (28) Using the two-dimensional Simpson integration method for numerical integration can significantly reduce the computational cost while maintaining accuracy. (29) Wherein, the integration step size L x and Calculated using the following formula: (30) Weighting coefficients of the Simpson integral and Determined by the following rules: (31) in, Let be the integral step size of the solar collector's light-receiving surface in the x-direction. Let be the integral step size of the solar collector's light-receiving surface in the y-direction; , The coordinates of the center of the light spot on the surface of the solar collector are given. , Let x be the lower and upper limits of the integral of the collector receiving surface in the x-direction. , Let be the lower and upper limits of the integral of the collector receiving surface in the y direction; The number of grid divisions for the collector receiving surface in the x-direction; The number of mesh divisions for the collector receiving surface in the y-direction. The number of mesh divisions for the collector receiving surface in both the x and y directions. , The settings are usually determined based on the size of the solar collector and the required accuracy.

[0060] Furthermore, step S3, calculating the mutual shading loss between heliostats, includes: The sampling point ray tracing method is used to uniformly select multiple sampling points on the reflecting surface of the main heliostat; A search circle is set with the main heliostat as the center, and other heliostats that fall within the search circle are used as auxiliary heliostats; Determine whether the incident and reflected rays passing through each sampling point are blocked by the secondary heliostat; The shading rate of the primary heliostat is calculated based on the ratio of the number of obscured sampling points to the total number of sampling points. .

[0061] The sampling point ray tracing method involves uniformly selecting sampling points on the surface of the primary heliostat and tracing the incident ray (pointing towards the sun) and reflected ray (pointing towards the actual projection tower) at each sampling point to determine if it is obstructed by other heliostats. Setting a search circle can narrow down the range of secondary heliostats to be checked, improving computational efficiency. Shadow occlusion rate. Defined as the proportion of unobstructed sampling points, it reflects the proportion of the effective reflective surface of the heliostat and is a key parameter characterizing the loss due to mutual shading.

[0062] like Figure 10 As shown, the shadow occlusion module uses the sampling point ray tracing method to calculate the shadow occlusion rate of each heliostat. Taking the heliostat with coordinates (50, 500, 0) as the primary heliostat, 10 × 10 = 100 sampling points are uniformly selected on its reflecting surface. With the center of the primary heliostat as the center, a search circle with radius R_search = 100m is set, and other heliostats falling within this circle are considered secondary heliostats that may cause occlusion. For each sampling point, it is determined whether the incident and reflected rays are blocked. The number of blocked sampling points, sum, is then used to calculate the shadow occlusion rate. =(100-sum) / 100. For example, if 23 sampling points are occluded, then... =0.77.

[0063] Furthermore, determining whether light is blocked by the secondary heliostat includes: Transform the coordinates of the sampling points from the local coordinate system of the primary heliostat to the global coordinate system; Establish the parameter equations for the incident ray and the reflected ray passing through the sampling point in the global coordinate system; Calculate the coordinates of the intersection points of the incident ray and the reflected ray with the plane containing the secondary heliostat; Transform the coordinates of the intersection point from the global coordinate system to the local coordinate system of the secondary heliostat; Determine whether the coordinates of the intersection point are within the mirror surface of the secondary heliostat. If so, determine that the corresponding ray of the sampling point is blocked.

[0064] The specific coordinate system transformation and core formulas for ray tracing are as follows: First, establish the local coordinate system of the heliostat. The normal vector N of the heliostat is determined by the following formula: (32) Unit vector e in the x-direction of the heliostat plane x Calculated using the following formula: (33) Where V_ref=(0,0,1) is the reference vector.

[0065] Unit vector in the y-direction of the heliostat plane Calculated using the following formula: (34) Transformation matrix from local coordinate system to global coordinate system Constructed using the following formula: (35) Transformation matrix from global coordinate system to local coordinate system Constructed using the following formula: (36) Coordinates of sampling points in the global coordinate system Calculated using the following formula: (37) in, These are the coordinates of the sampling point in the local coordinate system. The coordinates of the heliostat center in the global coordinate system.

[0066] Coordinates of sampling points in the local coordinate system Calculated using the following formula: (38) Coordinates of the heliostat center in the global coordinate system Calculated using the following formula: (39) Incident ray parameter equation This can be established using the following formula: (40) Parametric equation of reflected ray This can be established using the following formula: (41) The equation of the plane containing the secondary heliostat is determined by the following conditions: (42) The parameter t, which is the intersection point of the incident ray and the plane of the secondary heliostat, is calculated using the following formula: (43) Coordinates of the intersection point of the incident ray and the plane of the secondary heliostat Calculated using the following formula: (44) The intersection parameter k of the reflected ray and the plane of the secondary heliostat is calculated using the following formula: (45) Coordinates of the intersection point of the reflected ray and the plane of the secondary heliostat Calculated using the following formula: (46) The coordinates of the intersection point in the local coordinate system of the secondary heliostat Calculated using the following formula: (47) The condition for determining whether the intersection point is within the mirror area of ​​the secondary heliostat is: (48) Where W and H are the width and height of the secondary heliostat, For a tolerance value close to 0, This refers to the mirror plane area of ​​the secondary heliostat.

[0067] It should be noted that the coordinate system transformation and ray tracing methods described above can accurately determine whether each ray is blocked by an adjacent heliostat. When the incident or reflected ray at a sampling point is determined to be blocked, it is considered that the mirror area corresponding to that sampling point cannot effectively contribute energy. Finally, by statistically analyzing the proportion of blocked sampling points to the total number of sampling points, the shadow occlusion rate of the heliostat at the current moment is obtained.

[0068] Furthermore, step S3 also includes: recalculating the actual projection power of each heliostat by substituting the shading rate into the projection power calculation process. The shading rate in this method... Defined as the proportion of the effective reflective surface of the heliostat that is not obstructed, its value ranges from 0 to 1. The calculated shadow shading rate... Substituting the values ​​into the energy distribution function shown in formula (27), the actual projected power of each heliostat onto the selected solar collector tower can be recalculated. This correction process makes the power calculation results more consistent with the actual operating conditions of the heliostat field—previously, the assumptions made during the tower selection stage were incorrect. =1 only yields the initial power value; after correction for shading rate, a power value closer to the actual operating state is obtained. By iteratively calculating the actual projection power of all heliostats at all time points, the total output power of the mirror field at each time point is finally obtained.

[0069] Furthermore, the comprehensive performance indicators in step S4 include: First objective: Total number of heliostats; Second objective: To determine the number of time points within the preset calculation time points that meet the preset design power range; The third objective is to calculate the deviation between the average power and the target average power.

[0070] The first objective, the total number of heliostats, is directly obtained from step S2 and reflects the construction cost of the heliostat field; the fewer the heliostats, the lower the cost. The second objective, the number of time points that meet the design power range, reflects the stability of the system operation; the higher this number, the more stably the heliostat field can output the design power under all-weather conditions. The third objective, the average power deviation, reflects the power generation accuracy; the smaller the deviation, the closer the output power of the heliostat field is to the design target. These three objectives are interdependent and need to be comprehensively balanced during the optimization process.

[0071] like Figure 11 As shown, the optimization module integrates the solar module, heliostat module, mirror field layout module, and shadow occlusion module into a fitness function for a multi-objective genetic algorithm. Taking an individual as an example, step S2 generates 1250 heliostats, so the first objective value is 1250. Step S3 calculates the actual projected power of each heliostat at 100 time points and sums them to obtain the total mirror field power at each time point. Assuming the preset design power range is [45MW, 55MW], the number of times the total power falls within this range in the 100 time points is 82, so the second objective value is 82. The average power P_avg = 48.6MW is calculated for the 100 time points, and the target average power P_target = 50MW, so the third objective value is |48.6-50| = 1.4MW. The three optimization objective values ​​for this individual are 1250, 82, and 1.4, which together reflect the comprehensive performance of the layout scheme in terms of cost, stability, and accuracy.

[0072] Furthermore, the iterative optimization using a multi-objective genetic algorithm in step S5 includes: The population is initialized using layout control parameters as individual codes; Substitute each individual into steps S2 to S4 in sequence to calculate its corresponding first target value, second target value and third target value; Based on the first, second, and third target values, a non-dominated ranking is performed to determine the Pareto level for each individual; Based on the Pareto hierarchy, a new generation of population is generated through selection, crossover, and mutation operations; Repeat the iteration until the preset termination condition is met, and output the Pareto optimal solution set.

[0073] In this embodiment, the optimization variables are X, , , , There are 5 parameters, encoded with real numbers, and each individual is represented by a 5-dimensional vector. The population size is set to 100, the maximum number of iterations is 200, the crossover probability is 0.85, and the mutation probability is 0.1. During initialization, 100 individuals are randomly generated within their respective value ranges as the initial population. For each individual, steps S2 to S4 are executed sequentially to calculate its three optimization objective values.

[0074] Then, a non-dominated ranking is performed: for example, individual A's target value is (1185, 88, 1.8), individual B's target value is (1250, 82, 1.4), and individual C's target value is (1120, 83, 1.3). Comparison shows that individual C has fewer heliostats than individual B, more satisfying time points than individual B, and a smaller power deviation than individual B; therefore, individual C dominates individual B. Individuals A and C each have their own advantages and disadvantages (individual A satisfies more time points but has a larger power deviation), and do not dominate each other. Based on this Pareto dominance relationship, individuals in the population are divided into different tiers, with individuals in the same tier not dominating each other.

[0075] A tournament selection method is used to select superior individuals from the current population. Simulated binary crossover and polynomial mutation are then performed to generate a new generation. The offspring are merged with their parents using an elite preservation strategy to form a new generation. This process is repeated iteratively for 200 generations to obtain a set of Pareto optimal solutions. For example: Solution A: X = 1.5, =1.2, =1.3, =1.4, =1.5; Number of heliostats: 1185, satisfying 88 time points, power deviation: 1.8MW; Solution B: X = 1.6, =1.3, =1.4, =1.5, =1.6; Number of heliostats: 1120, satisfying 83 time points, power deviation: 1.3MW; Solution C: X = 1.7, =1.4, =1.5, =1.6, =1.7; Number of heliostats: 1065, satisfying 78 time points, power deviation: 1.1MW; Based on the actual needs of the project, a suitable layout scheme can be selected from these Pareto optimal solutions: if construction cost is a greater concern, solution C can be selected; if operational stability is a greater concern, solution A can be selected; if power generation accuracy is a greater concern, solution B can be selected. The preset termination condition can be reaching the maximum number of iterations (e.g., 200 generations), or the population not improving within several consecutive generations (i.e., the Pareto front no longer changes).

[0076] The present invention also provides a multi-tower heliostat field layout optimization system based on a multi-objective genetic algorithm, including a processor and a memory storing a computer program. When the processor executes the computer program, it implements the steps of any of the above-mentioned multi-tower heliostat field layout optimization methods based on a multi-objective genetic algorithm.

[0077] The present invention also provides a computer-readable storage medium storing a computer program adapted to be loaded and executed by a processor to implement the steps of any of the above-described multi-tower heliostat field layout optimization methods based on a multi-objective genetic algorithm.

[0078] Compared to previous methods for heliostat field layout, this invention achieves precise calculations for each heliostat at each time point and location through an innovative multi-module architecture, including tower selection, shading rate, and projection power. The heliostat module incorporates tower selection logic, automatically choosing the optimal projection tower based on real-time solar information to meet the complex scheduling requirements of multi-tower systems. The heliostat field layout module utilizes horizontal parameters X and vertical parameters... The flexible adjustment allows for flexible changes in the arrangement and number of heliostats, providing a huge space for optimization. Based on this, the multi-objective genetic algorithm takes the minimum number of heliostats, the maximum number of time points that meet the design power, and the minimum power deviation as optimization objectives, and systematically solves the problems of construction cost, operational stability and power generation accuracy in the design of the heliostat field. Compared with traditional methods, this invention has the following significant advantages: First, it adapts to multi-tower layout optimization. Currently, there is limited research on multi-tower mirror field layouts, and mainstream simulation systems such as SolarPILOT, SolTrace, and Tonatiuh can only perform single-tower simulations. Second, it offers high flexibility, operating through modular combinations. Different modules can be replaced as needed, supporting multi-tower combinations such as single-tower, double-tower, triple-tower, and quad-tower simulations. Third, it boasts high reliability, calculating the mirror field with precision down to each heliostat, determining the orientation change of each heliostat over time, and calculating its projected power. The calculation results of all heliostats form a mirror field solution set, closely matching the actual mirror field operation. Finally, relying on a multi-objective genetic algorithm, it derives multiple Pareto solutions, providing designers with various options and achieving a comprehensive balance between construction costs, operational stability, and power generation accuracy.

[0079] The present invention also provides a multi-tower heliostat field layout optimization system based on a multi-objective genetic algorithm, including a processor and a memory storing a computer program. When the processor executes the computer program, it implements the steps of any of the above-mentioned multi-tower heliostat field layout optimization methods based on a multi-objective genetic algorithm.

[0080] The present invention also provides a computer-readable storage medium storing a computer program adapted to be loaded and executed by a processor to implement the steps of any of the above-described multi-tower heliostat field layout optimization methods based on a multi-objective genetic algorithm.

[0081] It is understood that the above embodiments only illustrate preferred embodiments of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can freely combine the above technical features without departing from the concept of the present invention, and can also make several modifications and improvements, all of which fall within the protection scope of the present invention. Therefore, all equivalent transformations and modifications made with respect to the scope of the claims of the present invention should fall within the scope of the claims of the present invention.

Claims

1. A method for optimizing the layout of a multi-tower heliostat field based on a multi-objective genetic algorithm, characterized in that, Includes the following steps: Step S1: Determine the initial data required for mirror field layout optimization. The initial data includes geographical data, time data, equipment parameters, and layout control parameters. Step S2: Generate the coordinate information of each heliostat in the multi-tower system according to the layout control parameters; Step S3: Based on the initial data and heliostat coordinate information, calculate the projection power of each heliostat to the solar collector tower at each time point and the mutual shading loss between heliostats; Step S4: Determine the comprehensive performance index of the mirror field based on the projection power and the mutual occlusion loss; Step S5: Using the layout control parameters as optimization variables and the comprehensive performance index as optimization objectives, a multi-objective genetic algorithm is used to iteratively optimize the layout control parameters. When the preset termination condition is met, the optimized solution set of the multi-tower heliostat field layout is output.

2. The multi-objective genetic algorithm based layout optimization method of a heliostat field of claim 1, wherein, The layout control parameters include the lateral parameter X and the longitudinal parameter. , where i represents the region number; The step S2 of generating heliostat coordinate information includes: based on the horizontal parameter X and the vertical parameter X... A radial staggered layout and geometric mapping screening method are adopted to divide the mirror field area with the heat collection tower as the center and generate the coordinate information of each heliostat.

3. The method for optimizing the layout of a multi-tower heliostat field based on a multi-objective genetic algorithm according to claim 2, characterized in that, The radial staggered layout and geometric mapping filtering method includes the following steps: The initial layout radius is determined with the heat collection tower as the center, according to the preset restricted area; The center-to-center distance between adjacent heliostats is calculated based on the diagonal length and lateral parameter X of the heliostats. Based on the center spacing and longitudinal parameters Determine the ring spacing for each region; Based on the initial arrangement radius and ring spacing, heliostat coordinates are generated ring by ring, and heliostats are evenly arranged in the same ring according to azimuth intervals. When the gap between adjacent heliostats on the same ring is large enough to insert another heliostat, the current area division is completed, and the arrangement of the next area begins.

4. The method for optimizing the layout of a multi-tower heliostat field based on a multi-objective genetic algorithm according to claim 3, characterized in that, The center-to-center distance DM between adjacent heliostats is calculated using the following formula: in, The length of the diagonal of the heliostat. X represents the length of the heliostat, and X is the lateral parameter.

5. The multi-objective genetic algorithm based layout optimization method of a heliostat field of claim 4, wherein, The diagonal length DH of the heliostat is calculated using the following formula: wherein is the width of the heliostat, is the length of the heliostat.

6. The multi-objective genetic algorithm based layout optimization method of a heliostat field of claim 3, wherein, The interloop distance of each region This is calculated by the following equation: in, For vertical parameters, This represents the center-to-center distance between adjacent heliostats.

7. The multi-objective genetic algorithm based layout optimization method of a heliostat field of claim 3, wherein, The azimuth interval Calculated using the following formula: wherein, is the center-to-center spacing of adjacent heliostats, is the starting layout radius, i represents the zone number.

8. The multi-objective genetic algorithm based layout optimization method of a heliostat field of claim 3, wherein, The judgment condition for completion of the area division is that the number of heliostats arranged on the current ring n satisfies n x DM = 2pi R, wherein R is the radius of the current ring; the starting radius of the next area after completion of the current area division .

9. The multi-objective genetic algorithm based layout optimization method of a heliostat field of claim 1, wherein, The calculation of the projection power of each heliostat onto the solar collector tower in step S3 includes: According to the geographical data and the time data, the solar irradiance DNI, the solar elevation angle , the solar azimuth angle and the solar position vector S are calculated for each time point; For each heliostat, calculate its projection power onto each collector tower, and select the collector tower with the highest projection power as the actual projection tower for that heliostat.

10. The multi-objective genetic algorithm based layout optimization method of a heliostat field of claim 9, wherein, The solar position vector S is calculated using the following formula: 。 11. The multi-objective genetic algorithm based layout optimization method of a heliostat field of claim 9, wherein, For coordinates ( , , A heliostat with coordinates () , , For a solar collector tower, the projected power is calculated using the following steps: Calculate the distance from the heliostat to the solar collector: wherein, H is the height of the heliostat base; Calculate the vector of reflected sunlight: Calculate the unit normal vector of the heliostat: Where S is the sun's position vector; Calculate the cosine efficiency of the heliostat: 。 12. The multi-objective genetic algorithm based layout optimization method of a heliostat field of claim 11, wherein, The atmospheric attenuation factor is also included when calculating the projected power. The atmospheric attenuation factor Calculated using the following formula: 。 13. The method for optimizing the layout of a multi-tower heliostat field based on a multi-objective genetic algorithm according to claim 9, characterized in that, When calculating the projected power of the heliostat onto the solar collector tower, a two-dimensional Gaussian distribution is used to describe the energy distribution of the reflected light spot on the collector surface. The two-dimensional Gaussian distribution function F(x,y) is: wherein, is the heliostat reflectivity, is the shadow obscuration, , is the spot center coordinate, is the total beam error standard deviation.

14. The multi-objective genetic algorithm based layout optimization method of a heliostat field of claim 13, wherein, The two-dimensional Gaussian distribution function is numerically integrated on the collector receiving surface using the two-dimensional Simpson integral method to calculate the total power I projected onto the collector by the heliostat.

15. The method according to claim 1, wherein, The calculation of mutual shading loss between heliostats in step S3 includes: The sampling point ray tracing method is used to uniformly select multiple sampling points on the reflecting surface of the main heliostat; A search circle is set with the main heliostat as the center, and other heliostats that fall within the range of the search circle are regarded as auxiliary heliostats; Determine whether the incident and reflected rays passing through each sampling point are blocked by the secondary heliostat; The shading rate of the primary heliostat is calculated based on the ratio of the number of obscured sampling points to the total number of sampling points. .

16. The multi-objective genetic algorithm based layout optimization method of a heliostat field of claim 15, wherein, Determining whether light is blocked by the secondary heliostat includes: Transform the coordinates of the sampling points from the local coordinate system of the primary heliostat to the global coordinate system; Establish the parameter equations for the incident ray and the reflected ray passing through the sampling point in the global coordinate system; Calculate the coordinates of the intersection points of the incident ray and the reflected ray with the plane containing the secondary heliostat; Transform the coordinates of the intersection point from the global coordinate system to the local coordinate system of the secondary heliostat; Determine whether the coordinates of the intersection point are within the mirror surface of the secondary heliostat. If so, determine that the corresponding ray of the sampling point is blocked.

17. The method according to claim 1, wherein, The comprehensive performance indicators in step S4 include: First objective: Total number of heliostats; Second objective: To determine the number of time points within the preset calculation time points that meet the preset design power range; The third objective is to calculate the deviation between the average power and the target average power.

18. The multi-objective genetic algorithm based layout optimization method of a heliostat field of claim 17, wherein, The iterative optimization using a multi-objective genetic algorithm in step S5 includes: The population is initialized using the layout control parameters as the individual codes; Substitute each individual into steps S2 to S4 in sequence to calculate its corresponding first target value, second target value and third target value; Based on the first target value, the second target value, and the third target value, a non-dominated ranking is performed to determine the Pareto level of each individual; Based on the Pareto hierarchy, a new generation of population is generated through selection, crossover, and mutation operations; Repeat the iteration until the preset termination condition is met, and output the Pareto optimal solution set.

19. A multi-tower heliostat field layout optimization system based on a multi-objective genetic algorithm, comprising a processor and a memory having stored therein a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the multi-tower heliostat field layout optimization method based on a multi-objective genetic algorithm as described in any one of claims 1 to 18.

20. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program adapted to be loaded and executed by a processor to implement the steps of the multi-tower heliostat field layout optimization method based on a multi-objective genetic algorithm as described in any one of claims 1-18.

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