A heliostat target point genetic optimization method suitable for tower type concentrating solar power system

Abstract

The application belongs to the technical field of heliostat target point adjustment method, and particularly relates to a heliostat target point genetic optimization method suitable for a tower type light concentration and heat collection system. The method comprises the following steps: S1. Calculating heat absorber energy flow: dividing the surface of the heat absorber into a target point matrix, calculating the light spot distribution of each heliostat at a given target point on the heat absorber, and calculating the energy flow density distribution formed by the superposition of the light spots of the m heliostats in the mirror field on the heat absorber; S2. Target point genetic optimization: randomly generating S target point strategies as the initial population, generating a group of individuals with higher fitness of the target point strategy through random selection, crossover and mutation, evolving the group, and obtaining the optimized target point aiming strategy, so that the mirror field incident energy flow is uniformly distributed on the surface of the heat absorber. The method provided by the application adopts a target point genetic optimization algorithm to optimize the aiming strategy of the tower type light concentration and heat collection system, so that the mirror field incident energy flow is uniformly distributed on the surface of the heat absorber.

Description

Technical Field

[0001] This invention belongs to the technical field of heliostat target point adjustment methods, specifically relating to a genetic optimization method for heliostat target points applicable to tower-type concentrating solar collector systems. Background Technology

[0002] In tower-type concentrating solar collectors, the receiver plays a crucial role in converting concentrated light from the heliostat field into heat. During this conversion, uneven temperature distribution on the receiver's receiving surface can lead to localized overheating, resulting in receiver performance degradation and damage. The temperature distribution on the receiver surface is primarily determined by factors such as receiver design, thermophysical properties, heat transfer fluid, and energy flux density distribution. Among these, energy flux density is closely related to the selection of the target point for the heliostat. Optimizing the target point requires finding a balance between uniform energy flux density distribution and minimizing overflow losses, ensuring the energy flux density distribution remains within the receiver's allowable range while minimizing overflow losses.

[0003] CN201110241387.6 A method for adjusting the target point of a heliostat is proposed. This method is suitable for adjusting the target point of a heliostat when the receiver is locally overheated. This method is a local target point optimization algorithm for the receiver after local overheating caused by uneven energy flow distribution. It cannot optimize the target point for the entire field.

[0004] CN202210389917.X A method for determining the target point of a heliostat during preheating of a tower solar thermal power plant uses a nonlinear model to optimize the z-coordinate (vertical) of the target point. This method suffers from the problems of few optimization variables and a small search space, and is prone to getting trapped in local optima. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a genetic optimization method for the target point of a heliostat in a tower-type concentrating solar collector system; the method is an optimization aiming strategy for the tower-type concentrating solar collector system so that the incident energy flow of the mirror field is uniformly distributed on the surface of the receiver.

[0006] This invention is achieved through the following technical solution:

[0007] A genetic optimization method for target points of heliostats applicable to tower-type concentrating solar collectors, the method comprising:

[0008] S1. Calculate the energy flow of the receiver: Divide the surface of the receiver into a matrix of target points, calculate the spot distribution of each heliostat at a given target point on the receiver, and calculate the energy flow density distribution formed by the superposition of the spot distributions of the m heliostats in the receiver.

[0009] S2. Target point genetic optimization: S target point strategies are randomly generated as the initial population. Through random selection, crossover and mutation, a group of individuals with higher fitness target point strategies are generated, and the population evolves to obtain an optimized target point aiming strategy so that the incident energy flow of the mirror field is evenly distributed on the surface of the receiver.

[0010] Furthermore, step S1 specifically includes:

[0011] S1.1. Divide the heat absorption panel of the heat absorber into an edge area and a target area, wherein the target points are arranged in the target area, and the target area is divided into equal intervals. A matrix of target points; a given target point is set for each heliostat;

[0012] S1.2. Calculate the light spot distribution of each heliostat at a given target point on the receiver:

[0013] ;

[0014] Among them, F h (x, y) is the energy flux density distribution function of the light spot, and (x, y) is the coordinate of the receiver panel. It is the effective deviation of the heliostat spot. P represents the coordinates of the corresponding target point on the receiver for the heliostat; h It is the total reflected energy of a single heliostat;

[0015] P h The calculation formula is:

[0016] ;

[0017] In the formula, Direct solar irradiance, unit: kW / m² 2 A m The area of ​​the heliostat's reflecting mirror, in meters (m²). 2 w is the angle between the sunlight and the normal to the surface of the heliostat; Atmospheric attenuation factor; The reflectivity of the heliostat;

[0018] It is the result of the convolution of four Gaussian error functions, calculated using the following formula:

[0019] ;

[0020] In the formula, This is due to a deviation in the shape of the sun. This refers to beam quality error; Astigmatism error; To track the error, the value is taken as 1~3 mrad; D is the distance between the center of the heliostat and the coordinates of the corresponding given target point on the receiver of the heliostat to be measured; Let be the cosine of the angle between the incident ray and the normal to the absorber surface;

[0021] Beam quality error With surface shape error related

[0022] ;

[0023] Among them, surface shape error The value range is 1~2 mrad; surface shape error The standard deviation of the difference between the slope of the mirror surface and the surface slope of the ideal mirror shape;

[0024] Astigmatism error Represented as:

[0025] ;

[0026] in, H t It is the height of the heliostat's spot; W s is the width of the heliostat spot; D is the distance between the center of the heliostat and the coordinates of the corresponding target point of the heliostat to be measured on the absorber.

[0027] ;

[0028] ;

[0029] In the formula, f represents the focal length, and d is the square root of the heliostat's area; cos w It is the cosine of the angle between the incident ray of the sun and the normal of the heliostat;

[0030] S1.3. The energy flux density distribution F(x,y) formed by the superposition of the light spots of the m-plane heliostats in the entire mirror field on the receiver is calculated by the following formula.

[0031] .

[0032] Furthermore, step S2 specifically includes:

[0033] S2.1. Randomly generate S target point strategies, each individual being a two-dimensional matrix corresponding to the heliostat and the target point, as the initial population; the two-dimensional matrix is ​​as follows:

[0034] ;

[0035] Based on the target point strategy of each individual, step S1 is used to generate an energy flow distribution map;

[0036] S2.2. Calculate the fitness of each individual: The fitness function is used to calculate the influence of energy flow distribution uniformity and spillover loss. The fitness function formula is as follows:

[0037] ;

[0038] in, The standard deviation of the surface energy flux of the receiver; This is the overflow loss;

[0039] Based on the calculated fitness of each individual, determine whether the fitness function meets the set termination threshold. If it does, terminate the algorithm and output the result; otherwise, proceed to step S2.3.

[0040] S2.3. Based on the selection probability Pt, select individuals with high fitness from the current population to form group S1;

[0041] S2.4. The number of individuals c to be crossed is controlled by the crossover probability Pc. C individuals are randomly selected from the population S1 and paired to perform random column crossover operations to generate new individuals to replace the original individuals. A second crossover probability Pcc is introduced to control the number of columns to be randomly exchanged, and population S2 is obtained.

[0042] S2.5. Mutation Operation: Based on the number of mutations m determined by the mutation rate Pm, randomly select m individuals from population S2, perform mutation operations on each individual, introduce a second mutation probability Pmm to control the number of mutations per individual, and replace the original individuals with the newly generated individuals to obtain population S3:

[0043] ;

[0044] S2.6. Determine if the number of population iterations is equal to the set maximum number of iterations M. If it is, terminate the algorithm and output the result; otherwise, return to step 2.2.

[0045] Furthermore, in step S2.2, the energy flow standard deviation The calculation formula is as follows:

[0046] ;

[0047] The target area on the surface of the heat absorber is divided into: A matrix of target points and Let the coordinates of the target point matrix be on the receiver. The average energy flow over the surface of the absorber.

[0048] Furthermore, in step S2.2, the overflow loss The calculation formula is as follows:

[0049] ;

[0050] in, The total energy output by the mirror field. This represents the total energy received by the absorber panel.

[0051] Furthermore, in S2.3, a probability proportional to fitness is used to determine the number of individuals that each individual inherits into the next generation population. The specific steps are as follows:

[0052] (1) First, calculate the fitness of all individuals. The sum ;

[0053] (2) Calculate the relative fitness of each individual. ;

[0054] (3) Calculate the cumulative probability of fitness for each individual. ;

[0055] (4) Generate a random number between 0 and 1, and select an individual based on the position of the random number in the probability region.

[0056] Beneficial technical effects of the present invention:

[0057] The heliostat target point genetic optimization method for tower concentrating solar collector systems provided by this invention uses a genetic algorithm to optimize the aiming strategy for the tower concentrating solar collector system so that the incident energy flux of the mirror field is uniformly distributed on the surface of the receiver. The main objective is to minimize the standard deviation of the energy flux density distribution and reduce overflow loss, which is accomplished by multiple flux density measurement points on the surface of the receiver.

[0058] The genetic optimization method for target points of heliostats in tower-type concentrating solar collectors provided by this invention requires no manual intervention and has a high degree of automation. The proposed fitness function simultaneously considers the issues of uniformity of energy flux density distribution and minimization of overflow loss, thus protecting the receiver's safety while improving the concentrating efficiency of the mirror field.

[0059] The heliostat target point genetic optimization method for tower concentrating solar collectors provided by this invention adopts the GA algorithm, which has good global search capability and its inherent parallelism can easily accelerate the solution speed in a multi-processor or distributed environment. Attached Figure Description

[0060] Figure 1 This is a flowchart of a heliostat target point genetic optimization method applicable to a tower-type concentrating solar collector system, as described in an embodiment of the present invention.

[0061] Figure 2This is a diagram showing the mirror field arrangement in an embodiment of the present invention;

[0062] Figure 3 This is a distribution map of target points in an embodiment of the present invention;

[0063] Figure 4 The iterative optimization curve is shown in the embodiment of this invention;

[0064] Figure 5a This is an initial energy flow distribution diagram in an embodiment of the present invention;

[0065] Figure 5b This is the optimized energy flow distribution diagram in an embodiment of the present invention. Detailed Implementation

[0066] 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 and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0067] Conversely, this invention encompasses any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of the invention as defined in the claims. Furthermore, to provide a better understanding of the invention, certain specific details are described in detail below. However, those skilled in the art will fully understand the invention even without these detailed descriptions.

[0068] This invention provides a genetic optimization method for target points of heliostats in tower-type concentrating solar collector systems, the method comprising:

[0069] S1. Calculate the absorber energy flow: Divide the absorber surface into... The target point matrix is ​​calculated, the light spot distribution of each heliostat at a given target point on the receiver is calculated, and the energy flux density distribution formed by the superposition of the light spots of the m heliostats on the receiver is calculated.

[0070] S2. Target point genetic optimization: S target point strategies are randomly generated as the initial population. Through random selection, crossover and mutation, a group of individuals with higher fitness target point strategies are generated, and the population evolves to obtain an optimized target point aiming strategy so that the incident energy flow of the mirror field is evenly distributed on the surface of the receiver.

[0071] This step utilizes phenomena such as replication, crossover, and mutation that occur in biological systems through natural selection and heredity. S target point strategies are randomly generated as the initial population. Through random selection, crossover, and mutation operations, a group of individuals with higher fitness target point strategies is produced, allowing the population to evolve into increasingly better regions in the search space. This process continues generation after generation until it converges to a group of individuals that are best adapted to the environment, thereby obtaining a high-quality solution to the problem.

[0072] In this embodiment, step S1 specifically includes:

[0073] S1.1. Divide the heat absorption panel of the heat absorber into an edge area and a target area. The target points are only located in the target area. Divide the target area into equal-distance sections. A matrix of target points; a given target point is set for each heliostat;

[0074] S1.2. Calculate the light spot distribution of each heliostat at a given target point on the receiver:

[0075] ;

[0076] Among them, F h (x, y) It is the energy flux density distribution function of the light spot. (x, y) are the coordinates of the receiver panel, see Figure 3 , It is the effective deviation of the heliostat spot. P represents the coordinates of the corresponding target point on the receiver for the heliostat; h It is the total reflected energy of a single heliostat;

[0077] P h The calculation formula is:

[0078] ;

[0079] In the formula, Direct solar irradiance, unit: kW / m² 2 A m The area of ​​the heliostat's reflecting mirror, in meters (m²). 2 w is the angle between the sunlight and the normal to the surface of the heliostat; Atmospheric attenuation factor; The reflectivity of the heliostat;

[0080] It is the result of the convolution of four Gaussian error functions, calculated using the following formula:

[0081] ;

[0082] In the formula, This is due to a deviation in the shape of the sun. For beam quality error; Astigmatism error refers to any additional distortion of the reflected light when the incident light ray is not parallel to the mirror normal; To track the error, the value is taken as 1~3 mrad; D is the distance between the center of the heliostat and the coordinates of the corresponding given target point on the receiver of the heliostat to be measured; Let be the cosine of the angle between the incident ray and the normal to the absorber surface;

[0083] in This is an empirical value, set to 0; tracking error The tracking accuracy of the heliostat in the T / GRLM 02-2014 National Solar Thermal Industry Technology Innovation Strategic Alliance standard is defined as the angle between the rotation center point of the heliostat and the geometric center point of the pattern formed by sunlight converging on the target surface through the heliostat, and the average geometric center point of the pattern during the test period. It is expressed by the root mean square (RMS) of its multiple measurements.

[0084] Beam quality error with surface shape error related

[0085] ;

[0086] Among them, surface shape error The value range is 1~2 mrad; surface shape error The standard deviation of the difference between the slope of the mirror surface and the surface slope of the ideal mirror shape;

[0087] Astigmatism error Represented as:

[0088] ;

[0089] in, H t It is the height of the heliostat's spot; W s is the width of the heliostat spot; D is the distance between the center of the heliostat and the coordinates of the corresponding target point of the heliostat to be measured on the absorber.

[0090] ;

[0091] ;

[0092] In the formula, f represents the focal length, and d is the square root of the heliostat's area; cos w It is the cosine of the angle between the incident ray of the sun and the normal of the heliostat;

[0093] S1.3. The energy flux density distribution F(x,y) formed by the superposition of the light spots of the m-plane heliostats in the entire mirror field on the receiver is calculated by the following formula.

[0094] .

[0095] In this embodiment, step S2 specifically includes:

[0096] S2.1. Randomly generate S target point strategies, each individual being a two-dimensional matrix corresponding to the heliostat and the target point, as the initial population: the two-dimensional matrix is ​​as follows:

[0097] ;

[0098] Based on the target point strategy of each individual, step S1 is used to generate an energy flow distribution map;

[0099] S2.2. Calculate the fitness of each individual: In the genetic algorithm, the choice of the fitness function determines the final optimization result. In the optimization of the target point, it is necessary to find a balance between uniform energy flow distribution and minimizing overflow loss. The fitness function is used to consider the influence of uniform energy flow distribution and overflow loss. The fitness function formula is as follows:

[0100] ;

[0101] in, The standard deviation of the surface energy flux of the receiver; This is the overflow loss; When the exponent is greater than 1, it can increase the weight of the overflow loss. The specific value can be adjusted according to the actual scenario.

[0102] Energy Flow Standard Deviation The calculation formula is as follows:

[0103] ;

[0104] The target area on the surface of the heat absorber is divided into: A matrix of target points and Let the coordinates of the target point matrix be on the receiver. The average energy flow over the surface of the receiver;

[0105] Overflow loss The calculation formula is as follows:

[0106] ;

[0107] in, The total energy output by the mirror field. This represents the total energy received by the absorber panel.

[0108] S2.3. Selection (replication) operation: Based on the selection probability Pt (in this embodiment, Pt is set to 1 to ensure that the number of individuals in each generation remains unchanged), individuals with high fitness are selected from the current population to form population S1;

[0109] The probability proportional to fitness is used to determine the number of each individual's traits that are passed on to the next generation. The specific steps are as follows:

[0110] (1) First, calculate the total fitness of all individuals. .

[0111] (2) Calculate the relative fitness of each individual. .

[0112] (3) Calculate the cumulative probability of fitness for each individual. .

[0113] (4) Generate a random number between 0 and 1, and select an individual based on which probability range the random number appears in.

[0114] S2.4. Crossover (Mating) Operation: This step is the main operation in the genetic algorithm for generating new individuals. The number of individuals c to be crossed is controlled by the crossover probability Pc (generally 0.4 to 0.99). c individuals are randomly selected from S1 and paired up for random column crossover to generate new individuals to replace the original individuals. Considering that changing the target point of a single heliostat will not significantly affect the energy flux density distribution, a second difference probability Pcc (ranging from 5% to 10%) is introduced to control the number of columns of random exchange, resulting in population S2:

[0115] ;

[0116] S2.5. Mutation Operation: This step is another operation to generate new individuals. Based on the mutation rate Pm (generally 0.0001 to 0.1), m individuals are randomly selected from S2 and subjected to mutation operations. Considering that changing the target point of a single heliostat will not significantly affect the energy flux density distribution, a second mutation probability Pmm (ranging from 1% to 5%) is introduced to control the number of mutations per individual. The generated new individuals replace the original individuals, resulting in population S3.

[0117] ;

[0118] S2.6. Termination judgment: If the condition (number of iterations, usually 200~500) is met, the algorithm terminates; otherwise, return to step 2.

[0119] This invention is illustrated using a small concentrating solar thermal field as an example. The mirror field arrangement is shown in Figure 1. The heliostat is a rectangular heliostat with dimensions of 2.3m × 1.07m and a total of 126 surfaces. The central tower is 60m high. The absorber panel is shown in Figure 2 and has dimensions of 12 × 10m. The target points are distributed in the central area within 2m of the edge of the absorber panel, with a total of 9 × 7 target points.

[0120] The heliostat target point genetic optimization method provided by this invention, applicable to tower-type concentrating solar collectors, was used for optimization. The maximum number of iterations was set to 50, and no fitness threshold was set to terminate the iteration prematurely. Figure 4 The iteration curve is given. The growth of fitness value can be divided into two stages. In the first 20 iterations, the fitness value grows faster and fluctuates more. In the last 30 iterations, the fitness value increases slowly. Figure 5a and Figure 5b The energy flow distribution diagrams before and after optimization are presented. Before optimization, the energy flow distribution was uneven, mainly manifested in the center point of the contour lines deviating from the geometric center of the receiver panel, the energy flow gradient on the right side being large, and the energy flow gradient on the left side being small. After optimization, the energy flow distribution is more uniform, mainly manifested in the symmetrical distribution of the energy flow contour lines around the geometric center of the receiver panel, and the uniform distribution of the intervals of the energy flow contour lines (reflecting the energy flow gradient).

[0121] Genetic algorithms (GA), as one of the most popular optimization techniques, have been found to be very useful in solving complex real-world design optimization problems because they face a set of candidate solutions rather than a single point in the search space. As long as the diversity of the population is well preserved, it can avoid getting stuck in local optima. The GA algorithm mimics the process of natural evolution to find the best solution to the optimization problem. Genetic operators such as mutation and crossover are applied to the candidate population. The best candidate is selected at the end of each generation by measuring the fitness function. This process is repeated for several generations until the best solution is found. The optimization algorithm can be summarized as follows: (1) The initial population is randomly generated. (2) The fitness function is evaluated for each population. (3) Offspring are generated by applying genetic operators (i.e., crossover and mutation) to individuals selected probabilistically from the initial population. (4) The fitness function is evaluated for the offspring. (5) A new population is selected, and the algorithm continues from step (2). This invention uses a genetic algorithm to optimize the aiming strategy for a tower-type concentrating solar collector system so that the incident energy flow of the mirror field is uniformly distributed on the surface of the receiver. The primary objective is to minimize the standard deviation of the energy flux density distribution and reduce overflow losses, which is achieved by multiple flux density measurement points on the surface of the receiver.

[0122] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A genetic optimization method for target points of heliostats suitable for tower-type concentrating solar collector systems, characterized in that, The method includes: S1. Divide the heat absorption panel of the heat absorber into an edge area and a target area, with target points arranged in the target area, and then divide the target area into equal intervals. A matrix of target points; a given target point is set for each heliostat; Calculate the light spot distribution of each heliostat at a given target point on the receiver; The energy flux density distribution F(x,y) formed by the superposition of the light spots of the entire m-plane heliostat in the receiver is calculated by the following formula. ； Among them, F h (x, y) is the energy flux density distribution function of the light spot, and (x, y) is the coordinate of the receiver panel; S2. Target Point Genetic Optimization: S target point strategies are randomly generated as the initial population. Through random selection, crossover, and mutation, a group of individuals with higher fitness target point strategies is generated, allowing the population to evolve and obtain an optimized target point aiming strategy to ensure that the incident energy flow from the mirror field is uniformly distributed on the surface of the receiver. Step S2 is specifically as follows: S2.

1. Randomly generate S target point strategies, each individual being a two-dimensional matrix corresponding to the heliostat and the target point, as the initial population; the two-dimensional matrix is ​​as follows: ； Based on the target point strategy of each individual, step S1 is used to generate an energy flow distribution map; S2.

2. Calculate the fitness of each individual: The fitness function is used to calculate the influence of energy flow distribution uniformity and spillover loss. The fitness function formula is as follows: ； in, The standard deviation of the surface energy flux of the receiver; This is the overflow loss; Based on the calculated fitness of each individual, determine whether the fitness function meets the set termination threshold. If it does, terminate the algorithm and output the result; otherwise, proceed to step S2.

3. S2.

3. Based on the selection probability Pt, select individuals with high fitness from the current population to form group S1; In step S2.2, the standard deviation of energy flow The calculation formula is as follows: ； The target area on the surface of the heat absorber is divided into: A matrix of target points and Let the coordinates of the target point matrix be on the receiver. The average energy flow across the absorber surface; Overflow loss The calculation formula is as follows: ； in, The total energy output by the mirror field. This represents the total energy received by the absorber panel.

2. The genetic optimization method for heliostat target points applicable to tower-type concentrating solar collectors according to claim 1, characterized in that, The energy flux density distribution function F of the light spot h (x, y): ； Where (x, y) are the coordinates of the receiver panel. It is the effective deviation of the heliostat spot. P represents the coordinates of the corresponding target point on the receiver for the heliostat; h It is the total reflected energy of a single heliostat; P h The calculation formula is: ； In the formula, Direct solar irradiance, unit: kW / m² 2 A m The area of ​​the heliostat's reflecting mirror, in meters (m²). 2 w is the angle between the sunlight and the normal to the surface of the heliostat; The atmospheric attenuation factor has a value range of 0 to 1, where 0 indicates that all light is absorbed by the atmosphere and 1 indicates that all light is reflected to the target point. The reflectivity of the heliostat; It is the result of the convolution of four Gaussian error functions, calculated using the following formula: ； In the formula, The sun's shape is deviated; For beam quality error; Astigmatism error; To track the error, the value is taken as 1~3mrad; D is the distance between the center of the heliostat and the coordinates of the corresponding given target point on the receiver of the heliostat to be measured; Let be the cosine of the angle between the incident ray and the normal to the absorber surface; Beam quality error With surface shape error related, ； Among them, surface shape error The value range is 1~2 mrad; surface shape error The standard deviation of the difference between the slope of the mirror surface and the surface slope of the ideal mirror shape; Astigmatism error Represented as: ； in, H t It is the height of the heliostat's spot; W s is the width of the heliostat spot; D is the distance between the center of the heliostat and the coordinates of the corresponding target point of the heliostat to be measured on the absorber. ； ； In the formula, f represents the focal length, and d is the square root of the heliostat's area; cos w It is the cosine of the angle between the incident ray of the sun and the normal of the heliostat.

3. The genetic optimization method for heliostat target points applicable to tower-type concentrating solar collectors according to claim 1, characterized in that, Step S2 also includes: S2.

4. The number of individuals c to be crossed is controlled by the crossover probability Pc. C individuals are randomly selected from the population S1 and paired to perform random column crossover operations to generate new individuals to replace the original individuals. A second crossover probability Pcc is introduced to control the number of columns to be randomly exchanged, and population S2 is obtained. S2.

5. Mutation Operation: Based on the number of mutations m determined by the mutation rate Pm, randomly select m individuals from population S2, perform mutation operations on each individual, introduce a second mutation probability Pmm to control the number of mutations per individual, and replace the original individuals with the newly generated individuals to obtain population S3: ； S2.

6. Determine if the number of group iterations is equal to the set maximum number of iterations M. If it is, terminate the algorithm and output the result; otherwise, return to step 2.

2.

4. The genetic optimization method for target points of heliostats in a tower-type concentrating solar collector system according to claim 1, characterized in that, In S2.3, a probability proportional to fitness is used to determine the number of individuals that each individual inherits into the next generation population. The specific steps are as follows: (1) First, calculate the fitness of all individuals. The sum ; (2) Calculate the relative fitness of each individual. ; (3) Calculate the cumulative probability of fitness for each individual. ; (4) Generate a random number between 0 and 1, and select an individual based on the position of the random number in the probability region.

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