Heliostat field layout optimization method, equipment, medium and product

Abstract

The invention discloses a heliostat field layout optimization method and device, a medium and a product, and relates to the field of photo-thermal power generation. The method comprises the following steps of: discretizing a solar cone into a plurality of light rays, simulating a propagation path of the light rays in a heliostat field by adopting a Monte Carlo ray tracing method, and further calculating shadow shielding efficiency and heat collector cutoff efficiency; calculating the comprehensive optical efficiency of the single-sided heliostats in the heliostat field according to the shadow shielding efficiency and the heat collector cut-off efficiency, and further calculating the annual average optical efficiency and the annual average output heat power of the heliostat field; the annual average optical efficiency and the annual average output thermal power are used as objective functions of a non-dominated sorting genetic algorithm, heliostat field layout is optimized, and a Pareto optimal solution set is obtained; and a weighted maximum satisfaction method is adopted to make a decision, and an optimal layout scheme of the heliostat field is selected from the Pareto optimal solution set. By improving the accuracy of optical efficiency calculation in the heliostat field layout optimization process, the energy efficiency of the heliostat field is maximized.

Description

Technical Field

[0001] This application relates to the field of concentrated solar power generation technology, and in particular to a method, equipment, medium and product for optimizing the layout of heliostat fields. Background Technology

[0002] Traditional calculations of the optical efficiency of heliostat fields typically assume that sunlight is parallel and ignore the influence of the sun's cone angle. Based on this, traditional methods often employ geometric analysis or simplified statistical models to simulate the light propagation path. These methods struggle to accurately reflect the actual propagation path of light within the heliostat field, as well as complex effects such as multiple reflections and shading. This leads to significant deviations in the estimation of shadow shading and truncation efficiencies, consequently affecting the accuracy of optical efficiency calculations during heliostat field layout optimization and ultimately preventing the achievement of maximum energy efficiency. Summary of the Invention

[0003] The purpose of this application is to provide a method, device, medium, and product for optimizing the layout of heliostat fields, so as to improve the accuracy of optical efficiency calculation during the optimization process of heliostat field layout, and thus maximize the energy efficiency of the field.

[0004] To achieve the above objectives, this application provides the following solution.

[0005] Firstly, this application provides a method for optimizing the layout of a heliostat field, including: The solar cone is discretized into multiple rays, and the Monte Carlo ray tracing method is used to simulate the propagation path of the rays in the heliostat field. The shading efficiency and collector cutoff efficiency are calculated based on the propagation path of light in the heliostat field. The overall optical efficiency of a single-sided heliostat in the heliostat field is calculated based on the shading efficiency and the collector cutoff efficiency. The annual average optical efficiency and annual average output heat power of the heliostat field are calculated based on the comprehensive optical efficiency of a single-sided heliostat. The annual average optical efficiency and annual average output thermal power of the heliostat field are used as the objective functions of the non-dominated sorting genetic algorithm. The non-dominated sorting genetic algorithm is used to optimize the layout of the heliostat field to obtain the Pareto optimal solution set. The layout of the heliostat field includes the position, size, installation height and number of heliostats. The weighted maximum satisfaction method is used for decision-making, and the optimal layout scheme of the heliostat field is selected from the Pareto optimal solution set.

[0006] Optionally, the discretization of the solar cone into multiple rays and the simulation of the ray propagation path in the heliostat field using the Monte Carlo ray tracing method specifically includes: By discretizing in the radial and circumferential directions, the conical sunlight is discretized into multiple rays, each ray containing the same amount of solar radiation energy; For each ray, randomly generate the incident direction and construct the incident ray direction vector; A point is randomly selected on a single-sided heliostat as the reflection point. The propagation path of the incident ray direction vector is traced, and the direction vector of the reflected ray and the normal vector of the heliostat surface are calculated.

[0007] Optionally, the calculation of the shading efficiency and collector cutoff efficiency based on the propagation path of light in the heliostat field specifically includes: For a single-sided heliostat, the plane equation of the plane containing the heliostat is established based on the normal vector of the heliostat mirror surface; By solving the equations of the plane and the straight line equation of the incident ray direction vector, the projection point of each incident ray direction vector on the plane is obtained. If a projection point exists, it is determined that the corresponding incident ray is not blocked. Count the number of unblocked rays and use the formula Calculate shadow occlusion efficiency ;in This represents the total number of incident rays; The propagation path of the reflected light direction vector is traced and the number of light rays that eventually reach the solar collector is counted. ; Using formula Calculate the cutoff efficiency of the solar collector .

[0008] Optionally, the calculation of the overall optical efficiency of a single-sided heliostat in the heliostat field based on the shading efficiency and the collector cutoff efficiency specifically includes: Based on shadow occlusion efficiency and collector cutoff efficiency Using formula Calculate the overall optical efficiency of a single-sided heliostat in a heliostat field. ;in Cosine efficiency; Atmospheric transmission efficiency; This refers to the specular reflection efficiency.

[0009] Optionally, the step of calculating the annual average optical efficiency and annual average output heat power of the heliostat field based on the comprehensive optical efficiency of the single-sided heliostat specifically includes: According to the first of the year The 21st of the month At the moment of the first Overall optical efficiency of a heliostat Using formula Calculate the annual average optical efficiency of the heliostat field. Using formula Calculate the annual average output heat power of the heliostat field. ;in This represents the total number of heliostats in the heliostat field; For the first The 21st of the month Normal direct irradiance at a given moment; This represents the area of ​​a single-sided heliostat.

[0010] Optionally, the following constraints need to be met when optimizing the heliostat field layout using a non-dominated sorting genetic algorithm: Heliostat size constraints ;in and These are the height and width of a single-sided heliostat, in units of [units missing]. ; Minimum Spacing Constraint ;in and These are two adjacent heliostats. and Position coordinates; Installation height constraints ;in The installation height of the heliostat, in units of 1 / 2000. ; Installation distance constraints ;in The coordinates of the absorption tower's location; and Heliostat field boundary constraints ;in Let be the radius of the circumcircle of the heliostat field.

[0011] Optionally, the step of using the weighted maximum satisfaction method for decision-making to select the optimal layout scheme of the heliostat field from the Pareto optimal solution set specifically includes: For the Pareto optimal solution set, the th There are non-dominated solutions, using the formula... Calculate the first Standardized satisfaction of a nondominated solution ;in For the first The nondominated solution corresponds to the th . The satisfaction level of each objective function; For the first The weights corresponding to each objective function; The number of objective functions; The number of non-dominated solutions; Choose Order The heliostat field layout scheme corresponding to the largest nondominated solution is taken as the optimal heliostat field layout scheme.

[0012] In a second aspect, the present application provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the heliostat field layout optimization method.

[0013] In a third aspect, the present application provides a computer readable storage medium, having stored thereon a computer program, which, when executed by a processor, implements the heliostat field layout optimization method.

[0014] In a fourth aspect, the present application provides a computer program product, comprising a computer program, which, when executed by a processor, implements the heliostat field layout optimization method.

[0015] According to the specific embodiments provided by the present application, the present application discloses the following technical effects.

[0016] In the heliostat field layout optimization method, device, medium and product provided by the present application, the sunlight cone is first discretized into multiple light rays, and the propagation path of the light rays in the heliostat field is simulated by the Monte Carlo ray tracing method, so as to accurately calculate the shadow blocking efficiency and the collector truncation efficiency, and then obtain the annual average optical efficiency and the annual average output thermal power of the heliostat field. Further, taking the annual average optical efficiency and the annual average output thermal power as the optimization target, the non-dominated sorting genetic algorithm is adopted to dynamically adjust the layout of the heliostat position, size, installation height and number, so as to maximize the energy efficiency of the mirror field. Through the light cone modeling and the high-efficiency light ray tracing, the present application significantly improves the accuracy of the optical efficiency and the thermal power calculation, and the optimized mirror field layout can reduce the shadow blocking loss by more than 10%, and the unit mirror power is increased by 8% to 12%, which is suitable for the design and operation optimization of different mirror field scales and concentrating technologies (such as tower type and dish type) of solar thermal power stations. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0018] Figure 1 It is a flowchart of the heliostat field layout optimization method of the present application; Figure 2 It is a schematic diagram of the discretization of the conical light rays; Figure 3 It is a schematic diagram of the mirror coordinate system of a single-sided heliostat; Figure 4 It is a schematic diagram of the blocking of the incident light rays by adjacent heliostats; Figure 5 This is a schematic diagram of the propagation path of a cone-shaped light ray; Figure 6 This is a schematic diagram of the visualization results of the Pareto optimal solution set in the embodiments of this application; Figure 7 This is a schematic diagram of the visualization results of the optimal layout scheme of the heliostat field in the embodiments of this application; Figure 8 This is a physical diagram of the actual engineering layout according to the optimal layout scheme of the heliostat field in the embodiments of this application. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] This application proposes a method, equipment, medium, and product for optimizing the layout of heliostat fields, aiming to improve the accuracy of optical efficiency calculation during the optimization process of heliostat field layout, thereby maximizing the energy efficiency of the field.

[0021] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0022] In one exemplary embodiment, such as Figure 1 As shown, this application provides a method for optimizing the layout of a heliostat field, including the following steps 1 to 6.

[0023] Step 1: Discretize the solar cone into multiple rays and use Monte Carlo ray tracing to simulate the propagation path of the rays in the heliostat field.

[0024] This application establishes a two-dimensional normal distribution model of the solar light cone based on light cone energy flux density modeling and Monte Carlo ray tracing, and simulates the propagation path of light in the heliostat field to accurately calculate the shading efficiency and collector cutoff efficiency. Step 1 specifically includes steps 1.1 to 1.3.

[0025] Step 1.1: Discretize the conical sunlight into multiple rays by discretizing in the radial and circumferential directions, with each ray containing the same amount of solar radiation energy.

[0026] like Figure 2 As shown, firstly, the solar cone is discretized radially, and the radius of the base of the cone is... do equally divided, the length of each part Secondly, along the bottom surface circumferential direction discrete, the bottom surface circumferential of the cone light is divided into equally, the central angle of each part Thus, the cone light is discretized into light rays.

[0027] For the cone reflected light of a point on the first surface of the heliostat, the cone angle of the light beam formed by the radial th part and the circumferential th part is: (1); where is a constant related only to sunlight, =4.65 mrad (milliradians).

[0028] Assuming that the light distribution within the sunlight cone conforms to a two-dimensional normal distribution, its probability density function can be expressed as: (2); where and are the coordinates within the light cone, and are the standard deviations in the and directions, is the correlation coefficient between and . In the sunlight cone model, it is usually assumed that =0, i.e., the distributions in the and directions are independent. The standard deviations and can be determined according to the cone angle of the sunlight cone and the size of the heliostat. For example, it can be assumed that the standard deviations are proportional to the light cone angle: = = (3); where is a proportionality coefficient that can be adjusted according to actual conditions.

[0029] The above two-dimensional normal distribution model (2) of the sunlight cone assumes that the distribution of light within the light cone is continuous, but in reality, light is discrete, so it is necessary to approximate the real distribution situation by a large number of random light rays.

[0030] Step 1.2: Randomly generate the incident direction for each light ray, and construct the incident light ray direction vector.

[0031] A large number of incident directions of random light rays are generated using a two-dimensional normal distribution model (2). This can be achieved by a random number generator, for example, the randn function in MATLAB can be used to generate random numbers conforming to the normal distribution. The incident direction is randomly generated for each light ray, which can be calculated according to the solar elevation angle, solar azimuth angle, heliostat pitch angle, and heliostat azimuth angle. For the conical incident light ray, since its tangent angle does not exceed 0.0043, a random incident light ray direction vector can be generated within its range. According to the geometric relationship, the incident light ray direction vector of the first (4); wherein, is the solar elevation angle; is the solar azimuth angle; is the incident light ray direction vector of the first heliostat.

[0032] Step 1.3: Randomly select a point on the single-sided heliostat as the reflection point, trace the propagation path of the incident light ray direction vector, and calculate the reflected light ray direction vector and the heliostat mirror normal vector.

[0033] A point is randomly selected on the current heliostat as the reflection point, and the reflection light ray equation can be calculated by tracing the light ray propagation path. Combined with the generated incident light ray direction vector, the normal vector of the heliostat plane can be solved.

[0034] Specifically, the reflected light ray through the center of the heliostat always points to the center of the collector, and the reflected light ray direction vector is: (5); wherein, = is the collector center coordinate, is the absorption tower height; = is the center coordinate of the first heliostat, is the coordinate in the X and Y directions, is the installation height of the first heliostat. is the reflected light ray direction vector of the first heliostat.

[0035] The heliostat mirror normal vector of the first heliostat is: (6).​

[0036] The role of tracking the light propagation path is to count the trajectories of multiple light rays, to calculate two factors in the optical efficiency of the heliostat, i.e. the shadow blocking efficiency and the collector truncation efficiency, so as to further provide a theoretical basis for the heliostat field efficiency and layout optimization.

[0037] Step 2: Calculate the shadow blocking efficiency and the collector truncation efficiency according to the propagation path of the light in the heliostat field.

[0038] By tracking the propagation path of the light in the heliostat field, it can be judged whether the light is blocked by the adjacent heliostat, and then the shadow blocking efficiency and the collector truncation efficiency are calculated. The step 2 specifically comprises the following steps 2.1 to 2.5.

[0039] Step 2.1: For a single-sided heliostat, a plane equation of the plane where the heliostat is located is established based on the normal vector of the heliostat mirror surface.

[0040] For the convenience of subsequent calculation, for a single-sided heliostat, a mirror coordinate system is established as shown in the figure. For any one flat mirror (single-sided heliostat), the center of the heliostat is taken as the coordinate origin O, the normal vector passing through the origin O is taken as the positive direction of the Z axis, the vector passing through the origin and perpendicular to the pitch axis and eastward is taken as the positive direction of the X axis, and the vector passing through the origin and parallel to the pitch axis and northward is taken as the positive direction of the Y axis. Then for a single-sided heliostat, the coordinates of its four vertices Figure 3 、 、 and in the mirror coordinate system are respectively: 、 、 and . (7); wherein, and respectively represent the height and width of the single-sided heliostat.

[0041] Further, the conversion relationship between the mirror coordinate system and the mirror field coordinate system needs to be established to obtain the coordinates of the four vertices of the heliostat in the mirror field coordinate system. For a heliostat with a pitch angle of and an azimuth angle of , its mirror coordinate system only needs to be rotated counterclockwise by around the X axis, then rotated counterclockwise by around the Z axis, and then a corresponding translation vector is added to obtain the mirror field coordinate system. Thus the coordinates in the mirror field coordinate system can be obtained: (8); wherein, ​= 1, 2, 3, 4 correspond to the four vertices; and are the coordinates of the vertices in the mirror coordinate system and the mirror field coordinate system, respectively. is the coordinate of the center of the heliostat in the mirror field coordinate system, is the rotation matrix generated by the rotation of the coordinate axis around the Z axis, is the rotation matrix generated by the rotation of the coordinate axis around the X axis. is the pitch angle of the heliostat, is the azimuth angle of the heliostat.

[0042] For the heliostat , assuming its unit normal vector is and passing through the center of the heliostat , its plane equation is: (9).

[0043] Step 2.2: By simultaneously solving the plane equation and the straight line equation of the incident light direction vector, the projection point of each incident light direction vector on the plane is solved, and if there is a projection point, it is determined that the corresponding incident light is not blocked.

[0044] Figure 4 The blocking of incident light by adjacent heliostats is shown. Referring to Figure 4 , for the incident light, the heliostat causes blocking to the heliostat , then the four vertices of the heliostat are projected along the incident light to the plane where the heliostat is located to determine the incident light blocking area. Specifically, by simultaneously solving the plane equation (9) and the straight line equation (4) of the incident light direction vector, the projection point of each incident light direction vector on the plane can be solved, and if there is a projection point, it is determined that the corresponding incident light is not blocked.

[0045] Step 2.3: Count the number of unblocked light rays and calculate the shadow blocking efficiency using formula (10): (10); where is the total number of incident light rays.

[0046] Step 2.4: Track the propagation path of the reflected light direction vector and count the number of light rays that finally reach the collector .

[0047] ​Figure 5 The propagation path of the cone light is shown. The cone incident light reaches the plane mirror of the heliostat, and is reflected to the collector receiving plane. Due to the angle problem, part of the reflected light finally reaches the collector and is received by the collector, and the other part of the reflected light that cannot reach the collector is the overflow part. The propagation path of the reflected light direction vector is tracked, and the number of light rays finally reaching the collector , that is, the number of light rays received by the collector, is counted.

[0048] Step 2.5: Calculate the collector truncation efficiency using formula (11) : (11).

[0049] So far, the process of simulating the propagation path of light in the heliostat field by the Monte Carlo ray tracing method and further calculating the shadow blocking efficiency and the collector truncation efficiency can be roughly summarized as follows: initialize the sun position and mirror field parameters → generate incident light direction vectors → emit light to mirror sampling points → shadow blocking detection → update → calculate reflected light direction vectors → truncation efficiency detection → update .

[0050] Step 3: Calculate the comprehensive optical efficiency of a single heliostat in the heliostat field according to the shadow blocking efficiency and the collector truncation efficiency.

[0051] Combine the shadow blocking efficiency , the cosine efficiency , the atmospheric transmission efficiency , the collector truncation efficiency and the mirror reflection efficiency to calculate the comprehensive optical efficiency of a single heliostat in the heliostat field : (12).

[0052] Where, when the incident light is inclined to the plane where the heliostat is located, there is an angle , between the central normal line of the heliostat and the incident light, which will cause a certain energy loss, that is, the cosine loss. The cosine efficiency is calculated as follows: (13).

[0053] When the light propagates in the atmosphere, it may be affected by the atmospheric composition and water vapor content, resulting in a certain degree of energy attenuation. The atmospheric transmission efficiency is related to the distance from the center of the mirror to the center of the collector, and its calculation formula is: (14).

[0054] Specular reflection efficiency Depending on factors such as the material and cleanliness of the mirror, in most cases, the mirror's reflectivity can be taken as a constant, with a value of 0.92.

[0055] Step 4: Calculate the annual average optical efficiency and annual average output heat power of the heliostat field based on the overall optical efficiency of the single-sided heliostat.

[0056] By calculating the combined optical efficiency of all heliostats at five specific times on the 21st of each month throughout the year, and then summing and averaging the results, the annual average optical efficiency of the heliostat field can be obtained. : (15); in, Indicates the first of the year The 21st of the month At the moment of the first The overall optical efficiency of the heliostat; . This represents the total number of heliostats in the heliostat field.

[0057] Average annual output heat power of heliostat field for: (16); in, For the first The 21st of the month Normal direct irradiance at a given moment; This represents the area of ​​a single-sided heliostat.

[0058] Step 5: Using the annual average optical efficiency and annual average output thermal power of the heliostat field as the objective function of the non-dominated sorting genetic algorithm, the layout of the heliostat field is optimized to obtain the Pareto optimal solution set.

[0059] This application employs NSGA-II (Non-dominated sorting genetic algorithm) for multi-objective optimization. Before using the NSGA-II algorithm to optimize the heliostat field layout, it is necessary to first set the constraints that the algorithm needs to satisfy during the optimization process, including heliostat size constraints, minimum spacing constraints, installation height constraints, installation distance constraints, and heliostat field boundary constraints.

[0060] In a heliostat field, each heliostat has the same dimensions, meaning each heliostat has the same height and width, and the height and width are between 2 meters and 8 meters, with the height not exceeding the width. Therefore, the dimensional constraints of the heliostats are as follows: (17); wherein and are the height and width of a single heliostat, respectively, in meters.

[0061] To avoid mechanical collision between adjacent heliostats, the distance between heliostats and adjacent heliostats needs to be more than 5m than the mirror width, so there is a minimum spacing constraint as follows: (18) wherein and are the position coordinates of two adjacent heliostats and , here only the X-axis and Y-axis coordinates are used.

[0062] To prevent ground blockage, mirror height limitation is needed. The installation height of each heliostat is the same, and satisfies that the mirror height is between 2m and 6m; and the mirror should not touch the ground when rotating around the horizontal axis, that is, the installation height is greater than half of the mirror height, so the installation height constraint is as follows: (19); wherein is the installation height of the heliostat, in meters.

[0063] Since no heliostat is installed within 100m of the absorption tower, the installation distance constraint is as follows: (20); wherein is the position coordinate of the absorption tower, which is usually located at the center of the heliostat field, and the collector is located on the absorption tower. For example, for a circular heliostat field layout, the absorption tower is located at the center of the circle.

[0064] Since the heliostat is located within the mirror field range, the distance from any one heliostat to the absorption tower should be greater than the maximum radius of the mirror field , so the boundary of the heliostat field is constrained as follows: (21); Since the heliostat field is not necessarily a regular circular layout, the is set as the radius of the circumscribed circle of the heliostat field, that is, the maximum radius of the mirror field.

[0065] The present application uses a non-dominated sorting genetic algorithm to optimize the layout of the heliostat field, which specifically includes the following steps 5.1 to step 5.8. ​​

[0066] Step 5.1: Set parameters and initialize the population.

[0067] The parameters are set as follows: maximum number of generations T=50, population size Q=50, crossover probability Pc=0.9, mutation probability Pm=0.1. An initial population is randomly generated, and each individual (chromosome) in the population represents a solution, which consists of decision variables. For the heliostat field layout optimization in this application, the decision variables include: 1) heliostat size parameters, including the height of the heliostat. and width 1) Affects the reflective area; 2) Installation height information of each heliostat, including the installation height of the heliostat. This typically affects reflection efficiency and shading relationships; 3) the spatial coordinates of each heliostat, i.e. Coordinates, usually the specific distribution on a plane, indicate where each heliostat is placed; 4) Number of heliostats, that is, the total number of heliostats in the heliostat field. Determine the total number of heliostats needed. Generate several chromosomes as the initial population, then calculate the number of iterations and iteration rate for each chromosome, and add them to the end of each chromosome.

[0068] Step 5.2: Calculate fitness: For each individual in the population, calculate its value under each objective function, and then determine the individual's non-dominated level and crowding level based on these values.

[0069] To facilitate subsequent selection operations, the non-dominated rank and crowding degree of individuals in the initial population are calculated, and a fast non-dominated sorting is performed. The non-dominated rank and crowding degree of an individual are related to the value of the objective function. In this application, the annual average optical efficiency calculation formula (15) and the annual average output thermal power calculation formula (16) of the heliostat field are used as the objective function of the non-dominated sorting genetic algorithm.

[0070] Step 5.3: Selection Operation: Based on an individual's non-dominance rank and crowding level, select individuals to enter the next generation of the population. Generally, individuals with higher non-dominance rankings and higher crowding levels are more likely to be selected.

[0071] The selection process employs a binary competition mechanism. First, the mating pool size is set to 0.5Q. Then, each time, two individuals are randomly selected from the parent population to participate in the competition. Their ranks are compared, and the higher-ranking individual (smaller rank value) is returned. If the ranks are the same, crowding is further calculated and compared, returning the individual with the larger value. Crowding is an operator introduced by NSGA-II to ensure solution diversity. At different stages of the algorithm, the crowding operator guides the selection results towards a uniformly distributed Pareto front.

[0072] Step 5.4: Crossover operation: selected individuals are subjected to crossover operation to generate new individuals. Crossover operation is achieved by combining the decision variables of two individuals.

[0073] Step 5.5: Mutation operation: the newly generated individuals are subjected to mutation operation to increase the diversity of the population. Mutation operation is achieved by randomly changing some decision variables of the individuals.

[0074] Step 5.6: Merge populations: the parent population and the offspring population are merged to form a new population.

[0075] Step 5.7: Repeat steps 5.2 to 5.6: repeat the steps of calculating fitness, selection, crossover, mutation, and merging populations until the preset termination condition is reached, such as reaching the maximum number of iterations or the degree of convergence of the population.

[0076] Step 5.8: Output results: the final output is the Pareto optimal solution set, i.e. the solution set that is not dominated by other solutions in the multi-objective optimization problem.

[0077] The variables in the layout optimization process of the heliostat field in this application are decision variables, which are unknowns of the problem and need to be determined through the optimization process to obtain their optimal values. The optimization result is usually represented as a Pareto optimal solution set, i.e. a set of solutions that are not dominated by other solutions in the multi-objective optimization problem. Each solution consists of the values of the decision variables and the corresponding objective function values. In practical applications, one or more solutions need to be selected from the Pareto optimal solution set as the final optimization result.

[0078] In an exemplary embodiment, the non-dominated sorting genetic algorithm is used to optimize the layout of the heliostat field, and the specific process of obtaining the Pareto optimal solution set is as follows.

[0079] First step: random or experience-based layout, determine the initial values of some parameters; give the initial values of the tower position coordinates, heliostat size, installation height, and number of heliostats, and perform large step traversal within the range of the respective data dimensions.

[0080] Second step: calculate the heliostat position; for the parameters determined in the first step, use the NSGA-II algorithm to solve, for the given parameters in one traversal, calculate the maximum annual average optical efficiency per unit mirror area under the constraint condition and the annual average output power If it is greater than the current optimal annual average optical efficiency and annual average output thermal power, it is replaced, and the optimal annual average optical efficiency and annual average output thermal power are recorded. By changing the parameters such as the size of the heliostat, the installation height, the spatial position coordinates, the number of heliostats, etc., the algorithm is brought into the calculation of the optimal annual average optical efficiency and the optimal annual average output thermal power, so that the algorithm can sort and select the solution according to the calculated objective function value, so as to evolve a better parameter combination, and thus determine the optimal mirror field layout.

[0081] Each time the algorithm is iterated, the parameters are brought into formulas (15) and (16) for each individual variable combination, and MATLAB is run to calculate the corresponding annual average optical efficiency and annual average output thermal power, and the iteration is performed to the Pareto optimal solution set. Each Pareto non-dominated solution (also known as a frontier solution) in the Pareto optimal solution set is specifically mapped to a layout scheme, including the heliostat position , the heliostat installation height , the heliostat size , and , the number of heliostats , etc.

[0082] In this exemplary embodiment, the visualization result of the Pareto optimal solution set is shown in Figure 6 , with units of meters. Figure 6 Each cross in the figure represents the position of a heliostat, the coordinates are randomly simulated; the size of the cross indicates the mirror size, and the larger the cross, the larger the cross; the color of the cross indicates the installation height , and the color from light to dark corresponds to 2m to 6m; the number of crosses of the same size and color is the number of heliostats . Further according to the actual demand, the most suitable layout scheme (such as the scheme with the highest comprehensive satisfaction) is selected from the Pareto frontier solution, that is, the "optimal layout" of the heliostat field.

[0083] Step 6: Decision-making is performed by using the weighted maximum satisfaction method to select the optimal layout scheme of the heliostat field from the Pareto optimal solution set.

[0084] After obtaining the Pareto optimal solution set of the problem by the NSGA-II algorithm, a final scheme needs to be obtained from it by multi-attribute decision-making. The weighted maximum satisfaction method is selected for decision-making in the present application. According to the fuzzy satisfaction function, the satisfaction of each non-dominated solution to each objective function is calculated, and the one with the maximum comprehensive satisfaction is the final scheme. For the th non-dominated solution in the Pareto optimal solution set, the normalized satisfaction of the non-dominated solution is: (twenty two); in, Indicates the first Standardized satisfaction of a nondominated solution; The number of non-dominated solutions. For the first The nondominated solution corresponds to the th . The satisfaction level of each objective function; For the first The weights corresponding to each objective function; The number of objective functions in this application =2, which correspond to the annual average optical efficiency and annual average output thermal power of the heliostat field, respectively. Formula (22) will be adjusted according to the weights. The two objective functions are combined into a weighted objective function to measure the "goodness" of the solution. This is achieved by adjusting the weights. Decision-makers can prioritize different objectives and adjust the characteristics of the final mirror field layout. In this application, optical efficiency directly reflects the heliostat's ability to effectively reflect and focus sunlight onto the collector. Higher efficiency theoretically results in better power generation performance. Total heat generation power (proportional to the annual average output heat generation power) represents the thermal energy collected by the collector, determining the amount of energy utilized and the input power of the thermodynamic cycle. Since the total heat generation power objective has a significant impact on the success of the scheme among the two objective functions, weights are assigned to the annual average optical efficiency and the annual average output heat generation power objective functions. The values ​​are 0.3 and 0.7 respectively.

[0085] After calculating the standardized satisfaction level of the non-dominated solution, choose to let The heliostat field layout scheme corresponding to the largest nondominated solution is taken as the optimal heliostat field layout scheme, which includes the specific location distribution of each heliostat, the optimal installation height, the optimal size, and the optimal number of heliostats.

[0086] In this exemplary embodiment, the visualization results of the optimal layout scheme of the heliostat field are as follows: Figure 7 As shown, Figure 7 The x and y coordinates represent the deployment range of the heliostat field, in meters; each circle represents one heliostat. Figure 7 The position of the central heliostat is the optimal layout position.

[0087] The method described in this application was applied to a 100MW concentrated solar power plant. The optimal layout scheme for the heliostat field determined by this method was used for the actual engineering layout, as shown in the following diagram. Figure 8As shown, the heliostat field center is the receiver tower. After optimization, the annual average optical efficiency is increased from 58.2% to 65.1%, the unit mirror power is increased from 412 W / m² to 78 W / m², the shadow blocking loss is reduced by 11.3%, and the truncation efficiency is increased by 8.7%.

[0088] The method of the present application first establishes a two-dimensional normal distribution model of the sunlight cone, simulates the propagation path of light in the heliostat field by the Monte Carlo ray tracing method, accurately calculates the shadow blocking efficiency and the collector truncation efficiency, combines the cosine efficiency, the atmospheric transmission efficiency and the mirror reflection efficiency to obtain the comprehensive optical efficiency of the heliostat field. Further, taking the annual average optical efficiency and the annual average output thermal power as the optimization objectives, the non-dominated sorting genetic algorithm is used to dynamically adjust the layout of the heliostat position, size, installation height and number, etc. to maximize the energy efficiency of the mirror field. The present application significantly improves the accuracy of optical efficiency calculation through light cone modeling and efficient ray tracing. The optimized mirror field layout can reduce the shadow blocking loss by more than 10%, and the unit mirror power can be increased by 8% to 12%. The present application is suitable for the design and operation optimization of solar thermal power stations of different mirror field scales and concentrating technologies (such as tower type and dish type), especially for tower type solar thermal power stations. Tower type thermal power generation has high energy conversion efficiency, and the collector has high operating temperature. At the same time, the short working fluid stroke can reduce energy loss. Moreover, due to the high temperature of tower type thermal power generation, it has the condition to set up a heat storage system to reduce the dependence on nature for solar power generation, ensure more stable power supply within a certain range, and ensure the quality of on-grid power.

[0089] In an exemplary embodiment, the present application also provides a computer device which can be a server or a terminal. The computer device includes a processor, a memory, an input / output interface and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through network connection. The computer program is executed by the processor to implement the heliostat field layout optimization method.

[0090] In an exemplary embodiment, the present application also provides a computer readable storage medium having a computer program stored thereon, which is executed by the processor to implement the heliostat field layout optimization method.

[0091] In one example embodiment, the application also provides a computer program product comprising a computer program which, when executed by a processor, implements the heliostat field layout optimization method.

[0092] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by computer program instruction related hardware, and the computer program can be stored in a non-volatile computer readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiment methods. Any reference to memory or other medium in the embodiments provided by the application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magneto resistive memory (MRAM), ferroelectric memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0093] It should be noted that the information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant regulations.

[0094] The technical features of the above embodiments can be combined in any way. To make the description concise, not all possible combinations of the technical features in the above embodiments are described, but as long as the combination of the technical features does not exist contradictory, it should be considered as the scope of the present application.

[0095] The principles and implementation manners of the present application are described herein by using specific examples, and the above examples are only used to help understand the method of the present application and its core idea; meanwhile, for those skilled in the art, according to the idea of the present application, the specific implementation manners and application ranges will have changes. In conclusion, the content of the specification should not be understood as a limitation of the present application.

Claims

1. A method for optimizing the field layout of a heliostat, characterized in that, include: The solar cone is discretized into multiple rays, and the Monte Carlo ray tracing method is used to simulate the propagation path of the rays in the heliostat field. The shading efficiency and collector cutoff efficiency are calculated based on the propagation path of light in the heliostat field. The overall optical efficiency of a single-sided heliostat in the heliostat field is calculated based on the shading efficiency and the collector cutoff efficiency. The annual average optical efficiency and annual average output heat power of the heliostat field are calculated based on the comprehensive optical efficiency of a single-sided heliostat. The annual average optical efficiency and annual average output thermal power of the heliostat field are used as the objective functions of the non-dominated sorting genetic algorithm. The non-dominated sorting genetic algorithm is used to optimize the layout of the heliostat field to obtain the Pareto optimal solution set. The layout of the heliostat field includes the position, size, installation height and number of heliostats. The weighted maximum satisfaction method is used for decision-making, and the optimal layout scheme of the heliostat field is selected from the Pareto optimal solution set.

2. The method for optimizing the field layout of heliostats according to claim 1, characterized in that, The discretization of the solar cone into multiple rays and the simulation of the ray propagation path in the heliostat field using the Monte Carlo ray tracing method specifically include: By discretizing in the radial and circumferential directions, the conical sunlight is discretized into multiple rays, each ray containing the same amount of solar radiation energy; For each ray, a random incident direction is generated, and an incident ray direction vector is constructed. A point is randomly selected on a single-sided heliostat as the reflection point. The propagation path of the incident ray direction vector is traced, and the direction vector of the reflected ray and the normal vector of the heliostat surface are calculated.

3. The method for optimizing the heliostat field layout according to claim 2, characterized in that, The calculation of shadow blocking efficiency and collector cutoff efficiency based on the propagation path of light in the heliostat field specifically includes: For a single-sided heliostat, the plane equation of the plane containing the heliostat is established based on the normal vector of the heliostat mirror surface; By solving the equations of the plane and the straight line equation of the incident ray direction vector, the projection point of each incident ray direction vector on the plane is obtained. If a projection point exists, it is determined that the corresponding incident ray is not blocked. Count the number of unblocked rays and use the formula Calculate shadow occlusion efficiency ;in This represents the total number of incident rays; The propagation path of the reflected light direction vector is traced and the number of rays that eventually reach the solar collector is counted. ; Using formula Calculate the cutoff efficiency of the solar collector .

4. The method for optimizing the heliostat field layout according to claim 3, characterized in that, The calculation of the overall optical efficiency of a single-sided heliostat in the heliostat field based on shading efficiency and collector cutoff efficiency specifically includes: Based on shadow occlusion efficiency and collector cutoff efficiency Using formula Calculate the overall optical efficiency of a single-sided heliostat in a heliostat field. ;in Cosine efficiency; Atmospheric transmission efficiency; This refers to the specular reflection efficiency.

5. The method for optimizing the field layout of heliostats according to claim 4, characterized in that, The calculation of the annual average optical efficiency and annual average output heat power of the heliostat field based on the comprehensive optical efficiency of a single-sided heliostat specifically includes: According to the first of the year The 21st of the month At the moment of the first Overall optical efficiency of a heliostat Using formula Calculate the annual average optical efficiency of the heliostat field. Using formula Calculate the annual average output heat power of the heliostat field. ;in This represents the total number of heliostats in the heliostat field; For the first The 21st of the month Normal direct irradiance at a given moment; This represents the area of ​​a single-sided heliostat.

6. The method for optimizing the field layout of heliostats according to claim 5, characterized in that, When optimizing the heliostat field layout using a non-dominated sorting genetic algorithm, the following constraints must be met: Heliostat size constraints ;in and These are the height and width of a single-sided heliostat, in units of [units missing]. ; Minimum Spacing Constraint ;in and These are two adjacent heliostats. and Position coordinates; Installation height constraints ;in The installation height of the heliostat, in units of 1 / 2000. ; Installation distance constraints ;in These are the coordinates of the absorption tower's location; as well as Heliostat field boundary constraints ;in Let be the radius of the circumcircle of the heliostat field.

7. The method for optimizing the field layout of heliostats according to claim 6, characterized in that, The decision-making process employs a weighted maximum satisfaction method, selecting the optimal layout scheme for the heliostat field from the Pareto optimal solution set. Specifically, this includes: For the Pareto optimal solution set, the th There are non-dominated solutions, using the formula... Calculate the first Standardized satisfaction of a nondominated solution ;in For the first The nondominated solution corresponds to the th . The satisfaction level of each objective function; For the first The weights corresponding to each objective function; The number of objective functions; The number of non-dominated solutions; Choose Order The heliostat field layout scheme corresponding to the largest nondominated solution is taken as the optimal heliostat field layout scheme.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the heliostat field layout optimization method according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the heliostat field layout optimization method as described in any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the heliostat field layout optimization method as described in any one of claims 1 to 7.

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