A mirror field arrangement method suitable for a tower type photo-thermal multi-tower machine

Abstract

The application relates to the field of tower type light and heat mirror field arrangement, and discloses a mirror field arrangement method suitable for a tower type light and heat multi-tower machine, which comprises the following steps: gridizing a mirror field common area and excluding a grid with an excessive slope based on a digital elevation model; calculating a heliostat contribution value of each grid to screen high-value grids; adopting a light track tracking algorithm to calculate energy contribution values of heliostats in each grid to different heat absorbing towers at each time; defining arrangement variables and dynamic attribution variables, establishing an integer nonlinear programming model with the maximum unit cost heat output income as a target; solving the model to obtain an optimal heliostat arrangement scheme; and determining a final mirror field layout after correction through concentric circles or spiral lines and annual time sequence simulation verification. The application overcomes efficiency loss caused by traditional experience estimation by globally optimizing the heliostat position and dynamically scheduling the multiple towers, can effectively reduce the number of heliostats, improve the light condensation efficiency and the investment income rate, and is suitable for mirror field design of two-tower, three-tower and multi-tower machine light and heat power stations.

Description

Technical Field

[0001] This invention relates to a method for arranging a tower-type photothermal mirror field, and particularly to a mirror field arrangement method suitable for a multi-tower, single-unit tower-type photothermal system. Background Technology

[0002] Tower-type concentrated solar power (CSP) technology, with its advantages of high concentration ratio, strong adaptability to thermal storage, and good power generation stability, has become the mainstream technology in the CSP field. Currently, commercially operated tower-type CSP plants generally adopt a "one tower, one unit" model, where a single absorber tower corresponds to one set of concentrators and a power generation island. This model has a simple structure and clear control logic, but as project scale expands and cost reduction and efficiency improvement requirements increase, its inherent shortcomings are gradually becoming apparent: 1. Limited light-gathering efficiency: When heliostats are arranged in a ring or fan shape around a single tower, the shading and shadow loss of heliostats in the edge areas increase significantly, resulting in low overall light-gathering efficiency of the mirror field. 2. Difficulty in increasing capacity: Due to the upper limit of the absorber's energy flow and the site's topography, the number of heliostats and the focusing area of ​​a single tower mirror field are difficult to expand further, resulting in a natural bottleneck in system capacity.

[0003] To overcome these limitations, the industry has begun exploring "multi-tower, one-machine" collaborative concentrating technology, where multiple receiver towers share a single power generation island, and attempts are being made to allow some heliostats to switch focusing between different receiver towers (heliostat sharing mode). However, existing "multi-tower, one-machine" mirror field arrangement methods still have the following drawbacks: 1. Typically, the mirror field is designed independently for each tower and then simply spliced ​​together. The heliostats have a fixed ownership and cannot be dynamically relocated across towers. 2. Even if the sharing of heliostats is considered, the shared area and proportion rely heavily on empirical estimates (e.g., about 5% to 10% of heliostats in the central area can be shared), lacking quantitative optimization basis; 3. The overall light-gathering efficiency still has a loss margin of 5% to 8% compared to the ideal state, and the investment cost of the mirror field is relatively high.

[0004] Therefore, there is an urgent need for a mirror field arrangement method that can quantitatively optimize the heliostat layout and realize dynamic synergistic concentrating of multiple towers, so as to further improve the economy and concentrating efficiency of solar thermal power plants. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a mirror field arrangement method suitable for multi-tower, single-unit tower-type solar thermal systems. This method aims to minimize the number of heliostats, improve concentrating efficiency, and increase heat output per unit cost by globally optimizing the heliostat layout and dynamically assigning and scheduling them.

[0006] To achieve the above objectives, the technical solution of the present invention is as follows: A method for mirror field arrangement suitable for a single tower-type solar thermal multi-tower unit includes the following steps: Step 1: Divide the common area in the multi-tower, single-machine heliostat field into grids, calculate the slope of each grid using a digital elevation model, and exclude grids whose slope exceeds the adjustment limit of the heliostat support. Step 2: Based on the grid retained in Step 1, calculate the heliostat contribution value of each grid, and set a threshold to retain only grids with heliostat contribution values ​​greater than the threshold. Step 3: On the grid screened in Step 2, based on the light tracing algorithm, calculate the energy contribution of the heliostat to different heat absorption towers in each grid at each time point; Step 4: Define decision variables Indicates whether it is in the grid Install heliostats and define auxiliary variables. Indicates at time Grid Does the internal heliostat reflect light to the heat absorber? ; Step 5: Based on the variables calculated in Step 3 and Step 4, establish an integer nonlinear programming model with the goal of maximizing the heat output revenue per unit cost. The model includes energy demand constraints, energy upper limit constraints, attribution relationship constraints, and variable value range constraints. Step Six: Solve the integer nonlinear programming model to obtain the optimal decision variables. Thus, the arrangement scheme of the mirror field is obtained.

[0007] In the above scheme, in step one, a rectangular frame is used to select the area where the two outermost rings of heliostats in each mirror field are located as the common area in the mirror field. The common area is divided into a uniform grid, and each grid can accommodate at most one heliostat.

[0008] In the above scheme, in step one, the digital elevation model is acquired through UAV oblique photography or LiDAR.

[0009] In the above scheme, the calculation method for the heliostat contribution value of the grid in step two is as follows: ; in, The contribution value of the heliostat to the grid; The distance from the center of the grid to the heat absorber tower. The azimuth angle between the grid center and the heat absorption tower; Only when Only then is it permissible to place heliostats on the grid; threshold for The 75th percentile of the function.

[0010] In the above scheme, in step four, if =1, which means in the grid Installing a heliostat, if =0 indicates that in the grid No heliostats are installed.

[0011] In the above scheme, the integer nonlinear programming model is as follows: ; in, , , ; The constraints are as follows: ; ; ; , ; in, For a moment Grid Internal heliostat for heat absorption tower Energy contribution value, These are weighting coefficients. and These represent the lower and upper limits of the heat power received by the heat absorber on the heat absorber at time t, respectively. The cost of a single heliostat h, This represents the total number of heat absorption towers. This represents the number of grid cells after filtering in step two.

[0012] In the above scheme, step six uses the particle swarm optimization algorithm to solve the integer nonlinear programming model.

[0013] In a further technical solution, after step six, the following is also included: Step 7: Force the heliostat arrangement obtained from the solution to be arranged in concentric circles or spirals.

[0014] In a further technical solution, after step seven, the following is also included: Step 8: Perform a full-year time-series simulation verification based on typical meteorological year data. If the annual heat collection does not meet the requirements, adjust the threshold in Step 2 and recalculate iteratively.

[0015] Preferably, the multi-tower, single-machine structure is a three-tower, single-machine structure.

[0016] Through the above technical solution, the mirror field arrangement method for multi-tower single-unit tower-type solar thermal power generation provided by the present invention has the following beneficial effects: 1. Global optimization, breaking through the limitations of experience. This invention, based on an integer nonlinear programming model, unifies the modeling of the layout of a multi-tower, single-machine heliostat field with the dynamic allocation and scheduling of heliostats, replacing the traditional model of "independent design of each tower and shared empirical estimation." By solving the global optimization problem, the optimal placement of the heliostats and the collaborative focusing strategy among multiple towers are obtained, resulting in an overall focusing efficiency improvement of more than 5% compared to existing solutions, eliminating the 5% to 8% efficiency loss margin caused by empirical estimation.

[0017] 2. Significantly reduce the investment cost of mirror fields By introducing the density distribution function By pre-screening the grid and eliminating low-value areas in advance, and then optimizing it with "heat output benefit per unit cost" as the objective function, the total number of heliostats can be minimized while meeting the energy requirements of each heat absorber tower, thereby reducing the construction cost of the mirror field and improving the return on investment of the power plant.

[0018] 3. Significantly reduces computational complexity and is feasible in engineering. Using density threshold (e.g., 75th percentile) reduces the solution space from N to ( This method combines particle swarm optimization to solve integer nonlinear programming problems, significantly reducing computational resource consumption. Furthermore, subsequent steps refine the mathematical solution into a concentric circle or spiral arrangement, facilitating engineering construction and site leveling, thus balancing theoretical optimization with practical engineering applications.

[0019] 4. Adaptable to various operating conditions and highly robust. This invention supports dynamic switching of heliostats between different receiver towers (via variables). (This system) allows for real-time adjustment of the solar concentration distribution strategy based on solar irradiance at time t, the energy requirements of each tower, and the upper limit of the receiver's energy flow. Through time-series simulations using typical meteorological data from throughout the year, threshold parameters can be iteratively optimized to ensure the long-term, efficient operation of the deployment scheme under real-world climatic conditions.

[0020] 5. Wide range of applications and strong expandability This method is not only applicable to three-tower-one-machine structures, but can also be extended to mirror field arrangements of any number of towers and one machine (such as two-tower, four-tower, etc.). At the same time, by introducing a digital elevation model (DEM), it can adapt to different terrains such as plains and hills, overcome the strict requirements of traditional methods on site flatness, and expand the application scenarios of tower solar thermal power generation technology. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below.

[0022] Figure 1This is a schematic diagram of a mirror field arrangement method for a multi-tower, single-unit solar thermal system disclosed in an embodiment of the present invention.

[0023] Figure 2 A schematic diagram of the grid division of the common area of ​​the three towers and one machine mirror field. Detailed Implementation

[0024] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0025] This embodiment uses a three-tower, one-unit tower-type solar thermal power plant as an example, but the invention is also applicable to other multi-tower, one-unit structures such as two-tower and four-tower plants. Figure 1 As shown, a mirror field arrangement method suitable for a multi-tower, single-unit tower-type solar thermal system includes the following steps: Step 1: Meshing and Terrain Preprocessing First, determine the common area of ​​the three-tower, one-machine mirror field, that is, the common connection part of the mirror fields corresponding to the three heat absorber towers (e.g., Figure 2 As shown in the figure, each circle represents a heat-absorbing tower, and the rings represent the heliostats arranged in the image. A rectangular frame is used to select the area containing the two outermost rings of heliostats in each mirror field as a common area within the field. This area is then uniformly divided into N square grids of the same size. The side length of each grid is set to 1.2 times the characteristic size of the heliostat, ensuring that each grid can only accommodate a maximum of one heliostat.

[0026] A high-precision digital elevation model (DEM) of the site was acquired using oblique photogrammetry from an unmanned aerial vehicle (UAV), achieving an elevation accuracy better than 0.1m. The average slope was calculated for each grid cell. The adjustment limit of the heliostat support is 5% (i.e., a slope angle of approximately 2.86°). If the average slope of a certain grid... If the angle is greater than 2.86°, the weight of the grid is reset to 0, meaning no heliostats will be placed there. This step initially eliminates areas with unsuitable terrain.

[0027] Step 2: Density Screening Calculate the heliostat contribution value for each grid cell, and set a threshold to retain only grid cells with heliostat contribution values ​​greater than the threshold. The calculation method for the heliostat contribution value of each grid cell is as follows: ; in, The contribution value of the heliostats to the grid. It's about the distance from the heat absorption tower. and azimuth Continuous functions; The distance from the center of the grid to the heat absorber tower. The azimuth angle between the grid center and the heat-absorbing tower is the angle between the solar incident direction and the normal to the mirror. In practical engineering, it can be simplified to the azimuth angle function of the grid location relative to the tower.

[0028] For each retained grid, calculate its connection to the three endothermic towers separately. The maximum value among them is taken as the heliostat contribution value of the grid.

[0029] Only when Only when this condition is met can heliostats be placed on the grid; threshold for The 75th percentile of a function is typically the region furthest from the tower and with the worst terrain.

[0030] After this step, the number of grid cells is reduced from N to , ( This significantly reduces the solution scale of subsequent integer programming problems.

[0031] Step 3: Energy Contribution Calculation Based on optical tracing algorithms (such as SolTrace or Tonatiuh), an optical model of the heliostat at each candidate grid location is established. Typical annual meteorological data of the site is input, and with hourly time resolution, the heat power received by the receivers at each time t throughout the year is calculated when each heliostat i reflects sunlight onto the receivers on the three receiver towers. , which is the energy contribution of heliostat i to the heat absorber k at time t. Factors such as atmospheric attenuation, specular reflectivity, shading, and absorber cutoff efficiency are considered in the calculation.

[0032] To simplify calculations, a day can be divided into several typical times (such as noon and two times each in the morning and afternoon at the spring equinox, summer solstice, autumn equinox, and winter solstice), with each time corresponding to a weight.

[0033] Step 4: Variable Definition Define decision variables: ∈{0,1}: If a heliostat is installed at grid i, then =1, otherwise =0.

[0034] Auxiliary variables For at any time Grid Does the internal heliostat reflect sunlight onto the heat absorber? The light is focused on the absorber on the grid. At time t, if the grid... If the fixed heliostat reflects sunlight onto the heat-absorbing tower k, then... =1, otherwise =0.

[0035] Indicates the layout of the heliostats. This indicates the relationship between the heliostat and the heat absorber.

[0036] A heliostat can point to at most one heat-absorbing tower at any given time.

[0037] Step 5: Establish an integer nonlinear programming model The objective function is to maximize the heat output benefit per unit cost: ; in, For a moment Grid Internal heliostat for heat absorption tower Energy contribution value; , ; ; This is a weighting coefficient, set according to the project's investment preferences. If more emphasis is placed on annual power generation, α is set to 1; if more emphasis is placed on saving mirror field costs, α is set to 0.8. In this embodiment, α is set to 0.95.

[0038] Cost of a single heliostat h (including manufacturing, installation, and maintenance). This represents the total number of heat absorption towers. This represents the number of grid cells after filtering in step two.

[0039] The constraints include: 1. Energy demand lower limit constraint (each heat absorption tower must meet the minimum thermal power): ; in, The lower limit of the heat output received by the receiver at time t on the heat absorption tower k is determined by the minimum load of the power generation island and the requirements of the thermal storage system; in this embodiment... =10MW.

[0040] 2. Energy upper limit constraint (safe limit of absorber energy flux density): ; in, The upper limit of the heat output received by the receiver on the heat tower k at time t is taken in this embodiment. =50 MW 。

[0041] 3. Attribution Constraints: ; That is, only the location where heliostats are installed ( Only when the value is 1 can a spotlight task be assigned at a certain time.

[0042] 4. Variable value range constraints: , ; Step Six: Solve for the optimal layout scheme The Particle Swarm Optimization (PSO) algorithm is used to solve the above integer nonlinear programming model. The algorithm parameters are set as follows: Particle swarm size: 200; Number of iterations: 500; Inertia weight: 0.7; Individual learning factor: 1.5; Social learning factor: 1.5.

[0043] Because the decision variables have a high dimensionality (N′ can reach thousands to tens of thousands), a two-stage heuristic is used in the actual solution: first, fix... The initial distribution (e.g., uniform distribution), for each time step Perform local optimization; then update via particle swarm optimization. Repeat the iteration until convergence.

[0044] Finally, the optimal heliostat arrangement vector is obtained. .like If the value is 1, then install a heliostat on the corresponding grid; otherwise, do not install one.

[0045] Step 7: Layout Correction Obtained by mathematical solution The distribution of the mirror field points on the plane may be irregular and scattered, which is not conducive to the construction of the mirror field and the leveling of the land. Therefore, a concentric circle or spiral correction method is adopted: Concentric circle correction: Using the geometric center of each heat exchanger tower as the center, arrange concentric rings at equal radii intervals. The grid position of =1 is mapped to the nearest ring, and the azimuth angle is finely adjusted so that the heliostats are arranged in an approximately ring shape.

[0046] Helix correction (Archimedean spiral): Heliostats are arranged at intervals along the spiral to ensure that there is no obstruction between adjacent heliostats.

[0047] After correction, the optical efficiency of the mirror field needs to be recalculated. If the efficiency loss caused by the correction exceeds 1%, the number of heliostats should be increased appropriately as compensation. In this embodiment, concentric circle correction is used, with a radius interval of 10 m and 8 rings.

[0048] Step 8: Timing Simulation Verification Hourly simulations of the corrected final layout were conducted throughout the year using typical meteorological year (TMY) data for the site. The simulation included: Hourly calculation of the sun's position; Dynamic simulation of mirror field shadow occlusion; The heat absorber receives energy integrals; Thermal cycle calculation for the power generation island (optional).

[0049] Calculate the actual heat collection for the whole year With design goals Compare. If Then return to step two and set the density screening threshold. The quantile is reduced from the original 75th percentile to the 70th percentile, thus retaining more grid candidate points. Steps three through seven are then repeated until the performance metrics are met.

[0050] In this embodiment, after one iteration, the annual heat collection reaches 97.2% of the design target, which meets the requirements.

[0051] Implementation results: Compared to the traditional single-tower independent design followed by simple splicing, the three-tower, one-machine heliostat field arranged using the above method reduces the total number of heliostats by 12.6% within the same land area, increases the annual equivalent full-load power generation hours by 8.3%, and shortens the investment payback period by 2.1 years. Simultaneously, through dynamic scheduling (… (As time changes), the energy flux density fluctuation of each heat absorption tower is reduced by 40%, significantly improving the safety of system operation.

[0052] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A mirror field arrangement method suitable for a single tower-type solar thermal multi-tower unit, characterized in that, Includes the following steps: Step 1: Divide the common area in the multi-tower, single-machine heliostat field into grids, calculate the slope of each grid using a digital elevation model, and exclude grids whose slope exceeds the adjustment limit of the heliostat support. Step 2: Based on the grid retained in Step 1, calculate the heliostat contribution value of each grid, and set a threshold to retain only grids with heliostat contribution values ​​greater than the threshold. Step 3: On the grid screened in Step 2, based on the light tracing algorithm, calculate the energy contribution of the heliostat to different heat absorption towers in each grid at each time point; Step 4: Define decision variables Indicates whether it is in the grid Install heliostats and define auxiliary variables. Indicates at time Grid Does the heliostat reflect light to the heat absorber? ; Step 5: Based on the variables calculated in Step 3 and Step 4, establish an integer nonlinear programming model with the goal of maximizing the heat output revenue per unit cost. The model includes energy demand constraints, energy upper limit constraints, attribution relationship constraints, and variable value range constraints. Step Six: Solve the integer nonlinear programming model to obtain the optimal decision variables. Thus, the arrangement scheme of the mirror field is obtained.

2. The method according to claim 1, characterized in that, In step one, a rectangular frame is used to select the area where the two outermost rings of heliostats in each mirror field are located as the common area in the mirror field. The common area is divided into a uniform grid, and each grid can accommodate at most one heliostat.

3. The method according to claim 1, characterized in that, In step one, the digital elevation model is acquired through UAV oblique photography or LiDAR.

4. The method according to claim 1, characterized in that, In step two, the heliostat contribution value of the grid is calculated as follows: ； in, The contribution value of the heliostats to the grid; This represents the distance from the center of the grid to the heat absorber tower. The azimuth angle between the grid center and the heat absorber tower; Only when Only then is it permissible to place heliostats on the grid; threshold for The 75th percentile of the function.

5. The method according to claim 1, characterized in that, In step four, if =1, which means in the grid Installing a heliostat, if =0 indicates that in the grid No heliostats are installed.

6. The method according to claim 1, characterized in that, The specific integer nonlinear programming model is as follows: ； in, , , ; The constraints are as follows: ； ； ； ， ； in, For a moment Grid Internal heliostat for heat absorption tower Energy contribution value, These are weighting coefficients. and These represent the lower and upper limits of the heat power received by the heat absorber on the heat absorber at time t, respectively. The cost of a single heliostat h, This represents the total number of heat absorption towers. This represents the number of grid cells after filtering in step two.

7. The method according to claim 1, characterized in that, In step six, the particle swarm optimization algorithm is used to solve the integer nonlinear programming model.

8. The method according to claim 1, characterized in that, Step six is ​​followed by: Step 7: Force the heliostat arrangement obtained from the solution to be arranged in concentric circles or spirals.

9. The method according to claim 8, characterized in that, Step seven is followed by: Step 8: Perform a full-year time-series simulation verification based on typical meteorological year data. If the annual heat collection does not meet the requirements, adjust the threshold in Step 2 and recalculate iteratively.

10. The method according to claim 1, characterized in that, The multi-tower, single-machine structure is a three-tower, single-machine configuration.

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