CN110414057B - Radiation energy density simulation method of focusing heliostat in tower type solar thermal power station - Google Patents
Radiation energy density simulation method of focusing heliostat in tower type solar thermal power station Download PDFInfo
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Abstract
The invention discloses a radiation energy density simulation method of a focusing heliostat in a tower type solar thermal power station, which belongs to the technical field of tower type solar thermal power station simulation and comprises the following steps: (1) determining the relative position between the sub-plane mirrors in the local coordinates, and integrally adjusting the positions and the orientations of the sub-plane mirrors according to the incident direction of sunlight; (2) defining an approximate imaging plane according to the direction of the reflected light of the focusing heliostat; (3) uniformly subdividing the three-dimensional space, and calculating heliostats with shadow and shielding relation with the heliostats; (4) performing rasterization representation on the approximate imaging plane; (5) calculating the radiant energy density distribution on the virtual imaging plane; (6) the radiation energy density distribution on the virtual imaging plane is projected onto the receiving surface by oblique parallel projection. The convolution distribution law and the approximate imaging plane are utilized to obtain the accumulation of projection areas of the sub-plane mirrors, namely the weighted effective reflection area, and the solving efficiency of the focusing heliostat model is improved.
Description
Technical Field
The invention relates to the technical field of simulation of tower type solar thermal power stations, in particular to a radiation energy density simulation method of a focusing heliostat in a tower type solar thermal power station.
Background
The tower Solar system is an important mode of photo-thermal power generation, and has the advantages of high conversion efficiency, low heat loss and stable power output (Vanthull L.L. Solar thermal power systems based on optical transmission [ J ]. Solar Energy, 1976, 18 (1): 31-39). The development of the tower type solar energy is influenced by factors such as complex system design, high power generation cost and the like, and the radiant energy density simulation of the tower type solar thermal power station can be used for verifying the design scheme and estimating the power generation efficiency, optimizing the layout and focusing strategy of the heliostats and the mirror surface parameters of the heliostats, improving the power generation efficiency of the system and reducing the average power generation cost. Therefore, the efficient and accurate radiant energy simulation method has important significance for the development of the tower type solar energy system.
For the radiation energy density simulation of the tower type solar thermal power station, two types of methods are mainly used at present: ray tracing and analytical models (Cruz N.C., Rendo J.L., Berengel M., et al.review of software for optical analysis and optimization of hierarchical fields [ J ]. Recewable & Sustainable Energy Reviews, 2017, 72: 1001-1018.). The ray tracing method obtains the radiation energy density distribution of the receiver by simulating the propagation of rays, naturally takes the influence of shadow and shielding into account, and is suitable for the simulation of the radiation energy density of the single-side heliostat. The analytic model simplifies the radiant energy density by using a mathematical model, has high solving speed and is suitable for the simulation of a large-scale system.
Ray tracing is a rendering technique in the field of computer graphics that generates images by simulating the propagation of rays, the intersection of rays with virtual objects. Ray tracing methods can be classified into three types according to ray generation positions: forward ray tracing, reverse ray tracing, and bidirectional ray tracing. The ray tracing method has the problems of low calculation efficiency and unstable peak value because enough rays need to be emitted to obtain a stable result.
The essence of the analytical model is to represent the radiant energy density distribution at the receiver using mathematical formulas, usually in the form of convolution integrals to characterize the effect of the sun model, heliostat micro-surfaces on the radiation spot. The most classical analytical models at present are the HFLCAL model and the UNIZAR model.
HFLCAL uses a simplified mathematical model to characterize FDD as an isotropic Gaussian distribution of the radiation energy density distribution of heliostat spots (
P.，Pitz-Paal R.，Schmitz M. Visual HFLCAL—A Software Tool for Layout and Optimisation of Heliostat Fields[C]// Proceedings of 15th International SolarPACES Symposium, Berlin, September.2009: 15-18.). To improve the accuracy of the HFLCAL model, Garcia defines an isotropic gaussian-distributed scalar Field of radiant energy density on the heliostat surface, and then projects the scalar Field onto the receiver surface by oblique parallel projection (Garcia l., Burisch m., Sanchez m]Energy Procedia, 2015, 69 (12): 1269-1276.). He et al uses a uniform grid structure to organize heliostats, calculates heliostats with shadows and occlusions, and uses a rendering pipeline method to map shadowsTaking into account occlusion, a parallel HFLCAL algorithm (He C., Feng J., Zhao Y. Fast flux density distribution of central receiver system on GPU [ J ] is implemented on the GPU]. Solar Energy，2017，144：424-435.)。
Collado proposed a UNIZAR model in integrated form for representing the radiant Energy density distribution of heliostat spots in 1986 (Collado F., Gomez A., Turegano J. an analytical function for the fluorescent light to the bright reflected from a heliostat J. Solar Energy, 1986, 37 (3): 215- > 234.). UNIZAR models were originally only applicable to planar receiving surfaces, and Albert proposed a general projection method to project Energy distributions from an imaging plane onto a variety of receiving surfaces (S a nchez-Gonz lez, Albert, Santana D. solar flash distribution on central receivers: A projection method from an imaging function [ J ]. Renewable Energy, 2015, 74: 576) -587.).
The defects of the existing simulation method mainly lie in that: the ray tracing calculation efficiency is low, and the peak value is unstable; the analytical model is simplified to result in low simulation accuracy.
Disclosure of Invention
The invention aims to provide a radiation energy density simulation method of a focusing heliostat in a tower type solar thermal power station, which can be used for radiation energy density simulation of a large-scale tower type solar system, realizes acceleration by utilizing an algorithm and a technology of computer graphics, and has the advantages of high efficiency, accuracy and strong universality.
In order to achieve the purpose, the radiation energy density simulation method of the focusing heliostat in the tower type solar thermal power station provided by the invention comprises the following steps:
(1) determining the relative position between the sub-plane mirrors in a local coordinate system, and adjusting the position and the orientation of the sub-plane mirrors according to the incident direction of sunlight;
preferably, in the tower-type solar energy system, the heliostat is generally a focusing heliostat in consideration of the factors such as the system power generation efficiency and the control system cost. The focusing heliostat is composed of a plurality of plane mirrors distributed according to a spherical surface, a paraboloid or other curved surface shapes.
(2) Defining an approximate imaging plane according to the direction of the reflected light of the focusing heliostat;
preferably, the approximate imaging plane is an imaging plane determined by an average of directions of the sub-plane mirror reflected light rays. Each sub-plane mirror corresponds to an imaging plane, since the normal directions of the sub-plane mirrors are different from each other. In an actual mirror field, the distance between a heliostat and a receiving surface is generally hundreds of meters, which is far larger than the size of the heliostat, the direction difference of reflected light rays of different sub-planes is very small, and the distance is also close. A plurality of imaging planes are used for calculation, convolution and projection calculation are needed to be carried out on each imaging plane, and the size of occupied storage space is in direct proportion to the number of the sub-plane mirrors. For a focusing heliostat, the directions of the reflected light rays of different sub-plane mirrors are calculated firstly, the average value of the directions of the reflected light rays is calculated, and an approximate imaging plane is defined according to the average direction of the reflected light rays, so that the calculation times of convolution and projection can be reduced, and the simulation process is accelerated.
(3) Uniformly dividing a three-dimensional space into a series of regularly distributed three-dimensional cuboid grids, intersecting four rays at the edge of a light column with an object in a scene instead of the light column, and determining a focusing heliostat which is intersected with the light column reflected by the heliostat in a heliostat field;
(4) performing rasterization representation on the approximate imaging plane, dispersing the imaging plane into pixel points, accumulating effective reflection areas of single sub-plane mirrors to obtain weighted effective reflection areas, and using n to represent the pixel as the effective reflection areas of n sub-plane mirrors;
the weighted effective reflection region is an accumulated value of projection of the sub-mirrors on the imaging plane to represent the projection of the focusing heliostat on the imaging plane. Ignoring the process of projection area correction, the effective reflection area uses 0 or 1 to indicate whether a pixel on the imaging plane is a projection of the heliostat on the imaging plane, and the weighted effective reflection area uses n to indicate that n sub-flat mirrors are projected on the pixel. Fig. 2 is an illustration of the weighted effective reflection area, where the projection value of a single plane mirror on the imaging plane is 1, and the pixel values are accumulated when the projection areas of multiple sub-planes coincide. Wherein the lines of different colors respectively differ from the effective reflective area of the sub-plane mirror.
(5) Calculating the radiant energy density distribution on the imaging plane on the approximate imaging plane, wherein the calculation formula is as follows:
wherein:
Fimagerepresenting the radiant energy density distribution on the approximate imaging plane;
Cavgthe approximate convolution kernel function is expressed, the approximate convolution kernel function is obtained through calculation of the average incidence angle of the sub-plane mirror and the average distance between the sub-plane mirror and the receiving surface, the radiation energy density distribution of heliostat micro-element light spots corresponding to the distance and the incidence angle is obtained, the heliostat consists of a series of mirror surface micro-elements, and each mirror surface micro-element is a heliostat micro-element.
(6) Projecting the radiant energy density distribution on the approximate imaging plane onto the receiving surface by oblique parallel projection:
Freceiver(u,v)＝Fimage(fu(u,v),fv(u,v))cosθ
wherein:
Freceiverrepresenting a distribution of radiant energy density on the receiving surface;
(u, v) are coordinates of a point on the receiving surface in the plane of the receiving surface local coordinate system oUV;
(fu(u,v),fv(u, V)) are oU 'V' plane coordinates of a point on the approximate imaging plane under the local coordinate system of the imaging plane, the point (u, V) and the point (f)u(u,v),fv(u, v)) the correspondence is an oblique parallel projection;
theta denotes the angle of the reflected ray normal to the receiving surface.
Compared with the prior art, the invention has the beneficial effects that:
for the focusing heliostat, the convolution distribution law and the approximate imaging plane are utilized to obtain the accumulation of projection areas of the sub-plane mirrors, namely the weighted effective reflection area, so that the solving efficiency of a focusing heliostat model is improved.
Drawings
FIG. 1(a) is a schematic view of a plurality of flat mirrors in an embodiment of the present invention, and FIG. 1(b) is a schematic view of a single imaging plane in an embodiment of the present invention;
FIG. 2 is an illustration of a weighted effective reflection area in an embodiment of the present invention;
FIG. 3 is a schematic diagram of a PS10 mirror field in an embodiment of the present invention;
FIG. 4 is a comparison of contours of the HFLCAL model (first column) and the UNIZAR model (second column) in the case of the spring minute day at noon and the squares in the example of the present invention, each row corresponding to heliostat numbers 11, 40, 83, 222 in the PS10 mirror field;
FIG. 5 is a comparison of contours of an HFLCAL model (first column) and a UNIZAR model (second column) in the case of the midday time of the spring minute day and squares in an embodiment of the invention, each row corresponding to heliostat numbers 260, 414, 474, and 623 of FIG. 1, respectively;
FIG. 6(a) is the result of ray tracing, (b) is the result of multi-imaging plane algorithm, (c) the result of single-imaging plane algorithm
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described with reference to the following embodiments and accompanying drawings.
Examples
Referring to fig. 1 to 6, in the present embodiment, an experimental heliostat field is taken as an example, the position of the heliostat field is shown in fig. 3, and table 1 below is specific parameters of the field.
TABLE 1 relevant parameters of the experimental mirror field
The method for simulating the radiant energy density of the focusing heliostat in the tower type solar thermal power station comprises the following steps:
(1) firstly, determining the relative position between the sub-plane mirrors in local coordinates, and integrally adjusting the positions and the orientations of the sub-plane mirrors according to the incident direction of sunlight;
in a tower type solar energy system, the heliostat is generally a focusing type heliostat by considering factors such as system power generation efficiency, control system cost and the like. The focusing heliostat is composed of a plurality of plane mirrors distributed according to a spherical surface, a paraboloid or other curved surface shapes.
(2) Defining an approximate imaging plane according to the direction of the reflected light of the focusing heliostat;
the approximate imaging plane is an imaging plane determined according to an average value of directions of the sub-plane mirror reflection light rays. As shown in fig. 1(a), each of the sub-flat mirrors corresponds to one imaging plane because the normal directions of the sub-flat mirrors are different from each other. In an actual mirror field, the distance between a heliostat and a receiving surface is generally hundreds of meters, which is far larger than the size of the heliostat, the direction difference of reflected light rays of different sub-planes is very small, and the distance is also close. A plurality of imaging planes are used for calculation, convolution and projection calculation are needed to be carried out on each imaging plane, and the size of occupied storage space is in direct proportion to the number of the sub-plane mirrors. As shown in fig. 1(b), for one focusing heliostat, the reflected light ray directions of different sub-plane mirrors are calculated, the average value of the reflected light ray directions is calculated, and an approximate imaging plane is defined by the average reflected light ray direction.
(3) Uniformly dividing a three-dimensional space into a series of regularly distributed three-dimensional cuboid grids, intersecting four rays at the edge of a light column with an object in a scene instead of the light column, and calculating a mirror surface intersected with each sub-plane mirror;
(4) performing rasterization representation on the approximate imaging plane, wherein the rasterization representation is to disperse the imaging plane into pixel points, obtain weighted effective reflection areas through accumulation of effective reflection areas of single sub-plane mirrors, and use n to represent the pixel as the effective reflection areas of n sub-plane mirrors;
the weighted effective reflection region is an accumulated value of projection of the sub-mirrors on the imaging plane to represent the projection of the focusing heliostat on the imaging plane. Ignoring the process of projection area correction, the effective reflection area uses 0 or 1 to indicate whether a pixel on the imaging plane is a projection of the heliostat on the imaging plane, and the weighted effective reflection area uses n to indicate that n sub-flat mirrors are projected on the pixel. Fig. 2 is an illustration of the weighted effective reflection area, where the projection value of a single plane mirror on the imaging plane is 1, and the pixel values are accumulated when the projection areas of multiple sub-planes coincide. Wherein the lines of different colors respectively differ from the effective reflective area of the sub-plane mirror.
(5) Calculating a radiant energy density distribution on the imaging plane on the approximate imaging plane:
the multi-imaging-plane algorithm is a method of directly accumulating the FDD of the sub-mirrors. The multi-imaging plane algorithm calculates the focusing heliostat as a plurality of sub-plane heliostats respectively to obtain FDD (frequency division duplexing) reflected by the sub-plane heliostat to a receiving surface, and accumulates corresponding energy values. The following equation illustrates the principle of the multi-imaging plane algorithm:
wherein K represents the number of the sub-plane mirrors, BiDenotes the effective reflection area, C, of the sub-plane mirror i on the imaging planeiRepresenting the convolution kernel corresponding to the sub-flat mirror i,
is defined on the imaging plane of the sub-flat mirror i.
The single imaging plane algorithm is a method of obtaining an imaging plane FDD by convolving the weighted effective reflection area with an approximate convolution kernel. Firstly, an approximate imaging plane is obtained according to the average normal direction of the sub-plane mirror, a weighted effective reflection area is calculated, then an approximate convolution kernel function is selected according to the average distance and the average incidence angle of the sub-plane mirror and a receiver, finally the weighted effective reflection area is convoluted with the approximate convolution kernel function, and FDD on the imaging plane is projected to a receiving surface. Single imaging plane algorithm principle:
wherein, CavgRepresenting an approximate convolution kernel function, solved by the average incident angle of the sub-mirrors and the average distance from the receiving surface. The distances from the different sub-flat mirrors to the receiving surface are almost the same, and the incident angles are also close, so that the different sub-flat mirrors can use an approximate convolution kernel function according to the average distance from the sub-flat mirrors to the focusing point and the incident angle, and the error caused by the approximation can be further analyzed below.
Denotes the weighted effective reflection area, K denotes the number of sub-flat mirror blocks, BiRepresenting the effective reflection area of the sub-flat mirror i on the approximate imaging plane.
The calculation efficiency of the single imaging plane algorithm is better than that of the multi-imaging plane algorithm. By analyzing the running time, in the calculation of the FDD numerical convolution model of the plane mirror light spot, the most time-consuming part is discrete Fourier convolution calculation, which accounts for about 80% of the total time. For the focusing heliostat, the multi-imaging plane algorithm needs to perform convolution calculation on each sub-plane mirror one by one, so that the overall calculation efficiency of the focusing heliostat is low. The single-imaging plane algorithm only needs one convolution calculation for the heliostat with one focusing surface, and the calculation speed is effectively improved.
TABLE 2 comparison of Multi-imaging plane Algorithm with Single imaging plane Algorithm
Table 2 specifically illustrates the advantages of the single imaging plane algorithm, which is superior to the multi-imaging plane algorithm in terms of storage and computational efficiency. The multi-imaging plane algorithm needs to store the rasterization results of K imaging planes in a memory, and the single-imaging plane algorithm only needs to store the rasterization result of one imaging plane; the convolution operation, energy value accumulation and oblique parallel projection times of multiple imaging planes all need to be performed for K times, and the convolution calculation and oblique parallel projection of a single imaging plane only need to be performed for once.
FIG. 6 shows the results of ray tracing, the results of a multi-imaging plane algorithm, and the results of a single imaging plane algorithm, respectively. Wherein the parameters of line tracking are 2048 rays emitted by each grid, the division granularity of the surface of the heliostat is 0.01m, and the total number of rays emitted by the focusing heliostat is about 4 hundred million. The single imaging plane algorithm has certain difference with the result of multiple imaging planes because of selecting and using an approximate mode in a convolution kernel function and an approximate imaging plane. Table 3 is a statistical result of the energies in fig. 6, and it can be seen that the results of the single-imaging-plane algorithm and the multi-imaging-plane algorithm are both similar to the ray tracing result, and the approximate imaging plane has little influence on the results. The single imaging plane algorithm is less time consuming in terms of time consumption.
(6) Projecting the radiant energy density distribution on the approximate imaging plane onto the receiving surface by oblique parallel projection:
Freceiver(u,v)＝Fimage(fu(u,v),fv(u,v))cosθ
wherein:
Freceiverrepresenting a distribution of radiant energy density on the receiving surface;
(u, v) are coordinates of a point on the receiving surface in the plane of the receiving surface local coordinate system oUV;
(fu(u,v),fv(u, V)) are oU 'V' plane coordinates of a point on the approximate imaging plane under the local coordinate system of the imaging plane, the point (u, V) and the point (f)u(u,v),fv(u, v)) the correspondence is an oblique parallel projection;
theta denotes the angle of the reflected ray normal to the receiving surface.
The experimental results are as follows:
fig. 4 and 5 are respectively the result of spot fitting of focusing heliostats at different positions, where the sub-flat mirrors are distributed on a paraboloid, and the focal length of the paraboloid is the distance between the heliostat and the focusing center. With the heliostat being relatively far from the receiver in fig. 5. As can be seen from the results of the HFLCAL, the UNIZAR, and the focusing heliostat model corresponding to each row in fig. 4, the writing error of the HFLCAL model to the light spot of the focusing heliostat is relatively large, and the light spots are not identical in shape. The results of both the UNIZAR and model focusing heliostat models are relatively close to the spot. Fig. 5 shows the results of the HFLCAL, the UNIZAR and the focusing heliostat model for each column, respectively, and it can be seen from the figure that the HFLCAL, the UNIZAR and the focusing heliostat model in the present embodiment can better fit the energy distribution of the heliostat, but the focusing heliostat model in the present embodiment is closest to the contour line of the ray tracking result.
Table 4 shows the average error for the simulation of a 624-sided heliostat in the field of mirrors by HFLCAL, UNIZAR and the focusing heliostat model of this example. The total energy error, the peak error, the mean square error and the loss function value of the focusing heliostat model are all smaller than those of an HFLCAL model and a UNIZAR model.
TABLE 4 mean error of mirror field simulation
Claims (4)
1. A radiation energy density simulation method of a focusing heliostat in a tower type solar thermal power station is characterized by comprising the following steps:
(1) determining the relative position between the sub-plane mirrors in a local coordinate system, and adjusting the position and the orientation of the sub-plane mirrors according to the incident direction of sunlight;
(2) defining an approximate imaging plane according to the direction of the reflected light of the focusing heliostat;
(3) uniformly dividing a three-dimensional space into a series of uniformly distributed three-dimensional cuboid grids, intersecting the light column with an object in a scene by using four light rays at the edge of the light column instead of the light column, and determining a focusing heliostat intersected with the light column reflected by the heliostat in a heliostat field;
(4) performing rasterization representation on the approximate imaging plane, dispersing the imaging plane into pixel points, and accumulating effective reflection areas of the single sub-plane mirror to obtain a weighted effective reflection area;
(5) calculating a radiant energy density distribution on the imaging plane on the approximate imaging plane; for the convolution calculation of the radiant energy density of the sub-plane mirror light spots, optimizing the flow of the convolution calculation by using a distribution law of convolution, and performing convolution by using an approximate convolution kernel and a weighted effective reflection area; the formula for calculating the radiant energy density distribution on the imaging plane is:
wherein:
Fimagerepresenting the radiant energy density distribution on the approximate imaging plane;
Cavgrepresenting an approximate convolution kernel;
(6) projecting the radiant energy density distribution on the approximate imaging plane onto the receiving surface by oblique parallel projection:
Freceiver(u,v)＝Fimage(fu(u,v),fv(u,v))cosθ
wherein:
Freceiverrepresenting a distribution of radiant energy density on the receiving surface;
(u, v) are coordinates of a point on the receiving surface in the plane of the receiving surface local coordinate system oUV;
(fu(u,v),fv(u, V)) are oU 'V' plane coordinates of a point on the approximate imaging plane under the local coordinate system of the imaging plane, the point (u, V) and the point (f)u(u,v),fv(u, v)) the correspondence is an oblique parallel projection;
theta denotes the angle of the reflected ray normal to the receiving surface.
2. The method of claim 1, wherein the focusing heliostat is comprised of a plurality of sub-mirrors, either spherical or parabolic.
3. The method for simulating the radiant energy density of a focusing heliostat in a tower-type solar thermal power plant according to claim 1, wherein in the step (2), the imaging plane is determined by calculating the average value of the directions of the reflected light rays of all the sub-planes as the normal direction of the approximate imaging plane.
4. The method of claim 1, wherein in step (4), the projection of the focusing heliostat onto the approximate imaging plane is represented using a weighted effective projection area.
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