A method of parametric simulation of the surface radiant energy density distribution of a power plant receiver

Abstract

The application discloses a kind of parameterization simulation methods of power station receiver surface radiant energy density distribution, this method is with gravity center offset elliptical Gaussian distribution representation rectangular plane heliostat reflected light spot on imaging plane radiant energy density distribution.Method uses neural network as regression model, utilizes heliostat size, heliostat and the relative position of receiver, heliostat micro surface normal disturbance, sunlight direction, sunlight distribution, direct solar radiation intensity and other information to estimate radiant energy light spot total energy, elliptical Gaussian distribution variance matrix.When heliostat is shaded, the method considers the gravity center offset of light spot;The method considers more comprehensive influencing factor, so that simulation result is more accurate.

Description

Technical Field

[0001] This invention relates to the field of energy, and in particular to a parametric simulation method for the surface radiation energy density distribution of a power plant receiver. Background Technology

[0002] Tower solar power systems have become a research hotspot in the field of new energy due to their clean and environmentally friendly advantages. The radiation energy density distribution on the receiver surface is crucial for optimizing the mirror field layout and heliostat focusing, and the analytical model of the radiation energy density distribution is the foundation for achieving rapid simulation and optimization.

[0003] Existing parametric simulation methods model the radiant energy density distribution of the heliostat's reflected light spot using mathematical models. The calculation methods for the parameters in the mathematical models used in traditional parametric simulation methods fall into two categories: fitting methods and direct calculation methods. Fitting methods calculate analytical model parameters by fitting measurement results or ray-tracing simulation results. Substituting the parameters obtained through fitting methods into the analytical model yields simulation results that are very close to the true values. However, when ray-tracing simulation results or measurement results are available, there is no need to simulate using an analytical model, so this parameter calculation method is not suitable for practical applications. Direct calculation methods substitute data such as heliostat dimensions, the relative position of the heliostat and receiver, the normal perturbation of the heliostat's micro-surface, the direction of sunlight, the distribution of sunlight, and the intensity of direct solar radiation into empirical formulas to solve for the analytical model parameters. Currently, existing direct calculation methods generally suffer from problems such as incomplete consideration of influencing factors and large calculation errors when using calculated parameters in simulations.

[0004] Considering the shortcomings of existing models, this invention aims to solve the problems of incomplete consideration of influencing factors and inaccurate simulation by existing parametric simulation methods. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a parametric simulation method for the radiation energy density distribution on the surface of a power station receiver. This method represents the radiation energy density distribution of the reflected light spot from a rectangular heliostat on the imaging plane using an elliptical Gaussian distribution with a centroid offset. This method is implemented through the following technical solution:

[0006] A parameterized simulation method for the surface radiation energy density distribution of a power plant receiver includes:

[0007] (1) Obtain the dimensions of each heliostat in the power plant concentrator system, the relative position of each heliostat and the receiver, the normal perturbation of the micro-surface of the heliostat, the direction of sunlight at the current moment, the distribution of sunlight, and the intensity of direct solar radiation.

[0008] (2) Rotate each heliostat to focus on the surface of the receiver, and calculate the area of ​​each heliostat that is blocked by shadow;

[0009] (3) Construct a shallow neural network, which includes an input layer, an output layer and three hidden layers. The input vector of the network includes the length and width of the heliostat, the normal perturbation of the micro-surface of the heliostat, the solar light distribution parameters, the incident angle of the solar light on the heliostat surface and the distance from the heliostat to the receiver. The network predicts the total energy of the radiation spot reflected by the heliostat and the variance matrix of the elliptic Gaussian distribution corresponding to the energy density distribution on the imaging plane.

[0010] (4) Calculate the peak shift of the radiation energy spot caused by the shadow occlusion in the area of ​​the heliostat obtained in step (2); combine the total energy and the variance matrix of the elliptical Gaussian distribution obtained in step (3) to characterize the radiation energy density distribution of the heliostat reflected spot on the imaging plane with an elliptical Gaussian distribution with centroid offset; combine the oblique parallel projection method to calculate the radiation energy density distribution generated by the heliostat on the receiver surface.

[0011] Specifically, in step (3), the imaging plane is a virtual plane that passes through the focal point of the heliostat and is perpendicular to the direction of the reflected light from the center of the heliostat.

[0012] Specifically, in step (4), the radiant energy density distribution of the heliostat reflected spot on the imaging plane is represented by an elliptical Gaussian distribution with a centroid offset, and the centroid offset is calculated from the area of ​​the heliostat that is shaded; the radiant energy density distribution F of the heliostat reflected spot on the imaging plane is... image (x, y) is defined as follows:

[0013]

[0014] Where P h The total energy of the radiant spot reflected by the heliostat is (x b y b ) represents the projection of the centroid of the effective reflection area of ​​the heliostat onto the imaging plane under shadow occlusion, and σ1 and σ2 are the variances of the elliptical Gaussian distribution.

[0015] Furthermore, S3 specifically involves sampling a massive number of simulation scenarios, using the Monte Carlo ray tracing algorithm to simulate and obtain the reflected light spot on the imaging plane, fitting the reflected light spot to obtain the variance matrix of the corresponding elliptical Gaussian distribution, and using the data obtained from sampling, simulation, and fitting to train a shallow neural network for estimating the total energy of the reflected light spot and the variance matrix of the elliptical Gaussian distribution corresponding to its energy density distribution in actual simulation applications.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention takes into account the centroid shift of the radiation energy spot caused by shadow occlusion, making the simulation results closer to the real situation; the influencing factors considered in the modeling are more comprehensive, making the simulation results more accurate. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0018] Figure 1 A flowchart illustrating the parameterized simulation method for the surface radiative energy density distribution of receivers in tower solar power plants is provided for this invention.

[0019] Figure 2 This is a schematic diagram illustrating the calculation process of shadow occlusion between heliostats in this embodiment;

[0020] Figure 3 This is a schematic diagram of the energy density distribution of the heliostat radiation spot on the imaging plane in this embodiment. Detailed Implementation

[0021] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application.

[0022] This invention provides a parameterized simulation method for the surface radiative energy density distribution of receivers in tower solar power plants, such as... Figure 1 As shown. For a given mirror field to be simulated and a given focusing strategy, the specific simulation method steps are as follows:

[0023] S1: Acquire data such as the size of each heliostat in the mirror field, the relative position of each heliostat and the receiver, the normal perturbation of the micro-surface of the heliostat, the direction of sunlight at the current moment, the distribution of sunlight, and the intensity of direct sunlight radiation;

[0024] S2: Rotate each heliostat according to the given focusing strategy to focus on the receiver surface, and calculate the shading of each heliostat. The area and centroid of the unshaded region of the heliostat can be calculated using a mask soft-mapping algorithm, as follows: Figure 2 As shown: The heliostat's surface is uniformly discretized into several sub-heliostats. Two probe rays are emitted from the center of each sub-heliostat along the ideal incident and reflected ray directions. If the rays intersect with other heliostats in the mirror field, the sub-heliostat is considered to be blocked. The unblocked sub-heliostats constitute the effective reflection area of ​​the heliostat.

[0025] S3: Using a pre-trained neural network, predict the relevant parameters of the radiation energy density distribution on each heliostat imaging plane based on the data obtained in S1. The input to the neural network is a parameter vector consisting of the length and width of the heliostat, the normal perturbation of the heliostat micro-surface, the solar radiation distribution parameters, the angle of incidence of sunlight on the heliostat surface, and the distance from the heliostat to the receiver. The output is the predicted value of the total energy of the radiation spot reflected by the heliostat and the variance matrix of the elliptic Gaussian distribution corresponding to the energy density distribution.

[0026] The pre-trained neural network is obtained through the following steps: Sampling of massive simulation scenes, simulation is performed using a Monte Carlo ray tracing algorithm (such as QMCRT) to obtain the reflected light spot of the heliostat on the imaging plane. One-point fitting is then performed on this reflected light spot to obtain the variance matrix of the corresponding elliptical Gaussian distribution. The shallow neural network is then trained using the data obtained from sampling, simulation, and fitting.

[0027] S4: Based on the area of ​​the heliostat obscured by shadows obtained in S2, the total energy obtained in S3, and the variance matrix of the elliptic Gaussian distribution, the radiant energy density distribution of the heliostat reflected light spot on the imaging plane is represented by an elliptic Gaussian distribution with a centroid offset. The centroid offset is obtained by projecting the centroid of the effective reflection area of ​​the heliostat onto the imaging plane, and the calculation formula is as follows:

[0028]

[0029] Where P h The total energy of the radiant spot reflected by the heliostat is (x b y b ) represents the projection of the centroid of the effective reflection area of ​​the heliostat onto the imaging plane under shadow occlusion, and σ1 and σ2 are the variances of the elliptical Gaussian distribution.

[0030] By combining the oblique parallel projection method, the radiation density distribution on the imaging plane is mapped onto the receiver surface, obtaining the simulation results of the radiation density distribution of a single heliostat on the receiver. The final simulation results are obtained by summing the radiation densities reflected from all heliostats on the receiver surface. Figure 3 The diagram shown is a schematic of the energy density distribution of the heliostat radiation spot on the imaging plane. The image is a heat map, and the darker areas indicate higher radiation density. This radiation density distribution follows an elliptical Gaussian distribution, and the boundary distribution is also a Gaussian distribution.

[0031] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.

[0032] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope.

Claims

1. A parameterized simulation method for the surface radiation energy density distribution of a power station receiver, characterized in that, include: (1) Obtain the dimensions of each heliostat in the power plant concentrator system, the relative position of each heliostat and the receiver, the normal perturbation of the micro-surface of the heliostat, the direction of sunlight at the current moment, the distribution of sunlight and the intensity of direct solar radiation; (2) Rotate each heliostat to focus on the surface of the receiver, and calculate the area of ​​each heliostat that is blocked by shadow; (3) Construct a shallow neural network, which includes an input layer, an output layer and three hidden layers. The input vector of the network includes the length and width of the heliostat, the normal perturbation of the micro-surface of the heliostat, the solar distribution parameters, the incident angle of the solar light on the heliostat surface and the distance from the heliostat to the receiver. The network outputs the total energy of the radiation spot reflected by the heliostat and the variance matrix of the elliptic Gaussian distribution corresponding to the energy density distribution on the imaging plane. Specifically, by sampling a large number of simulation scenes, the reflected spot on the imaging plane is obtained by simulation using the Monte Carlo ray tracing algorithm. After fitting the reflected spot, the variance matrix of the corresponding elliptic Gaussian distribution is obtained. The shallow neural network is trained using the data obtained from sampling, simulation and fitting, and is used for the estimation of the total energy of the reflected spot and the variance matrix of the elliptic Gaussian distribution corresponding to its energy density distribution in actual simulation applications. (4) Calculate the peak shift of the radiation energy spot caused by the shadowing of the heliostat in the area shaded by the shadow obtained in step (2); combine the total energy and the variance matrix of the elliptical Gaussian distribution obtained in step (3) to characterize the radiation energy density distribution of the heliostat reflected spot on the imaging plane with an elliptical Gaussian distribution with centroid shift: ； in This represents the total energy of the radiant spot reflected by the heliostat. This represents the projection of the centroid of the effective reflection area of ​​the heliostat onto the imaging plane under shading conditions. , The variance is an elliptic Gaussian distribution. The radiation energy density distribution generated by the heliostat on the receiver surface is calculated by combining the oblique parallel projection method.

2. The parameterized simulation method for the surface radiation energy density distribution of a power station receiver according to claim 1, characterized in that, In step (3), the imaging plane is a virtual plane that passes through the focal point of the heliostat and is perpendicular to the direction of the reflected light from the center of the heliostat.

Images