Method and device for photovoltaic array layout optimization based on dynamic coupling of irradiation and shadow

Abstract

The application relates to a photovoltaic array layout optimization method and device based on irradiation and shadow dynamic coupling, and the method comprises the following steps: constructing a total irradiance model about a photovoltaic component inclination angle and a photovoltaic component azimuth angle according to direct solar radiation, scattering radiation and reflected radiation of the inclined surface of the photovoltaic component; calculating a shadow loss factor by combining a critical unshaded spacing and an actual spacing of front and rear rows of the photovoltaic component array, and coupling the shadow loss factor to the total irradiance model to obtain an effective irradiance model; constructing an optimization objective function based on the effective irradiance model by taking the maximization of annual effective irradiance as an optimization objective; and adopting the lemur optimization algorithm to perform comprehensive balance optimization of dynamic irradiance benefits and shadow losses on the optimization objective function by taking the photovoltaic component inclination angle, the photovoltaic component azimuth angle and the actual spacing as optimization variables to obtain an optimal variable combination. The application can solve the core problems of large irradiation calculation error, static shadow evaluation and low optimization efficiency.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic power generation, and more specifically to a method and apparatus for optimizing the layout of photovoltaic arrays based on dynamic coupling of irradiance and shading. Background Technology

[0002] The large-scale grid integration of renewable energy sources, especially photovoltaic (PV) power generation, has been driven by the large-scale grid connection. In PV power plant planning, the coordinated optimization of PV module tilt, azimuth angle, and PV module array spacing is a core issue for improving system power generation efficiency and optimizing PV module array space. Traditional layout methods suffer from three technical bottlenecks: First, irradiance modeling generally adopts the isotropic sky assumption, ignoring the azimuth non-uniformity of atmospheric scattering, leading to tilt angle optimization results deviating from the actual optimal value. Second, shading assessment is mostly based on static geometric projection methods, employing fixed spacing design criteria, resulting in low land utilization. Finally, existing optimization methods only consider the coupling problem of tilt angle and spacing when dealing with the spatial problem of PV systems, ignoring the impact of azimuth angle on PV array power generation efficiency; simultaneously, existing optimization methods, such as genetic algorithms and particle swarm optimization, suffer from slow convergence speed and susceptibility to local optima when dealing with multivariate coupled problems. Summary of the Invention

[0003] This invention provides a method and apparatus for optimizing the layout of photovoltaic arrays based on dynamic coupling of irradiance and shading, in order to solve at least one of the above-mentioned technical problems.

[0004] The technical solution of this invention to solve the above-mentioned technical problems is as follows: a photovoltaic array layout optimization method based on dynamic coupling of irradiance and shading, comprising:

[0005] S1. Based on the direct solar radiation, scattered radiation, and reflected radiation of the tilted surface of the photovoltaic module, construct a total irradiance model for the tilt angle and azimuth angle of the photovoltaic module.

[0006] S2, taking into account the shading problem of the front and rear rows of the photovoltaic module array, the shadow loss factor is calculated by combining the critical unshaded spacing and the actual spacing of the front and rear rows of the photovoltaic module array, and the shadow loss factor is coupled to the total irradiance model to obtain the effective irradiance model;

[0007] S3, with the goal of maximizing annual effective irradiance, construct an optimization objective function based on the effective irradiance model;

[0008] S4. Using the tilt angle of the photovoltaic module, the azimuth angle of the photovoltaic module, and the actual spacing as optimization variables, the lemur optimization algorithm is used to perform a comprehensive balance optimization of dynamic irradiance gain and shadow loss on the optimization objective function to obtain the optimal variable combination.

[0009] Based on the above technical solution, the present invention can be further improved as follows.

[0010] Furthermore, S1 specifically refers to:

[0011] S11, Based on the direct solar radiation on the tilted surface of the photovoltaic module, construct a direct radiation model regarding the tilt angle and azimuth angle of the photovoltaic module;

[0012] S12, Based on the solar scattering radiation from the tilted surface of the photovoltaic module, construct a scattering radiation model regarding the tilt angle and azimuth angle of the photovoltaic module;

[0013] S13, Based on the solar reflection radiation of the tilted surface of the photovoltaic module, construct a reflection radiation model about the tilt angle of the photovoltaic module;

[0014] S14, the direct radiation model, the scattered radiation model and the reflected radiation model are superimposed to obtain the total irradiance model.

[0015] Furthermore, the direct radiation model is as follows:

[0016]

[0017] Among them, I b，T I represents the hourly direct solar irradiance on the tilted surface of the photovoltaic module. b θ represents the hourly direct irradiance on a horizontal surface. z Let θ be the zenith angle, and θ z =arccos(sinφsinδ+cosφcosδcosω), where φ is the local latitude, δ is the solar declination angle, ω is the solar hour angle, and θ is the angle of incidence, and cosθ = cosθ z cosβ+sinθ z sinβcos(γ s -γ), β is the tilt angle of the photovoltaic module, γ is the azimuth angle of the photovoltaic module, γ s The angle between the projection of the sun's rays onto the ground and the north-south direction line;

[0018] The scattered radiation model is as follows:

[0019]

[0020] Among them, I d，T I represents the hourly solar diffuse irradiance on the tilted surface of the photovoltaic module. d F1 represents the hourly scattered irradiance on the horizontal plane; F2 and F1 are the annular luminance coefficient and the horizontal plane coefficient, respectively. Δ represents brightness, and m is the mass of the atmosphere, and I0 represents the irradiance of sunlight on a vertical plane outside the atmosphere, f 11 f 12 f 13 f 21 f 22 and f 23 All are empirical coefficients;

[0021] The reflected radiation model is as follows:

[0022]

[0023] Among them, I g，T Let I be the hourly ground-reflected irradiance on the tilted surface of the photovoltaic module; and let I be the total irradiance on the horizontal surface, where I = I0. d +I b ρ is the ground reflectivity.

[0024] Furthermore, S2 specifically refers to:

[0025] S21, calculate the critical unobstructed spacing between the front and rear rows of the photovoltaic module array based on the projected length of the photovoltaic module and the dynamic correction term of the solar azimuth.

[0026] S22, calculate the occlusion ratio based on the critical unobstructed distance and the actual distance;

[0027] S23, Based on the shading superposition effect of multiple rows of photovoltaic modules, the shadow loss factor is calculated according to the shading ratio;

[0028] S24, the shadow loss factor is coupled to the total irradiance model to obtain the effective irradiance model.

[0029] Furthermore, the critical unobstructed distance is:

[0030]

[0031] Among them, D ts Let D1 be the critical unobstructed spacing, D1 be the projected length of the photovoltaic module, and D1 = Lcosβ, and D2 be the dynamic correction term for the solar azimuth. L is the length of the photovoltaic module, β is the tilt angle of the photovoltaic module, H is the vertical height of the photovoltaic module, and H = L × sinβ, α is the solar altitude angle, and α = 90° - θ z γ s γ is the solar azimuth angle, and γ is the azimuth angle of the photovoltaic module;

[0032] The occlusion ratio is:

[0033]

[0034] Where, p shadWhere D is the occlusion ratio, and D is the actual spacing;

[0035] The shadow loss factor is:

[0036]

[0037] Among them, F shad The shadow loss factor is n, where n is the number of rows side-by-side on a single support.

[0038] The effective irradiance model is as follows:

[0039] I T，eff =I T ·F shad =(I b，T +I d，T +I g，T )·F shad ;

[0040] Among them, I T，eff For effective irradiance, I T For total irradiance, and I T =I b，T +I d，T +I g，T I b，T I represents the hourly direct solar irradiance on the tilted surface of the photovoltaic module. d，T I represents the hourly solar diffuse irradiance on the tilted surface of the photovoltaic module. g，T This represents the hourly ground-reflected irradiance on the tilted surface of the photovoltaic module.

[0041] Furthermore, S3 specifically refers to:

[0042] S31, Based on the effective irradiance model, construct the effective irradiance function per unit area of ​​photovoltaic module in one hour;

[0043] S32, with maximizing the annual effective irradiance as the optimization objective, construct the objective function based on the effective irradiance function.

[0044] Furthermore, the effective irradiance function is:

[0045]

[0046] in, H dir，t (β, γ), H diff，t (β, γ) and H ref，t (β) represents the effective irradiance, direct irradiance, diffuse irradiance, and reflected irradiance per unit area of ​​the photovoltaic module at hour t, respectively. t (β,γ,D) is the shadow loss factor at hour t;

[0047] The objective function is:

[0048]

[0049] in, This refers to the total effective irradiance per unit area of ​​a photovoltaic module over one year.

[0050] Furthermore, S4 specifically includes:

[0051] S41, Set the population size, maximum number of iterations, and individual constraints, and initialize the current number of iterations k = 1; wherein, the photovoltaic module tilt angle, the photovoltaic module azimuth angle, and the actual spacing are optimization variables, and the combination of the photovoltaic module tilt angle, the photovoltaic module azimuth angle, and the actual spacing is used as the population individual;

[0052] S42, treat the individuals in the population as lemurs, calculate the fitness value of the individuals in the population according to the objective function, and determine the global optimal solution and the local optimal solution based on the fitness value;

[0053] S43, generate uniform random numbers for the update behavior, and calculate the free risk coefficient based on the current iteration number;

[0054] S44, determine whether the uniform random number of the update behavior is less than the free risk coefficient. If yes, update the optimization variable based on the global optimal solution under the individual constraints. If no, update the optimization variable based on the local optimal solution under the individual constraints.

[0055] S45, after optimizing the variable update, let the current iteration number k = k + 1, and return to S42 to iterate until the current iteration number reaches the maximum iteration number or the annual effective irradiance change is less than the set threshold, then stop iterating and output the final global optimal solution.

[0056] Furthermore, the formula for updating and optimizing the variables is as follows:

[0057]

[0058] in, Let j be the value of the i-th individual in the population for the j-th optimization variable at the (k+1)-th iteration. Let j be the value of the i-th individual in the population for the j-th optimization variable at the k-th iteration. This represents the local optimum of the j-th optimization variable during the k-th iteration. Let be the global optimal solution for the j-th optimization variable in the k-th iteration, where j = 1, 2, 3; Rand is the uniformly random number for the update behavior, and FRR is the free risk coefficient. k is the current iteration number, kMax For the maximum number of iterations, FRR max and FRR min These are the upper and lower limits of FRR, respectively.

[0059] Based on the above-mentioned photovoltaic array layout optimization method based on dynamic coupling of irradiance and shading, the present invention also provides a photovoltaic array layout optimization device based on dynamic coupling of irradiance and shading.

[0060] A photovoltaic array layout optimization device based on dynamic coupling of irradiance and shading includes a processor, a memory, and a computer program stored in the memory. When the computer program is executed by the processor, it implements the photovoltaic array layout optimization method based on dynamic coupling of irradiance and shading as described above.

[0061] The beneficial effects of this invention are as follows: This invention is based on a photovoltaic array layout optimization method and apparatus using dynamic coupling of irradiance and shading. Firstly, it constructs a dynamic coupling model of the irradiance on the tilted surface of the photovoltaic modules, integrating the direct irradiance component, the scattering component dynamically corrected based on atmospheric brightness parameters, and the surface reflection component to form a spatiotemporally varying total irradiance model. Secondly, it establishes a dynamic shading correction mechanism, quantifying the geometric relationship of the photovoltaic modules through a critical unshaded spacing function, and generating a shading loss factor by combining the superposition effect of multiple rows of shading, thus achieving precise coupling of irradiance and shading loss. Finally, with the goal of maximizing annual effective irradiance, and using module tilt angle, module azimuth angle, and actual spacing as optimization variables, it employs a lemur swarm intelligent optimization algorithm to balance local search and global development through dynamic risk factors, outputting three-dimensional collaborative optimal layout parameters. This invention breaks through the limitations of traditional static models, realizing the spatiotemporal dynamic quantification of irradiance attenuation and multi-row shading; it improves power generation efficiency under land constraints through a three-dimensional collaborative optimization framework of tilt angle, azimuth angle, and actual spacing; the lemur optimization algorithm effectively solves the premature convergence problem of high-dimensional nonlinear optimization, and is applicable to all terrain scenarios such as deserts, snowfields, and mountains, significantly improving the energy capture efficiency and land resource utilization of photovoltaic systems. Attached Figure Description

[0062] Figure 1 This is a flowchart of the photovoltaic array layout optimization method based on dynamic coupling of irradiance and shading according to the present invention;

[0063] Figure 2 A flowchart for constructing the overall irradiance model;

[0064] Figure 3 This is a schematic diagram showing the positions of the sun and the photovoltaic panels.

[0065] Figure 4 Flowchart for constructing an effective irradiance model;

[0066] Figure 5 A schematic diagram showing unobstructed shadows in the front and rear rows of photovoltaic modules;

[0067] Figure 6 Flowchart of the algorithm optimization for lemurs;

[0068] Figure 7 The image shows the convergence curve of the lemur optimization algorithm in the example.

[0069] Figure 8 This is a schematic diagram showing the distribution space of all solution parameters and the optimal solution of the lemur optimization algorithm in the embodiment;

[0070] Figure 9 The present invention provides a structural block diagram of a photovoltaic array layout optimization device based on dynamic coupling of irradiation and shading. Detailed Implementation

[0071] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0072] Example 1:

[0073] like Figure 1 As shown, the photovoltaic array layout optimization method based on dynamic coupling of irradiance and shading includes:

[0074] S1. Based on the direct solar radiation, scattered radiation, and reflected radiation of the tilted surface of the photovoltaic module, construct a total irradiance model for the tilt angle and azimuth angle of the photovoltaic module.

[0075] S2, taking into account the shading problem of the front and rear rows of the photovoltaic module array, the shadow loss factor is calculated by combining the critical unshaded spacing and the actual spacing of the front and rear rows of the photovoltaic module array, and the shadow loss factor is coupled to the total irradiance model to obtain the effective irradiance model;

[0076] S3, with the goal of maximizing annual effective irradiance, construct an optimization objective function based on the effective irradiance model;

[0077] S4. Using the tilt angle of the photovoltaic module, the azimuth angle of the photovoltaic module, and the actual spacing as optimization variables, the lemur optimization algorithm is used to perform a comprehensive balance optimization of dynamic irradiance gain and shadow loss on the optimization objective function to obtain the optimal variable combination.

[0078] The following is a detailed explanation of each step.

[0079] In S1 of this invention, the horizontal irradiance varies at different locations on Earth. Hourly irradiance data on the horizontal surface can be directly measured by meteorological instruments or calculated based on geographical location and then stored in a standard meteorological database. In the hourly model, the basic data for hourly irradiance on the tilted surface of the photovoltaic module comes from this horizontal surface data. The radiation received by the tilted surface is mainly divided into three parts: direct radiation, scattered radiation, and reflected radiation. Direct radiation is a portion of solar radiation that is projected onto the surface with parallel rays; scattered radiation is solar radiation that is scattered by tiny particles in the atmosphere, such as air molecules and aerosol particles, when sunlight passes through the Earth's atmosphere; reflected radiation is a portion of solar radiation that reaches the surface of other objects, mainly the Earth's surface, through reflection.

[0080] In this embodiment, the photovoltaic (PV) site is located in a port area of ​​Lianyungang City, Jiangsu Province, specifically at longitude 119.216°, latitude 34,5977°, and time zone Beijing time (UTC+8). In the construction of PV power plants, PV strings are generally used as the basic unit for calculating irradiance and efficiency. Therefore, it is assumed that the PV modules have a common area of ​​0.991m x 1.956m, and that nine strings are arranged in a (0.991 x 3)m x (1.956 x 3)m configuration, forming a single PV string with an area of ​​2.973m x 5.868m = 17.4456㎡. Considering the influence of maintenance access and hot spot effects, the distance between strings should not be less than 1m; in this example, a 1m maintenance access is selected.

[0081] Specifically, such as Figure 2 As shown, S1 specifically refers to:

[0082] S11, Based on the direct solar radiation on the tilted surface of the photovoltaic module, construct a direct radiation model regarding the tilt angle and azimuth angle of the photovoltaic module;

[0083] S12, Based on the solar scattering radiation from the tilted surface of the photovoltaic module, construct a scattering radiation model regarding the tilt angle and azimuth angle of the photovoltaic module;

[0084] S13, Based on the solar reflection radiation of the tilted surface of the photovoltaic module, construct a reflection radiation model about the tilt angle of the photovoltaic module;

[0085] S14, the direct radiation model, the scattered radiation model and the reflected radiation model are superimposed to obtain the total irradiance model.

[0086] Figure 3 This is a schematic diagram showing the positions of the sun and the photovoltaic panel. Based on the positional relationship between the sun and the photovoltaic panel, the direct radiation model, scattered radiation model, and reflected radiation model are constructed as follows:

[0087] The direct radiation model of the tilted surface of a photovoltaic module is relatively simple, and it is proportional to the direct irradiance on the horizontal surface, specifically:

[0088]

[0089] Among them, I b，T I represents the hourly direct solar irradiance on the tilted surface of the photovoltaic module. b θ represents the hourly direct irradiance on a horizontal surface. z Let θ be the zenith angle, and θ z =arccos(sinφsinδ+cosφcosδcosω), where φ is the local latitude, δ is the solar declination angle, ω is the solar hour angle, and θ is the angle of incidence, and cosθ = cosθ z cosβ+sinθ z sinβcos(γs-γ), where β is the tilt angle of the photovoltaic module (i.e., the angle between the tilted surface of the photovoltaic module and the horizontal plane), and γ is the azimuth angle of the photovoltaic module. s It is the angle between the projection of sunlight onto the ground and the north-south direction line.

[0090] Scattered radiation is a key component of the radiation received by the tilted surface of a photovoltaic module, and its calculation is more complex. Generally, methods for calculating scattered radiation from a tilted surface can be divided into isotropic sky models and anisotropic models. The scattered radiation model of the tilted surface of a photovoltaic module calculated using the Perez model is as follows:

[0091]

[0092] Among them, I d，T I represents the hourly solar diffuse irradiance on the tilted surface of the photovoltaic module. d F1 represents the hourly scattered irradiance on the horizontal plane; F2 and F1 are the annular luminance coefficient and the horizontal plane coefficient, respectively, which are dimensionless. F1 and F2 describe the zenith angle θ of the sky. z The three parameters are sharpness ξ and brightness Δ. Δ represents brightness, and m represents atmospheric mass, which refers to the extent to which sunlight passes through the atmosphere. I0 is the irradiance of sunlight on the vertical plane outside the atmosphere, with a value of 1367 W / m². 2 ;f 11 f 12 f 13 f 21 f 22 and f 23 These are all empirical coefficients, representing brightness coefficients.

[0093] In addition, the sharpness ξ is related to the hourly scattered irradiance I on the horizontal plane. d and the direct irradiance I on the plane perpendicular to the incident sunlight n There is a certain calculation relationship:

[0094]

[0095] The reflection radiation model mainly involves ground reflectivity and the tilt angle of photovoltaic modules. The reflection radiation model is as follows:

[0096]

[0097] Among them, I g，T Let I be the hourly ground-reflected irradiance on the tilted surface of the photovoltaic module; and let I be the total irradiance on the horizontal surface, where I = I0. d +I b ρ is the ground reflectivity, the magnitude of which is related to the ground type. In this example, the ground reflectivity ρ is selected as 0.2 based on the tidal flat geology.

[0098] In summary, the direct radiation model, the scattered radiation model, and the reflected radiation model yield the overall irradiance model, which is specifically as follows:

[0099]

[0100] In S2 of the present invention, as Figure 4 As shown, S2 specifically refers to:

[0101] S21, calculate the critical unobstructed spacing between the front and rear rows of the photovoltaic module array based on the projected length of the photovoltaic module and the dynamic correction term of the solar azimuth.

[0102] S22, calculate the occlusion ratio based on the critical unobstructed distance and the actual distance;

[0103] S23, Based on the shading superposition effect of multiple rows of photovoltaic modules, the shadow loss factor is calculated according to the shading ratio;

[0104] S24, the shadow loss factor is coupled to the total irradiance model to obtain the effective irradiance model.

[0105] Figure 5 This is a schematic diagram showing the unobstructed shadows cast by the front and rear rows of photovoltaic modules; for example... Figure 5 As shown, the shading problem of the front and rear rows of a photovoltaic module array is determined by the relationship between the sun's position and the photovoltaic modules' geometry. The critical unshaded spacing D... ts Defined as the minimum spacing between photovoltaic modules at a given moment so that sunlight does not obstruct the view of the rear rows, it consists of the projected length of the photovoltaic modules and a dynamic correction term for the sun's azimuth:

[0106]

[0107] Among them, D ts Let D1 be the critical unobstructed spacing, D1 be the projected length of the photovoltaic module, and D1 = Lcosβ, and D2 be the dynamic correction term for the solar azimuth. L is the length of the photovoltaic module, β is the tilt angle of the photovoltaic module, H is the vertical height of the photovoltaic module, and H = L × sinβ, α is the solar altitude angle, and α = 90° - θ z γ s γ is the solar azimuth angle, and γ is the azimuth angle of the photovoltaic module.

[0108] The ratio of the actual spacing between the front and rear rows of a photovoltaic module array to the critical unshaded spacing determines the shading ratio, which is defined as:

[0109]

[0110] Where, p shad Where is the occlusion ratio, and D is the actual spacing.

[0111] For the cumulative shading effect of multiple rows of photovoltaic modules, a shading loss factor is defined, which is:

[0112]

[0113] Among them, F shad The shadow loss factor is... To round up to the nearest integer, n is the number of rows on a single support. In this embodiment, n is 1, meaning that a row of photovoltaic modules is set on a single support.

[0114] The effective irradiance model is as follows:

[0115] I T，eff =I T ·F shad =(I b，T +I d，T +I g，T )·F shad ;

[0116] Among them, I T，eff For effective irradiance, I T For total irradiance, and I T =I b，T +I d，T +I g，T I b,T I represents the hourly direct solar irradiance on the tilted surface of the photovoltaic module. d,T I represents the hourly solar diffuse irradiance on the tilted surface of the photovoltaic module. g，T This represents the hourly ground-reflected irradiance on the tilted surface of the photovoltaic module.

[0117] In S3 of the present invention, S3 specifically refers to:

[0118] S31, Based on the effective irradiance model, construct the effective irradiance function per unit area of ​​photovoltaic module in one hour;

[0119] S32, with maximizing the annual effective irradiance as the optimization objective, construct the objective function based on the effective irradiance function.

[0120] Specifically, the optimization objective is to maximize the annual effective irradiance. Considering the dynamic coupling between the shading loss factor and the total irradiance, the effective irradiance function is:

[0121]

[0122] in, H represents the effective irradiance per unit area of ​​the photovoltaic module in hour t. dir，t (β, γ), H diff，t (β, γ) and H ref，t (β) represents the direct irradiance, diffuse irradiance, and reflected irradiance per unit area of ​​the photovoltaic module at hour t, corresponding to the direct radiation model, diffuse radiation model, and reflected radiation model constructed in S1, respectively. t (β, γ, D) is the shadow loss factor at hour t, which corresponds to the shadow loss factor defined in S23; t is the time index (unit: hour, t = 1 to 8760, 8760 is calculated by multiplying 24 hours in a day by 365 days in a year).

[0123] The annual effective irradiance can be calculated based on the effective irradiance per unit area of ​​the photovoltaic module in hour t; therefore, the objective function is:

[0124]

[0125] in, This refers to the total effective irradiance per unit area of ​​a photovoltaic module over one year.

[0126] In S4 of the present invention, as Figure 6 As shown, S4 specifically includes:

[0127] S41, Set the population size, maximum number of iterations, and individual constraints, and initialize the current number of iterations k = 1; wherein, the photovoltaic module tilt angle, the photovoltaic module azimuth angle, and the actual spacing are optimization variables, and the combination of the photovoltaic module azimuth angle and the actual spacing is used as the population individual;

[0128] S42, treat the individuals in the population as lemurs, calculate the fitness value of the individuals in the population according to the objective function, and determine the global optimal solution and the local optimal solution based on the fitness value;

[0129] S43, generate uniform random numbers for the update behavior, and calculate the free risk coefficient based on the current iteration number;

[0130] S44, determine whether the uniform random number of the update behavior is less than the free risk coefficient. If yes, update the optimization variable based on the global optimal solution under the individual constraints. If no, update the optimization variable based on the local optimal solution under the individual constraints.

[0131] S45, after optimizing the variable update, let the current iteration number k = k + 1, and return to S42 to iterate until the current iteration number reaches the maximum iteration number or the annual effective irradiance change is less than the set threshold, then stop iterating and output the final global optimal solution.

[0132] Specifically, to further improve the power generation efficiency of photovoltaic modules under actual operating conditions, it is necessary to collaboratively optimize the tilt angle β, azimuth angle γ, and module spacing D. In this invention, the lemur optimization algorithm from the swarm intelligence algorithm is used to maximize the objective function constructed from the shading loss factor and the total irradiance model. The optimization variables are the photovoltaic module tilt angle, the photovoltaic module azimuth angle, and the actual spacing, and the optimization variables are subject to the following constraints (i.e., individual constraints):

[0133] β min ≤β≤β max γ min ≤γ≤γ max 0<D≤D max ;

[0134] Where, β min and β max Let β be the minimum and maximum value, and γ be the maximum and minimum values. min and γ max Let D be the minimum and maximum values ​​of γ. max This is the maximum value of D.

[0135] The lemur optimization algorithm simulates the jumping behavior of lemurs in nature, with a population size of (β). i γ i D i In this embodiment, the population size p is set to 30, and the maximum number of iterations k is set to 10. Max Set it to 1000.

[0136] The formula for calculating fitness value is:

[0137]

[0138] Among them, F i Let be the fitness value of the i-th individual in the population. For ease of optimization, the objective function is negative to indicate a minimization problem.

[0139] The formula for updating and optimizing variables is:

[0140]

[0141] in, Let j be the value of the i-th individual in the population for the j-th optimization variable at the (k+1)-th iteration. Let j be the value of the i-th individual in the population for the j-th optimization variable at the k-th iteration. This represents the local optimum of the j-th optimization variable during the k-th iteration. This represents the global optimal solution for the j-th optimization variable in the k-th iteration; j = 1, 2, 3, corresponding to the three optimization variables: photovoltaic module tilt angle, photovoltaic module azimuth angle, and actual spacing, respectively; Rand is a uniformly random number between 0 and 1 used to determine the probability of the update behavior; FRR is the free risk coefficient, which changes with the current iteration number, and k is the current iteration number, k Max For the maximum number of iterations, FRR max and FRR min These are the upper and lower limits of FRR, respectively.

[0142] Figure 7 This is the convergence curve of the lemur optimization algorithm in this embodiment; from Figure 7 As can be seen, the fitness value (logarithmic scale) decreases with increasing iteration number.

[0143] In the lemur optimization algorithm, the iteration terminates when the current iteration count reaches the maximum iteration count or the annual effective irradiance change is less than a set threshold. After stopping the iteration, the final global optimal solution output is the optimal variable combination. Through optimization using the lemur optimization algorithm, the optimal variable combination in this embodiment is (20.45°, 8.24°, 3.22m).

[0144] Figure 8 This diagram illustrates the distribution space of all solution parameters and the optimal solution of the lemur optimization algorithm in this embodiment; where color represents fitness value, and red pentagram represents the optimal solution.

[0145] Example 2:

[0146] Based on the above-mentioned photovoltaic array layout optimization method based on dynamic coupling of irradiance and shading, the present invention also provides a photovoltaic array layout optimization device based on dynamic coupling of irradiance and shading.

[0147] A photovoltaic array layout optimization device based on dynamic coupling of irradiance and shading includes a processor, a memory, and a computer program stored in the memory. When the computer program is executed by the processor, it implements the photovoltaic array layout optimization method based on dynamic coupling of irradiance and shading as described above.

[0148] In other words, the photovoltaic array layout optimization device based on dynamic coupling of irradiance and shading in the embodiments of the present invention may include, but is not limited to: a processor and a memory; the memory is used to store computer programs; the processor is used to execute the photovoltaic array layout optimization method based on dynamic coupling of irradiance and shading shown in any embodiment of the present invention by calling the computer programs.

[0149] In one alternative embodiment, a photovoltaic array layout optimization device based on dynamic coupling of irradiance and shading is provided, such as Figure 9 As shown. Figure 9 The photovoltaic array layout optimization device based on dynamic coupling of irradiance and shading, as shown, includes a processor and a memory. The processor and memory are connected, for example, via a bus. Optionally, the photovoltaic array layout optimization device based on dynamic coupling of irradiance and shading may further include a transceiver, which can be used for data interaction between the device and other electronic devices, such as data transmission and / or data reception. It should be noted that in practical applications, the transceiver is not limited to one unit, and the structure of this photovoltaic array layout optimization device based on dynamic coupling of irradiance and shading does not constitute a limitation on the embodiments of the present invention.

[0150] The processor can be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), a PLC (Programmable Controllers), a FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this invention. The processor can also be a combination that implements computational functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0151] A bus can include a pathway for transmitting information between the aforementioned components. The bus can be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 9 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0152] The memory may be ROM (Read Only Memory) or other types of static storage devices capable of storing static information and instructions, RAM (Random Access Memory) or other types of dynamic storage devices capable of storing information and instructions, or EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited to these.

[0153] The memory stores application code (computer program) that executes the present invention, and its execution is controlled by a processor. The processor executes the application code stored in the memory to implement the content shown in the foregoing method embodiments.

[0154] Among them, the photovoltaic array layout optimization device based on dynamic coupling of irradiance and shading can also be a terminal device. The terminal device can be any device that can install applications, including at least one of smartphones, tablets, laptops, desktop computers, smart speakers, smartwatches, smart TVs, and smart in-vehicle devices.

[0155] It should be noted that, Figure 9 The photovoltaic array layout optimization device based on dynamic coupling of irradiance and shading shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present invention.

[0156] This invention, based on a photovoltaic array layout optimization method and device using dynamic coupling of irradiance and shading, addresses the core problems of traditional methods—large irradiance calculation errors, static shading assessment, and low optimization efficiency—through three major innovations: dynamic modeling, multi-parameter collaboration, and intelligent algorithms. Compared with existing technologies, this invention has the following outstanding advantages: 1. It accurately quantifies the combined effects of changes in the tilt and azimuth angles of photovoltaic modules on direct sunlight attenuation and multi-row shading of photovoltaic modules through a dynamic irradiance-shading coupling mechanism, breaking through the spatiotemporal limitations of traditional static models; 2. It constructs a three-dimensional collaborative optimization framework of tilt angle, azimuth angle, and actual spacing to achieve the search for the globally optimal solution for power generation under land resource constraints; 3. It uses a lemur optimization algorithm to balance local development and global search with dynamic risk factors, overcoming the premature convergence problem of high-dimensional nonlinear optimization; 4. It integrates atmospheric brightness parameters and dynamic correction of surface reflectivity to achieve adaptive modeling of all terrain scenarios such as deserts, snowfields, and mountains.

[0157] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A photovoltaic array layout optimization method based on dynamic coupling of irradiance and shading, characterized in that, include: S1. Based on the direct solar radiation, scattered radiation, and reflected radiation of the tilted surface of the photovoltaic module, construct a total irradiance model for the tilt angle and azimuth angle of the photovoltaic module. S2, taking into account the shading problem of the front and rear rows of the photovoltaic module array, the shadow loss factor is calculated by combining the critical unshaded spacing and the actual spacing of the front and rear rows of the photovoltaic module array, and the shadow loss factor is coupled to the total irradiance model to obtain the effective irradiance model; S3, with the goal of maximizing annual effective irradiance, construct an optimization objective function based on the effective irradiance model; S4. Using the tilt angle of the photovoltaic module, the azimuth angle of the photovoltaic module, and the actual spacing as optimization variables, the lemur optimization algorithm is used to perform a comprehensive balance optimization of dynamic irradiance gain and shadow loss on the optimization objective function to obtain the optimal variable combination. Specifically, S2 is: S21, calculate the critical unobstructed spacing between the front and rear rows of the photovoltaic module array based on the projected length of the photovoltaic module and the dynamic correction term of the solar azimuth. S22, calculate the occlusion ratio based on the critical unobstructed distance and the actual distance; S23, Based on the shading superposition effect of multiple rows of photovoltaic modules, the shadow loss factor is calculated according to the shading ratio; S24, Couple the shadow loss factor to the total irradiance model to obtain the effective irradiance model; The critical unobstructed distance is: ； in, The critical unobstructed distance, Let be the projected length of the photovoltaic module, and , This is a dynamic correction term for the sun's azimuth, and , The length of the photovoltaic module, The tilt angle of the photovoltaic module. The vertical height of the photovoltaic module, and , It is the solar altitude angle, and , The azimuth of the sun. The azimuth angle of the photovoltaic module; The occlusion ratio is: ； in, The occlusion ratio is... The actual spacing; The shadow loss factor is: ； in, The shadow loss factor is... The number of rows side-by-side on a single support; The effective irradiance model is as follows: ； in, For effective irradiance, For total irradiance, and , The hourly direct solar irradiance on the tilted surface of the photovoltaic module. The hourly solar diffuse irradiance on the tilted surface of the photovoltaic module. This represents the hourly ground-reflected irradiance on the tilted surface of the photovoltaic module.

2. The photovoltaic array layout optimization method based on dynamic coupling of irradiance and shading according to claim 1, characterized in that, Specifically, S1 is: S11, Based on the direct solar radiation on the tilted surface of the photovoltaic module, construct a direct radiation model regarding the tilt angle and azimuth angle of the photovoltaic module; S12, Based on the solar scattering radiation from the tilted surface of the photovoltaic module, construct a scattering radiation model regarding the tilt angle and azimuth angle of the photovoltaic module; S13, Based on the solar reflection radiation of the tilted surface of the photovoltaic module, construct a reflection radiation model about the tilt angle of the photovoltaic module; S14, the direct radiation model, the scattered radiation model and the reflected radiation model are superimposed to obtain the total irradiance model.

3. The photovoltaic array layout optimization method based on dynamic coupling of irradiance and shading according to claim 2, characterized in that, The direct radiation model is as follows: ； in, The hourly direct solar irradiance on the tilted surface of the photovoltaic module. The hourly direct irradiance on a horizontal surface; It is the zenith angle, and , The latitude is the local latitude. The solar declination angle, Solar hour angle; Let be the angle of incidence, and , The tilt angle of the photovoltaic module. The azimuth angle of the photovoltaic module is [missing information]. The angle between the projection of the sun's rays onto the ground and the north-south direction line; The scattered radiation model is as follows: ； in, The hourly solar diffuse irradiance on the tilted surface of the photovoltaic module. The hourly scattered irradiance on the horizontal surface; and These are the annular solar brightness index and the horizontal plane index, respectively. , , For brightness, and , For atmospheric quality, and , This represents the irradiance of the sun's rays on the vertical plane outside the atmosphere. , , , , and All are empirical coefficients; The reflected radiation model is as follows: ； in, The hourly ground-reflected irradiance on the tilted surface of the photovoltaic module; The total irradiance on the horizontal plane, and ; This refers to the ground reflectivity.

4. The photovoltaic array layout optimization method based on dynamic coupling of irradiance and shading according to claim 1, characterized in that, Specifically, S3 is: S31, Based on the effective irradiance model, construct the effective irradiance function per unit area of ​​photovoltaic module in one hour; S32, with maximizing the annual effective irradiance as the optimization objective, construct the objective function based on the effective irradiance function.

5. The photovoltaic array layout optimization method based on dynamic coupling of irradiance and shading according to claim 4, characterized in that, The effective irradiance function is: ； in, , , and The photovoltaic module unit area at the th Effective irradiance, direct irradiance, diffuse irradiance, and reflected irradiance per hour. For the first Hourly shadow loss factor; The objective function is: in, This refers to the total effective irradiance per unit area of ​​a photovoltaic module over one year.

6. The photovoltaic array layout optimization method based on dynamic coupling of irradiance and shading according to claim 1, characterized in that, Specifically, S4 is: S41, Set the population size, maximum number of iterations, and individual constraints, and initialize the current number of iterations. Wherein, the tilt angle of the photovoltaic module, the azimuth angle of the photovoltaic module, and the actual spacing are optimization variables, and the combination of the tilt angle of the photovoltaic module, the azimuth angle of the photovoltaic module, and the actual spacing is used as the population individual; S42, treat the individuals in the population as lemurs, calculate the fitness value of the individuals in the population according to the objective function, and determine the global optimal solution and the local optimal solution based on the fitness value; S43, generate uniform random numbers for the update behavior, and calculate the free risk coefficient based on the current iteration number; S44, determine whether the uniform random number of the update behavior is less than the free risk coefficient. If yes, update the optimization variable based on the global optimal solution under the individual constraints. If no, update the optimization variable based on the local optimal solution under the individual constraints. S45, after optimizing the variable update, let the current iteration number be... Then return to S42 to iterate until the current iteration number reaches the maximum iteration number or the annual effective irradiance change is less than the set threshold, and stop iterating, and output the final global optimal solution.

7. The photovoltaic array layout optimization method based on dynamic coupling of irradiance and shading according to claim 6, characterized in that, The formula for updating and optimizing variables is: ； in, For the first During the nth iteration Individuals of the population at the time The values ​​of the optimization variables, For the first During the nth iteration Individuals of the population at the time The values ​​of the optimization variables, For the first During the nth iteration Local optimal solutions for each optimization variable For the first During the nth iteration The global optimal solution for each optimization variable. ; The update behavior is a uniform random number. Let be the free risk coefficient, and ; For the current iteration number, The maximum number of iterations, and They are respectively The upper and lower limits.

8. A photovoltaic array layout optimization device based on dynamic coupling of irradiance and shading, characterized in that, The device includes a processor, a memory, and a computer program stored in the memory, which, when executed by the processor, implements the photovoltaic array layout optimization method based on dynamic coupling of irradiance and shading as described in any one of claims 1 to 7.

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