A photovoltaic system optimization design method for optimizing benefits

Abstract

The present invention discloses a photovoltaic system optimization design method with the most optimized benefits, and relates to the technical field of photovoltaic system optimization. The method can effectively focus sunlight by installing a microlens array on the surface of a traditional photovoltaic panel, especially significantly improving the power generation efficiency of the photovoltaic system during low light intensity periods (such as morning and evening). Different from the microlens array designed with a fixed spacing in traditional technology, the present invention dynamically adjusts the layout and spacing of the microlens array in combination with real-time lighting conditions, temperature changes and other environmental factors to ensure that the best focusing effect is always maintained under different lighting environments to avoid energy loss. The present invention also monitors the shading of the photovoltaic panel in real time, constructs and evaluates the shading loss index, and adjusts the microlens array in time when the shading is abnormal to reduce the impact of shading on the efficiency of the photovoltaic system. The system can dynamically optimize the configuration by integrating the light, temperature, array spacing and shading data.

Description

Technical Field

[0001] The invention relates to the technical field of photovoltaic system optimization, and in particular to a photovoltaic system optimization design method with optimized benefits. Background Art

[0002] As global energy demand continues to grow, photovoltaic power generation, as a green renewable energy source, has become an important part of the global energy structure transformation. As the core component of the photovoltaic system, the conversion efficiency of photovoltaic panels directly affects the power generation efficiency of the entire system. However, traditional photovoltaic panels face many challenges in practical applications, especially in the morning and evening when the light intensity is low. The photovoltaic panels have a weak ability to receive light, which leads to a decrease in the power generation efficiency of the photovoltaic system.

[0003] To solve this problem, microlens arrays are gradually being applied to the surface of photovoltaic panels as an effective optical focusing technology. Microlens arrays can effectively improve the light utilization rate of photovoltaic panels by focusing sunlight, especially in periods of poor lighting conditions, such as morning and evening, which can significantly improve the power generation efficiency of photovoltaic systems. Traditional microlens arrays mostly use a fixed spacing design. Although it can improve lighting efficiency, since there is no dynamic adjustment based on real-time lighting conditions, this fixed-design microlens array may not perform at its best when lighting conditions change, and may even cause energy loss due to inappropriate focal length and spacing configuration. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a photovoltaic system optimization design method with optimal benefits to solve the problems mentioned in the background technology.

[0005] To achieve the above objectives, the present invention is implemented by the following technical solutions: A photovoltaic system optimization design method for optimizing benefits, comprising the following steps:

[0006] S1. Install a microlens array on the surface of a photovoltaic panel to concentrate sunlight onto the photovoltaic panel. Specifically, the microlens array is a plurality of microlenses arranged in a first initial microlens array. The first initial microlens array is a first rectangular network array. The plurality of microlenses are arranged according to the first rectangular network array. The position of each microlens in the array is determined by spacing parameters on the x and y axes. The lens spacing of each row and each column is equidistant, and a first array spacing d is obtained.

[0007] S2, based on the first initial microlens array and the first array spacing d, real-time collection of illumination condition data and calculation of relevant performance parameters, and calculation to obtain the surface reflectivity R of the photovoltaic panel and the absorption capacity X of the photovoltaic panel;

[0008] S3. Calculate the light-gathering efficiency based on the illumination condition data , and introduce the temperature correction factor , combined with the surface reflectivity R of the photovoltaic panel calculated in step S2 and the absorption capacity X of the photovoltaic panel, a convolutional neural network is used to build and train a microlens efficiency prediction model to construct a real-time lighting efficiency index and evaluating, if the evaluation result is unqualified, generating a first optimization instruction and a second optimization instruction, dynamically adjusting the first initial microlens array and the first array spacing d;

[0009] S4. Collect the area blocked by photovoltaic panels , occlusion time And the angle of occlusion , construct the occlusion loss index And evaluate, when the occlusion effect is abnormal, further modify the first optimization instruction and the second optimization instruction.

[0010] Preferably, S1 comprises:

[0011] S11, collecting size information of photovoltaic panels;

[0012] The size information includes the width W and the length L of the photovoltaic panel, which are combined to obtain the effective absorption area A of the photovoltaic panel, and the total number N of microlenses that need to be covered is calculated by the following formula:

[0013]

[0014]

[0015]

[0016] In the formula, A represents the effective absorption area of ​​the photovoltaic panel, represents the width of the photovoltaic panel, L represents the length of the photovoltaic panel, Indicates the area utilization of the photovoltaic panel, which is set between 90% and 95%; Indicates the actual beam coverage area of ​​a single microlens to the photovoltaic panel, represents pi, represents the diameter of the microlens, represents the array filling factor, which is set between 0.85-0.95 and is used to describe the arrangement density between lenses; N represents the total number of microlenses that need to be covered. represents the area of ​​each lens unit; represents the horizontal distance of each microlens unit in the microlens array in the X-axis direction, Represents the vertical distance of each lens unit in the microlens array in the Y-axis direction; Indicates the target light-concentrating efficiency, which is set between 80-95%, and is determined experimentally based on the material properties of the photovoltaic panel and microlens.

[0017] Preferably, S1 further comprises:

[0018] S12, the microlens array is composed of a plurality of microlenses, which are arranged according to a certain rule to form a first initial microlens array, and is completed based on the following design and setting:

[0019] S121, arrangement of the first initial microlens array: according to the total number N of microlenses to be covered obtained by calculation in S11, a plurality of microlenses are arranged in a first rectangular network array, the position of each microlens in the array is determined by the spacing parameters on the x and y axes, the lens spacing of each row and each column is equidistant, and a first array spacing d is obtained;

[0020] S122, the initial setting range of the first array spacing d is 0.86d opt ≤d≤1.5d opt ; where d opt is the theoretical optimal array spacing, which is experimentally determined based on the material properties of the photovoltaic panels and microlenses.

[0021] Preferably, S2 comprises:

[0022] S21, based on the first initial microlens array and the first array spacing d, collect illumination condition data closely related to the operating state of the photovoltaic panel through an illumination sensor and an environmental monitoring device, wherein the illumination condition data includes:

[0023] Solar radiation intensity I: The solar radiation capacity received per unit area, in W / m 2 ;

[0024] Sun angle θ: the angle between sunlight and the normal line of the photovoltaic panel, ranging from 0°≤θ≤90°;

[0025] Temperature T: current ambient temperature, in °C;

[0026] S22. Based on the collected illumination condition data and the optical characteristics of the microlens array, the surface reflectivity R of the photovoltaic panel is calculated using the following formula:

[0027]

[0028] Where R represents the surface reflectivity of the photovoltaic panel, Represents the intensity of reflected light detected by the optical sensor. Indicates the intensity of collected solar radiation; the surface reflectivity R of the photovoltaic panel indicates the proportion of solar energy reflected by the photovoltaic panel, which is used to evaluate the performance and surface condition of the anti-reflective coating of the photovoltaic panel;

[0029] S23. Calculate the absorption capacity X of the photovoltaic panel based on the surface reflectivity R of the photovoltaic panel. The absorption capacity X of the photovoltaic panel is the actual utilization efficiency of the solar radiation energy of the photovoltaic panel. The calculation formula is as follows:

[0030]

[0031] In the formula, It represents the area utilization rate of the photovoltaic panel, which is set between 90% and 95%. The absorption capacity of the photovoltaic panel X represents the actual utilization efficiency of the photovoltaic panel for solar radiation energy, excluding reflection loss.

[0032] Preferably, S3 includes:

[0033] S31. Calculate the light-gathering efficiency , focusing efficiency represents the current first initial microlens array and the first array spacing d based on the sun angle , the first array spacing d is related to the properties of the optical material, and the formula is as follows:

[0034]

[0035]

[0036] In the formula, represents the correction factor considering the angle of sunlight, cos represents the cosine function, and n is the microlens light characteristic index, which takes a value between 1 and 3; is the light intensity actually irradiated to the photovoltaic panel after being focused by the microlens; I represents the solar radiation intensity;

[0037] S32, introduce temperature correction factor , the calculation formula is:

[0038]

[0039] In the formula, T represents temperature, Indicates the reference temperature, set to 25°C. is the temperature influence coefficient;

[0040] S33. Using a convolutional neural network, construct an initial convolutional neural network model, and train and test the initial convolutional neural network model with the illumination condition data, and use the trained initial convolutional neural network model as a microlens efficiency prediction model, and use the intermediate layer output of the microlens efficiency prediction model as a feature vector to identify feature information, and train and test the microlens efficiency prediction model through the acquired feature information, and combine the absorption capacity X and concentration efficiency of the photovoltaic panel , the trained microlens efficiency prediction model is used as data to run the prediction, and the real-time lighting efficiency index is calculated by the following formula :

[0041] ;

[0042] In the formula, X represents the absorption capacity of the photovoltaic panel, represents the light-gathering efficiency, Represents the temperature correction factor.

[0043] Preferably, the microlens light characteristic index in S31 is obtained by the following steps:

[0044] S311, collecting the measurement of the light deviation ability of the microlens material to obtain the refractive index of the microlens , the refractive index of the microlens Determines the refraction angle and focusing ability of light when it passes through the microlens, including the refractive index of the following different materials:

[0045] Quartz glass: ;

[0046] Polymethyl methacrylate PMMA: ;

[0047] Polycarbonate PC: ;

[0048] S312, collecting the focal length of the real-time microlens. The focal length is the distance at which light converges to a point after passing through the lens. It depends on the radius of curvature of the microlens and the refractive index of the material. The focal length f of the microlens is calculated using the following formula:

[0049]

[0050] Where D is the radius of curvature of the microlens; when the focal length is short, f=5mm, the focusing effect is stronger, but the coverage range is narrower; when the focal length is long, f=15mm, the focusing range is larger, but the light gathering effect is weaker; represents the refractive index of the microlens, obtained from S311;

[0051] S313, collecting the surface shape of the microlens to determine whether it conforms to the relevant curvature or spherical shape, and obtaining the surface shape coefficient j of the microlens. When j=1, it indicates that the surface of the microlens is a spherical lens with uniform light focusing performance. When j≠1, it indicates that the surface of the microlens is an aspherical lens, and the aspherical lens can reduce spherical aberration.

[0052] S314, using the light intensity sensor to collect the light intensity of the microlens, calculate the angle range of the light beam in the horizontal and vertical directions, and obtain the light beam diffusion angle of the microlens by calculating the following formula: :

[0053]

[0054] In the formula, arctan represents the inverse tangent function; represents the diameter of the area illuminated by the light beam, and L represents the distance from the microlens to the light intensity sensor;

[0055] S315, combining the refractive index of the microlens obtained from S311-S314 , the focal length f of the microlens, the surface shape coefficient j of the microlens and the beam diffusion angle of the microlens , after dimensionless processing, the microlens light characteristic index n is calculated by the following formula:

[0056]

[0057] In the formula, , , and Represent the refractive index of the microlens , the focal length f of the microlens, the surface shape coefficient j of the microlens and the beam diffusion angle of the microlens The weight of , , ,and .

[0058] Preferably, S3 further includes:

[0059] S34, preset the target lighting efficiency threshold G, and convert the real-time lighting efficiency index Compare with the target illumination efficiency threshold G to obtain a first evaluation result, including:

[0060] If the real-time lighting efficiency index < target illumination efficiency threshold G, it means that the current light is insufficient and the illumination efficiency is unqualified, and a first optimization instruction is generated, including: switching the current first rectangular network array to a second honeycomb array, reducing the first array spacing d by 5%-15% to form a second array spacing d2, and further adjusting the microlens focal length f;

[0061] If the target lighting efficiency threshold G ≤ real-time lighting efficiency index ≤ target illumination efficiency threshold G*150%, it means that the current illumination efficiency is qualified, and the current first initial microlens array and the first array spacing d are maintained;

[0062] If the real-time lighting efficiency index > target illumination efficiency threshold G*150%, it means that the illumination efficiency is unqualified due to excessive illumination, and there is a risk of hot spot effect of photovoltaic panels. The second optimization instruction is generated, including: maintaining the current first rectangular network array, adjusting and expanding the first array spacing d by 5%-15% to form a third array spacing d3, and further adjusting the focal length f of the microlens.

[0063] Preferably, the method for further adjusting the focal length f of the microlens is:

[0064] The method includes dynamically adjusting the focal length f of the microlens of the microlens array according to the solar radiation intensity I and the solar angle θ. The adjustment method determines the focal length change by calculating the focal length adjustment index F. The calculation formula of the focal length adjustment index F is:

[0065]

[0066] Wherein, A represents the effective absorption area of ​​the photovoltaic panel. When F is greater than the preset focal length adjustment threshold F_max, the focal length f is automatically shortened by 5%-15%; when F is less than the preset focal length adjustment threshold F_min, the focal length f is automatically increased by 5%-15%.

[0067] Preferably, S4 includes:

[0068] S41. Collect the area blocked by photovoltaic panels , occlusion time And the angle of occlusion , after dimensionless processing, the occlusion loss index is calculated by the following formula :

[0069]

[0070] In the formula, represents the area blocked by the photovoltaic panel, Indicates the occlusion time, represents the angle of occlusion, cos represents the cosine function, represents the total area of ​​photovoltaic panels, Indicates the total duration of daily occlusion;

[0071] S42, preset occlusion loss threshold, if the occlusion loss index > shading loss threshold, it means that the shading effect is abnormal, the lighting efficiency of the photovoltaic panel is greatly lost, and a correction instruction is generated;

[0072] If the occlusion loss index ≤ the shading loss threshold, it means that the shading effect is normal, the photovoltaic panels can still effectively receive light, and the existing layout is maintained without the need for correction.

[0073] Preferably, the correction instruction includes:

[0074] The first optimization instruction is modified from reducing the original spacing by 5%-15% to reducing it by 16%-30%, so as to generate a fourth array spacing d4 and execute it;

[0075] The second optimization instruction is modified from the original spacing adjustment expansion of 5%-15% to 3%-8%, forming the fifth array spacing d5 and executing it.

[0076] The present invention provides a photovoltaic system optimization design method with the most optimized benefits. It has the following beneficial effects:

[0077] (1) The present invention not only relies on the focusing effect of the microlens array, but also combines the performance parameters of the photovoltaic panel itself, such as surface reflectivity and the absorption capacity of the photovoltaic panel, which will change with the change of lighting conditions. By calculating these performance parameters in real time and introducing a convolutional neural network (CNN) prediction model, the present invention can dynamically adjust the microlens array according to different lighting conditions to maximize the photoelectric conversion efficiency of the photovoltaic system.

[0078] (2) In practical applications, shading is a common factor of efficiency loss in photovoltaic systems. The present invention collects the shading area, shading angle and shading time of the photovoltaic panel surface in real time, constructs and evaluates the shading loss index, and can timely correct the optimization instructions of the microlens array when the shading effect is abnormal, thereby effectively reducing the efficiency loss of the photovoltaic system caused by shading. By combining the real-time data of lighting conditions, ambient temperature, microlens array spacing and shading factors, the system can dynamically optimize the configuration under different environments and improve the energy utilization efficiency of photovoltaic panels. This can not only effectively improve the power generation capacity of the photovoltaic system, but also enable the photovoltaic system to maintain a high economic benefit in different seasons and time periods, thereby maximizing the benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 The figure is a schematic diagram of the steps of a photovoltaic system optimization design method for optimizing benefits of the present invention. DETAILED DESCRIPTION

[0080] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0081] Example 1

[0082] Currently, existing technologies have not been able to effectively combine the dynamic adjustment of the microlens array with the actual performance parameters of the photovoltaic panel (such as surface reflectivity, the absorption capacity of the photovoltaic panel, temperature correction, etc.) for comprehensive optimization.

[0083] See also Figure 1 The present invention provides a photovoltaic system optimization design method for optimizing benefits, comprising the following steps:

[0084] S1. Install a microlens array on the surface of a photovoltaic panel to concentrate sunlight onto the photovoltaic panel. Specifically, the microlens array is a plurality of microlenses arranged in a first initial microlens array. The first initial microlens array is a first rectangular network array. The plurality of microlenses are arranged according to the first rectangular network array. The position of each microlens in the array is determined by spacing parameters on the x and y axes. The lens spacing of each row and each column is equidistant, and a first array spacing d is obtained.

[0085] S2, based on the first initial microlens array and the first array spacing d, real-time collection of illumination condition data and calculation of relevant performance parameters, and calculation to obtain the surface reflectivity R of the photovoltaic panel and the absorption capacity X of the photovoltaic panel;

[0086] S3. Calculate the light-gathering efficiency based on the illumination condition data , and introduce the temperature correction factor , combined with the surface reflectivity R of the photovoltaic panel calculated in step S2 and the absorption capacity X of the photovoltaic panel, a convolutional neural network is used to build and train a microlens efficiency prediction model to construct a real-time lighting efficiency index and evaluating, if the evaluation result is unqualified, generating a first optimization instruction and a second optimization instruction, dynamically adjusting the first initial microlens array and the first array spacing d;

[0087] S4. Collect the area blocked by photovoltaic panels , occlusion time And the angle of occlusion , construct the occlusion loss index And evaluate, when the occlusion effect is abnormal, further modify the first optimization instruction and the second optimization instruction.

[0088] In this embodiment, the traditional photovoltaic panels cannot fully utilize sunlight under low light intensity conditions such as in the morning and evening, resulting in low power generation efficiency. However, the present invention can focus sunlight and effectively improve the light utilization rate of the photovoltaic panels by installing a microlens array on the surface of the photovoltaic panels, especially during periods of weak light, significantly improving the power generation capacity of the photovoltaic system.

[0089] In the prior art, microlens arrays are usually designed with fixed spacing, which can improve efficiency in some cases, but cannot be dynamically adjusted according to real-time lighting conditions, temperature changes and other environmental factors. Through the innovative design of the present invention, combined with real-time collected lighting condition data, temperature correction factors, etc., the layout and spacing of the microlens array are dynamically adjusted, thereby ensuring that the microlens array can always maintain the best focusing effect under different lighting environments, avoiding the energy loss that may occur in traditional designs.

[0090] The present invention not only relies on the focusing effect of the microlens array, but also combines the performance parameters of the photovoltaic panel itself, such as surface reflectivity and the absorption capacity of the photovoltaic panel, which will change with the change of lighting conditions. By calculating these performance parameters in real time and introducing a convolutional neural network (CNN) prediction model, the present invention can dynamically adjust the microlens array according to different lighting conditions to maximize the photoelectric conversion efficiency of the photovoltaic system.

[0091] In practical applications, occlusion is a common factor of efficiency loss in photovoltaic systems. The present invention collects the occlusion area, occlusion angle and occlusion time of the photovoltaic panel surface in real time, constructs and evaluates the occlusion loss index, and can promptly correct the optimization instructions of the microlens array when the occlusion effect is abnormal, thereby effectively reducing the efficiency loss of the photovoltaic system caused by occlusion. By combining the real-time data of lighting conditions, ambient temperature, microlens array spacing and occlusion factors, the system can dynamically optimize the configuration under different environments and improve the energy efficiency of photovoltaic panels. This can not only effectively improve the power generation capacity of the photovoltaic system, but also enable the photovoltaic system to maintain high economic benefits in different seasons and time periods, thereby maximizing the benefits.

[0092] Example 2

[0093] This embodiment is an explanation of the embodiment 1. Specifically, S1 includes:

[0094] S11, collecting size information of photovoltaic panels;

[0095] The size information includes the width W and the length L of the photovoltaic panel, which are combined to obtain the effective absorption area A of the photovoltaic panel, and the total number N of microlenses that need to be covered is calculated by the following formula:

[0096]

[0097]

[0098]

[0099] In the formula, A represents the effective absorption area of ​​the photovoltaic panel, represents the width of the photovoltaic panel, L represents the length of the photovoltaic panel, Indicates the area utilization of the photovoltaic panel, which is set between 90% and 95%; Indicates the actual beam coverage area of ​​a single microlens to the photovoltaic panel, represents pi, represents the diameter of the microlens, represents the array filling factor, which is set between 0.85-0.95 and is used to describe the arrangement density between lenses; N represents the total number of microlenses that need to be covered. represents the area of ​​each lens unit; represents the horizontal distance of each microlens unit in the microlens array in the X-axis direction, Represents the vertical distance of each lens unit in the microlens array in the Y-axis direction; Indicates the target light-concentrating efficiency, which is set between 80-95%, and is determined experimentally based on the material properties of the photovoltaic panel and microlens.

[0100] By accurately obtaining the width W and length L of the photovoltaic panel, and combining these size information to calculate the effective absorption area A of the photovoltaic panel, it is ensured that the design of the microlens array can cover the effective area of ​​the photovoltaic panel to the maximum extent, thereby optimizing the light utilization rate. According to the effective absorption area A of the photovoltaic panel, combined with the area utilization rate, the lens unit area and the arrangement density of the array, the total number of microlenses N required is accurately calculated. A reasonable design of the number of microlenses avoids excessive or insufficient lens configurations and ensures the maximization of the light focusing effect on the surface of the photovoltaic panel. By calculating the beam coverage area of ​​a single lens, it is ensured that the microlens can effectively focus sunlight onto the photovoltaic panel without over-focusing or the beam being too large to cover the surface of the photovoltaic panel, further improving the photoelectric conversion efficiency of the photovoltaic panel.

[0101] S12, the microlens array is composed of a plurality of microlenses, which are arranged according to a certain rule to form a first initial microlens array, and is completed based on the following design and setting:

[0102] S121, arrangement of the first initial microlens array: according to the total number N of microlenses to be covered obtained by calculation in S11, a plurality of microlenses are arranged in a first rectangular network array, the position of each microlens in the array is determined by the spacing parameters on the x and y axes, the lens spacing of each row and each column is equidistant, and a first array spacing d is obtained;

[0103] By arranging the microlenses in the first rectangular network array according to the total number of microlenses N calculated above, this arrangement can ensure that each microlens is positioned reasonably, avoid redundancy or omission of lens arrangement, and ensure that the optical efficiency of each microlens reaches the optimal state. Under the design that the lens spacing in each row and column is equidistant, the microlens array can evenly cover the entire effective area of ​​the photovoltaic panel, ensuring that the photovoltaic panel can obtain sufficient light focus in each area, avoiding the problem of insufficient local light, and improving the power generation efficiency of the overall photovoltaic system.

[0104] S122, the initial setting range of the first array spacing d is 0.86d opt ≤d≤1.5d opt ; where d opt is the theoretical optimal array spacing, which is experimentally determined based on the material properties of the photovoltaic panels and microlenses.

[0105] In this embodiment, based on the experimental results of the material properties of the photovoltaic panel and the microlens, it can be ensured that the microlens array can always be in the optimal spacing range in practical applications. Too large a spacing may lead to uneven light coverage, and too small a spacing may lead to excessive or overlapping light focus. Reasonable spacing settings avoid these two situations, thereby effectively improving the efficiency of light focusing. The theoretical optimal array spacing d obtained through material property experiments is opt , combined with dynamic adjustment of actual environment and application scenarios, it can optimize array design for different types of photovoltaic panels and microlenses, and improve the efficiency of the overall photovoltaic system.

[0106] Through accurate size collection, reasonable lens number calculation, optimized micro-lens array arrangement, and dynamically adjusted array spacing settings, not only the efficiency of photovoltaic panels in low light periods is guaranteed, but also the overall power generation efficiency of photovoltaic systems under different working conditions can be maximized. Each of the above steps ensures the optimal performance of photovoltaic systems under different environments by comprehensively considering the arrangement of micro-lens arrays, lens spacing, and material properties experimental data, further optimizing the economic benefits and energy efficiency of photovoltaic power generation.

[0107] Example 3

[0108] This embodiment is an explanation of the embodiment 1. Specifically, S2 includes:

[0109] S21, based on the first initial microlens array and the first array spacing d, collect illumination condition data closely related to the operating state of the photovoltaic panel through an illumination sensor and an environmental monitoring device, wherein the illumination condition data includes:

[0110] Solar radiation intensity I: The solar radiation capacity received per unit area, in W / m 2 ;

[0111] Sun angle θ: the angle between sunlight and the normal line of the photovoltaic panel, ranging from 0°≤θ≤90°;

[0112] Temperature T: current ambient temperature, in °C;

[0113] Through light sensors and environmental monitoring equipment, real-time light condition data closely related to the operating status of photovoltaic panels, such as solar radiation intensity I, solar angle θ and temperature T, are collected. These data can fully reflect the environmental changes in the photovoltaic panels, allowing the microlens array to be dynamically optimized and adjusted according to the light conditions.

[0114] S22. Based on the collected illumination condition data and the optical characteristics of the microlens array, the surface reflectivity R of the photovoltaic panel is calculated using the following formula:

[0115]

[0116] Where R represents the surface reflectivity of the photovoltaic panel, Represents the intensity of reflected light detected by the optical sensor. Indicates the intensity of collected solar radiation; the surface reflectivity R of the photovoltaic panel indicates the proportion of solar energy reflected by the photovoltaic panel, which is used to evaluate the performance and surface condition of the anti-reflective coating of the photovoltaic panel;

[0117] The light-gathering efficiency of the microlens array can be adjusted more precisely by calculating the surface reflectivity R. When the reflectivity is high, the light absorption capacity of the photovoltaic panel can be further improved by adjusting the microlens array, thereby optimizing the overall system performance.

[0118] S23. Calculate the absorption capacity X of the photovoltaic panel based on the surface reflectivity R of the photovoltaic panel. The absorption capacity X of the photovoltaic panel is the actual utilization efficiency of the solar radiation energy of the photovoltaic panel. The calculation formula is as follows:

[0119]

[0120] In the formula, Indicates the area utilization of the photovoltaic panel, which is set between 90% and 95%. The absorption capacity X of the photovoltaic panel indicates the actual utilization efficiency of the photovoltaic panel for solar radiation energy, eliminating reflection losses. By calculating the absorption capacity X of the photovoltaic panel and combining it with the data of the surface reflectivity R, the design of the microlens array can be dynamically optimized. For example, in the case of low absorption capacity, the parameters of the microlens array can be adjusted to enhance the solar radiation absorption capacity of the photovoltaic panel, thereby improving the power generation efficiency.

[0121] In this embodiment, by collecting accurate illumination condition data, calculating the surface reflectivity and absorption capacity, and combining the optical characteristics of the microlens array for dynamic adjustment, it is not only possible to effectively optimize the power generation efficiency of the photovoltaic panel, but also to adapt to changes in different environmental conditions. These optimization measures ensure that the photovoltaic system can continue to operate at the best performance state under various illumination environments, thereby maximizing the power generation efficiency and improving the economic benefits and energy utilization efficiency of the photovoltaic system.

[0122] Example 4

[0123] This embodiment is an explanation of the embodiment 1. Specifically, S3 includes:

[0124] S31. Calculate the light-gathering efficiency , focusing efficiency represents the current first initial microlens array and the first array spacing d based on the sun angle , the first array spacing d is related to the properties of the optical material, and the formula is as follows:

[0125]

[0126]

[0127] In the formula, represents the correction factor considering the angle of sunlight, cos represents the cosine function, and n is the microlens light characteristic index, which takes a value between 1 and 3; is the light intensity actually irradiated to the photovoltaic panel after being focused by the microlens; I represents the solar radiation intensity;

[0128] Concentrating efficiency Directly affects the light gathering effect of the microlens array. By calculating the concentration efficiency related to the sun angle, array spacing and optical material properties, the performance of the microlens array can be more accurately evaluated and optimized to ensure that more solar energy is effectively concentrated on the surface of the photovoltaic panel and improve power generation efficiency.

[0129] S32, introduce temperature correction factor , the calculation formula is:

[0130]

[0131] In the formula, T represents temperature, Indicates the reference temperature, set to 25°C. is the temperature effect coefficient; temperature is an important factor affecting the efficiency of photovoltaic panels and microlenses. High temperature usually leads to a decrease in photoelectric conversion efficiency. By introducing the temperature correction factor, the impact of temperature changes on the performance of the photovoltaic system can be accurately compensated to ensure that the efficiency of the system can be properly adjusted under different temperature conditions.

[0132] S33. Using a convolutional neural network, construct an initial convolutional neural network model, and train and test the initial convolutional neural network model with the illumination condition data, and use the trained initial convolutional neural network model as a microlens efficiency prediction model, and use the intermediate layer output of the microlens efficiency prediction model as a feature vector to identify feature information, and train and test the microlens efficiency prediction model through the acquired feature information, and combine the absorption capacity X and concentration efficiency of the photovoltaic panel , the trained microlens efficiency prediction model is used as data to run the prediction, and the real-time lighting efficiency index is calculated by the following formula :

[0133] ;

[0134] In the formula, X represents the absorption capacity of the photovoltaic panel, represents the light-gathering efficiency, Represents the temperature correction factor.

[0135] By calculating the real-time lighting efficiency index ,Combined with the microlens efficiency prediction model, the system can dynamically monitor the lighting conditions and microlens efficiency, and adjust the working state of the microlens array in time, thereby maximizing the photoelectric conversion efficiency of the photovoltaic system.

[0136] In this embodiment, this embodiment not only optimizes the design of the microlens array, but also enhances the adaptability of the photovoltaic system to environmental changes by calculating the focusing efficiency, introducing the temperature correction factor, and using the convolutional neural network CNN to predict the microlens efficiency. In particular, by accurately predicting the microlens efficiency and adjusting the working state of the photovoltaic system in real time, the operating efficiency of the overall system can be improved, and the performance degradation caused by temperature fluctuations and changes in lighting conditions can be reduced. Ultimately, this optimized design can significantly improve the performance, stability and economic benefits of the photovoltaic power generation system, ensuring its efficient operation under various external environments.

[0137] Example 5

[0138] This embodiment is an explanation of the embodiment 1. Specifically, the microlens light characteristic index in S31 is obtained by the following steps:

[0139] S311, collecting the measurement of the light deviation ability of the microlens material to obtain the refractive index of the microlens , the refractive index of the microlens Determines the refraction angle and focusing ability of light when it passes through the microlens, including the refractive index of the following different materials:

[0140] Quartz glass: ;

[0141] Polymethyl methacrylate PMMA: ;

[0142] Polycarbonate PC: The refractive index of the microlens is a key factor in determining the deflection angle and focusing ability of light when it passes through the lens. Different materials (such as quartz glass, PMMA, PC, etc.) have different refractive indices. After collecting this data, the light focusing performance of the microlens can be optimized according to the characteristics of the material.

[0143] S312, collecting the focal length of the real-time microlens. The focal length is the distance at which light converges to a point after passing through the lens. It depends on the radius of curvature of the microlens and the refractive index of the material. The focal length f of the microlens is calculated using the following formula:

[0144]

[0145] In the formula, D represents the radius of curvature of the microlens; when the focal length is short, f=5mm, the focusing effect is stronger, but the coverage is narrower; when the focal length is long, f=15mm, the focusing range is larger, but the light gathering effect is weaker; the focal length is the core parameter that affects the light focusing effect. The shorter the focal length, the stronger the focusing effect, but the narrower the coverage. The longer the focal length, the weaker the focusing effect, but the wider the coverage. By collecting focal length data in real time, the focusing effect of the microlens can be accurately controlled to ensure the best use of light energy.

[0146] S313, collecting the surface shape of the microlens to determine whether it conforms to the relevant curvature or spherical shape, and obtaining the surface shape coefficient j of the microlens. When j=1, it indicates that the surface of the microlens is a spherical lens with uniform light focusing performance. When j≠1, it indicates that the surface of the microlens is an aspherical lens, and the aspherical lens can reduce spherical aberration.

[0147] The surface shape factor j is an important factor affecting the light focusing performance of the microlens. Spherical lenses have uniform focusing performance, while aspherical lenses can effectively reduce spherical aberration and provide more precise beam focusing. This data helps to design more efficient microlens arrays.

[0148] S314, using the light intensity sensor to collect the light intensity of the microlens, calculate the angle range of the light beam in the horizontal and vertical directions, and obtain the light beam diffusion angle of the microlens by calculating the following formula: :

[0149]

[0150] In the formula, represents the diameter of the light beam irradiation area, L represents the distance from the microlens to the light intensity sensor, and arctan represents the inverse tangent function; represents the refractive index of the microlens, obtained from S311;

[0151] By collecting the light intensity data of the microlens, the angular range of the light beam in the horizontal and vertical directions can be calculated to obtain the beam diffusion angle. A smaller diffusion angle indicates a stronger focusing effect, while a larger diffusion angle indicates a wider beam and a wider coverage area. This data helps to adjust the design of the microlens so that the diffusion range of the light beam matches the needs of the photovoltaic panel.

[0152] S315, combining the refractive index of the microlens obtained from S311-S314 , the focal length f of the microlens, the surface shape coefficient j of the microlens and the beam diffusion angle of the microlens , after dimensionless processing, the microlens light characteristic index n is calculated by the following formula:

[0153]

[0154] In the formula, , , and Represent the refractive index of the microlens , the focal length f of the microlens, the surface shape coefficient j of the microlens and the beam diffusion angle of the microlens The weight of , , ,and .

[0155] In this embodiment, through the above steps, the light characteristic index n of the microlens can provide an accurate optimization basis for the light energy gathering effect of the photovoltaic system. Collecting and processing data on multiple factors such as material refractive index, focal length, surface shape, and beam diffusion angle helps to design an efficient microlens array. Ultimately, based on these optimized designs, the photovoltaic system can achieve higher energy conversion efficiency under various lighting conditions, improving the stability of the system and long-term operating benefits.

[0156] Example 6

[0157] This embodiment is explained in Embodiment 4. Specifically, S3 also includes:

[0158] S34, preset the target lighting efficiency threshold G, and convert the real-time lighting efficiency index Compare with the target illumination efficiency threshold G to obtain a first evaluation result, including:

[0159] If the real-time lighting efficiency index < target illumination efficiency threshold G, it means that the current light is insufficient and the illumination efficiency is unqualified, and a first optimization instruction is generated, including: switching the current first rectangular network array to a second honeycomb array, reducing the first array spacing d by 5%-15% to form a second array spacing d2, and further adjusting the microlens focal length f;

[0160] If the target lighting efficiency threshold G ≤ real-time lighting efficiency index ≤ target illumination efficiency threshold G*150%, it means that the current illumination efficiency is qualified, and the current first initial microlens array and the first array spacing d are maintained;

[0161] If the real-time lighting efficiency index > target illumination efficiency threshold G*150%, it means that the illumination efficiency is unqualified due to excessive illumination, and there is a risk of hot spot effect of photovoltaic panels. The second optimization instruction is generated, including: maintaining the current first rectangular network array, adjusting and expanding the first array spacing d by 5%-15% to form a third array spacing d3, and further adjusting the focal length f of the microlens.

[0162] Adjustment of the microlens array can be achieved through the microlens mounting rail. The rail system allows the microlens array to be fine-tuned in the horizontal and vertical directions to optimize the lighting efficiency under lighting conditions. Adjustment of the array spacing: The spacing d of the microlens array can be changed by adjusting the fixtures on the rail. By reducing or increasing the array spacing d, the beam coverage of the microlens can be effectively adjusted to optimize the focus and distribution of the light and adapt to different solar angles and light intensities. The focal length f of the microlens is achieved through the focal length adjustment rail. In this case, the focal length adjustment rail has a high-precision adjustment capability, which can dynamically adjust the focal length of the microlens according to the real-time light intensity and solar angle, ensuring that the focus is always at the best receiving position of the photovoltaic panel.

[0163] In this embodiment, when the real-time illumination efficiency is insufficient, the system generates an optimization instruction, switches the current first rectangular network array to the second honeycomb array, reduces the array spacing (5%-15%), and adjusts the focal length of the microlens. This strategy helps to enhance the light focusing effect, thereby improving the illumination efficiency and compensating for the defect of insufficient illumination. By changing the arrangement and spacing of the array, the focusing and absorption of light can be effectively enhanced, and the photoelectric conversion efficiency of the photovoltaic panel can be improved. If the current illumination efficiency index is qualified, maintaining the existing configuration can avoid excessive adjustment, save resources and reduce unnecessary system adjustments, thereby reducing energy consumption and operation and maintenance costs. When the illumination is too strong, there may be a risk of hot spot effect of the photovoltaic panel, resulting in reduced photoelectric conversion efficiency. At this time, the system generates a second optimization instruction, expands the array spacing (5%-15%) and adjusts the focal length of the microlens, weakens the excessive illumination, thereby avoiding the hot spot effect and ensuring the stable operation of the photovoltaic system. Expanding the array spacing can effectively reduce the impact of excessive illumination and reduce the hot spot problem caused by excessive light focusing, thereby improving the safety and stability of the photovoltaic system.

[0164] Example 7

[0165] This embodiment is explained in Embodiment 6. Specifically, the method for further adjusting the focal length f of the microlens is:

[0166] The method includes dynamically adjusting the focal length f of the microlens of the microlens array according to the solar radiation intensity I and the solar angle θ. The adjustment method determines the focal length change by calculating the focal length adjustment index F. The calculation formula of the focal length adjustment index F is:

[0167]

[0168] In the formula, represents the area blocked by the photovoltaic panel, Indicates the occlusion time, Indicates the angle of occlusion, cos represents the cosine function, A represents the effective absorption area of ​​the photovoltaic panel, and when F is greater than the preset focal length adjustment threshold F_max, the focal length f is automatically shortened by 5%-15%; when F is less than the preset focal length adjustment threshold F_min, the focal length f is automatically increased by 5%-15%.

[0169] In this embodiment, the method can dynamically adjust the focal length of the microlens according to the real-time light intensity and the sun angle, thereby maintaining the optimal working state of the photovoltaic panel under different lighting conditions and improving the photoelectric conversion efficiency. By adjusting the focal length, the focusing effect of the light is optimized, so that the photovoltaic panel can absorb solar energy to the maximum extent under different lighting conditions, thereby improving the overall lighting efficiency.

[0170] Example 8

[0171] This embodiment is an explanation of the embodiment 1. Specifically, S4 includes:

[0172] S41. Collect the area blocked by photovoltaic panels , occlusion time And the angle of occlusion , after dimensionless processing, the occlusion loss index is calculated by the following formula :

[0173]

[0174] In the formula, represents the total area of ​​photovoltaic panels, Indicates the total duration of daily occlusion;

[0175] S42, preset occlusion loss threshold, if the occlusion loss index > shading loss threshold, it means that the shading effect is abnormal, the lighting efficiency of the photovoltaic panel is greatly lost, and a correction instruction is generated;

[0176] If the occlusion loss index ≤ the shading loss threshold, it means that the shading effect is normal, the photovoltaic panels can still effectively receive light, and the existing layout is maintained without the need for correction.

[0177] The correction instructions include:

[0178] The first optimization instruction is modified from the original spacing reduction of 5%-15% to 16%-30% to generate and execute the fourth array spacing d4; the reduction of the array spacing is increased (16%-30%), which can effectively reduce the shading effect and ensure that the photovoltaic panel can receive more light. This helps to restore the lighting efficiency of the photovoltaic panel, especially under the influence of long-term shading.

[0179] The second optimization instruction is modified from the original spacing adjustment expansion of 5%-15% to 3%-8%, forming the fifth array spacing d5 and executing it. The original 5%-15% expansion range is reduced to 3%-8%, avoiding excessive expansion of the array due to excessive adjustment, resulting in unnecessary waste of light. Moderately expanding the array spacing can improve the uniformity of light reception and avoid hot spot effects or excessive light caused by too small spacing, especially when there is sufficient light but no excessive occlusion. At this time, there is an occlusion anomaly, so the original 5%-15% expansion range is reduced to 3%-8% to make up for the occlusion loss caused by the occlusion anomaly.

[0180] In this embodiment, the shading loss index The larger the value, the more serious the shading effect on the photovoltaic panel, and the greater the loss of lighting efficiency. , it is considered that the lighting efficiency of the photovoltaic panel has been greatly lost, and the photovoltaic panel is abnormally affected by the shading, and needs to be adjusted. When adjusting the spacing, the spacing will be increased and the range will be reduced to ensure that the irradiation area of ​​the photovoltaic panel will not be blocked too much.

[0181] The threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by technicians in this field for each group of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0182] The above formulas are obtained by collecting a large amount of data for software simulation and selecting a formula that is close to the actual value. The coefficients in the formula are set by technical personnel in this field according to actual conditions. The above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited to this. Any technical personnel familiar with the technical field within the technical scope disclosed by the present invention, according to the technical solution and the inventive concept of the present invention, make equivalent replacement or change, which should be covered within the protection scope of the present invention.

Claims

1. A photovoltaic system optimization design method for optimizing benefits, characterized in that: The following steps are involved: S1. Install a microlens array on the surface of a photovoltaic panel to concentrate sunlight onto the photovoltaic panel. Specifically, the microlens array is a plurality of microlenses arranged in a first initial microlens array. The first initial microlens array is a first rectangular network array. The plurality of microlenses are arranged according to the first rectangular network array. The position of each microlens in the array is determined by spacing parameters on the x and y axes. The lens spacing of each row and each column is equidistant, and a first array spacing d is obtained. S2, based on the first initial microlens array and the first array spacing d, real-time collection of illumination condition data and calculation of relevant performance parameters, and calculation to obtain the surface reflectivity R of the photovoltaic panel and the absorption capacity X of the photovoltaic panel; S3. Calculate the light-gathering efficiency based on the illumination condition data , and introduce the temperature correction factor , combined with the surface reflectivity R of the photovoltaic panel calculated in step S2 and the absorption capacity X of the photovoltaic panel, a convolutional neural network is used to build and train a microlens efficiency prediction model to construct a real-time lighting efficiency index and evaluating, if the evaluation result is unqualified, generating a first optimization instruction and a second optimization instruction, dynamically adjusting the first initial microlens array and the first array spacing d; S4. Collect the area blocked by photovoltaic panels , occlusion time And the angle of occlusion , construct the occlusion loss index And evaluate, when the occlusion effect is abnormal, further modify the first optimization instruction and the second optimization instruction.

2. A photovoltaic system optimization design method for optimizing benefits according to claim 1, characterized in that: S1 includes: S11, collecting size information of photovoltaic panels; The size information includes the width W and the length L of the photovoltaic panel, which are combined to obtain the effective absorption area A of the photovoltaic panel, and the total number N of microlenses that need to be covered is calculated by the following formula: In the formula, A represents the effective absorption area of ​​the photovoltaic panel, represents the width of the photovoltaic panel, L represents the length of the photovoltaic panel, Indicates the area utilization of the photovoltaic panel, which is set between 90% and 95%; Indicates the actual beam coverage area of ​​a single microlens to the photovoltaic panel, represents pi, represents the diameter of the microlens, Indicates the array filling factor, which is set between 0.85-0.95 and is used to describe the arrangement density between lenses; N represents the total number of microlenses that need to be covered, represents the area of ​​each lens unit; represents the horizontal distance of each microlens unit in the microlens array in the X-axis direction, represents the vertical distance of each lens unit in the microlens array in the Y-axis direction, Indicates the target light-concentrating efficiency, which is set between 80-95%, and is determined experimentally based on the material properties of the photovoltaic panel and microlens.

3. A photovoltaic system optimization design method for optimizing benefits according to claim 2, characterized in that: S1 also includes: S12, the microlens array is composed of a plurality of microlenses, which are arranged according to a certain rule to form a first initial microlens array, and is completed based on the following design and setting: S121, arrangement of the first initial microlens array: according to the total number N of microlenses to be covered obtained by calculation in S11, a plurality of microlenses are arranged in a first rectangular network array, the position of each microlens in the array is determined by the spacing parameters on the x and y axes, the lens spacing of each row and each column is equidistant, and a first array spacing d is obtained; S122, the initial setting range of the first array spacing d is 0.86d opt ≤d≤1.5d opt ; where d opt is the theoretical optimal array spacing, which is experimentally determined based on the material properties of the photovoltaic panels and microlenses.

4. The photovoltaic system optimization design method for optimizing benefits according to claim 2, characterized in that S2 include: S21, based on the first initial microlens array and the first array spacing d, collect illumination condition data closely related to the operating state of the photovoltaic panel through an illumination sensor and an environmental monitoring device, wherein the illumination condition data includes: Solar radiation intensity I: The solar radiation capacity received per unit area, in W / m 2 ; Sun angle θ: the angle between sunlight and the normal line of the photovoltaic panel, ranging from 0°≤θ≤90°; Temperature T: current ambient temperature, in °C; S22. Based on the collected illumination condition data and the optical characteristics of the microlens array, the surface reflectivity R of the photovoltaic panel is calculated using the following formula: Where R represents the surface reflectivity of the photovoltaic panel, Represents the intensity of reflected light detected by the optical sensor. Indicates the intensity of collected solar radiation; the surface reflectivity R of the photovoltaic panel indicates the proportion of solar energy reflected by the photovoltaic panel, which is used to evaluate the performance and surface condition of the anti-reflective coating of the photovoltaic panel; S23. Calculate the absorption capacity X of the photovoltaic panel based on the surface reflectivity R of the photovoltaic panel. The absorption capacity X of the photovoltaic panel is the actual utilization efficiency of the solar radiation energy of the photovoltaic panel. The calculation formula is as follows: In the formula, It represents the area utilization rate of the photovoltaic panel, which is set between 90% and 95%. The absorption capacity of the photovoltaic panel X represents the actual utilization efficiency of the photovoltaic panel for solar radiation energy, excluding reflection loss.

5. The photovoltaic system optimization design method for optimizing benefits according to claim 4, characterized in that S3 include: S31. Calculate the light-gathering efficiency , focusing efficiency represents the current first initial microlens array and the first array spacing d based on the sun angle , the first array spacing d is related to the properties of the optical material, and the formula is as follows: In the formula, represents the correction factor considering the angle of sunlight, cos represents the cosine function, and n is the microlens light characteristic index, which takes a value between 1 and 3; is the light intensity actually irradiated to the photovoltaic panel after being focused by the microlens; I represents the solar radiation intensity; S32, introduce temperature correction factor , the calculation formula is: In the formula, T represents temperature, Indicates the reference temperature, set to 25°C. is the temperature influence coefficient; S33. Using a convolutional neural network, construct an initial convolutional neural network model, and train and test the initial convolutional neural network model with the illumination condition data, and use the trained initial convolutional neural network model as a microlens efficiency prediction model, and use the intermediate layer output of the microlens efficiency prediction model as a feature vector to identify feature information, and train and test the microlens efficiency prediction model through the acquired feature information, and combine the absorption capacity X and concentration efficiency of the photovoltaic panel , the trained microlens efficiency prediction model is used as data to run the prediction, and the real-time lighting efficiency index is calculated by the following formula : ； In the formula, X represents the absorption capacity of the photovoltaic panel, represents the light-gathering efficiency, Represents the temperature correction factor.

6. A photovoltaic system optimization design method for optimizing benefits according to claim 5, characterized in that: The microlens light characteristic index in S31 is obtained by the following steps: S311, collecting the measurement of the light deviation ability of the microlens material to obtain the refractive index of the microlens , the refractive index of the microlens Determines the refraction angle and focusing ability of light when it passes through the microlens, including the refractive index of the following different materials: Quartz glass: ; Polymethyl methacrylate PMMA: ; Polycarbonate PC: ; S312, collecting the focal length of the real-time microlens. The focal length is the distance at which light converges to a point after passing through the lens. It depends on the radius of curvature of the microlens and the refractive index of the material. The focal length f of the microlens is calculated using the following formula: Where D is the radius of curvature of the microlens; when the focal length is short, f=5mm, the focusing effect is stronger, but the coverage range is narrower; when the focal length is long, f=15mm, the focusing range is larger, but the light gathering effect is weaker; represents the refractive index of the microlens, obtained from S311; S313, collecting the surface shape of the microlens to determine whether it conforms to the relevant curvature or spherical shape, and obtaining the surface shape coefficient j of the microlens. When j=1, it indicates that the surface of the microlens is a spherical lens with uniform light focusing performance. When j≠1, it indicates that the surface of the microlens is an aspherical lens, and the aspherical lens can reduce spherical aberration. S314, using the light intensity sensor to collect the light intensity of the microlens, calculate the angle range of the light beam in the horizontal and vertical directions, and obtain the light beam diffusion angle of the microlens by calculating the following formula: : Where, arctan represents the inverse tangent function; represents the diameter of the area illuminated by the light beam, and L represents the distance from the microlens to the light intensity sensor; S315, combining the refractive index of the microlens obtained from S311-S314 , the focal length f of the microlens, the surface shape coefficient j of the microlens and the beam diffusion angle of the microlens , after dimensionless processing, the microlens light characteristic index n is calculated by the following formula: In the formula, , , and Represent the refractive index of the microlens , the focal length f of the microlens, the surface shape coefficient j of the microlens and the beam diffusion angle of the microlens The weight of , , , ,and .

7. A photovoltaic system optimization design method for optimizing benefits according to claim 6, characterized in that: S3 also includes: S34, preset the target lighting efficiency threshold G, and convert the real-time lighting efficiency index Compare with the target illumination efficiency threshold G to obtain a first evaluation result, including: If the real-time lighting efficiency index < target illumination efficiency threshold G, it means that the current light is insufficient and the illumination efficiency is unqualified, and a first optimization instruction is generated, including: switching the current first rectangular network array to a second honeycomb array, reducing the first array spacing d by 5%-15% to form a second array spacing d2, and further adjusting the microlens focal length f; If the target lighting efficiency threshold G ≤ real-time lighting efficiency index ≤ target illumination efficiency threshold G*150%, it means that the current illumination efficiency is qualified, and the current first initial microlens array and the first array spacing d are maintained; If the real-time lighting efficiency index > target illumination efficiency threshold G*150%, it means that the illumination efficiency is unqualified due to excessive illumination, and there is a risk of hot spot effect of photovoltaic panels. The second optimization instruction is generated, including: maintaining the current first rectangular network array, adjusting and expanding the first array spacing d by 5%-15% to form a third array spacing d3, and further adjusting the focal length f of the microlens.

8. A photovoltaic system optimization design method for optimizing benefits according to claim 7, characterized in that: The method for further adjusting the focal length f of the microlens is: The method includes dynamically adjusting the focal length f of the microlens of the microlens array according to the solar radiation intensity I and the solar angle θ. The adjustment method determines the focal length change by calculating the focal length adjustment index F. The calculation formula of the focal length adjustment index F is: Wherein, A represents the effective absorption area of ​​the photovoltaic panel. When F is greater than the preset focal length adjustment threshold F_max, the focal length f is automatically shortened by 5%-15%; when F is less than the preset focal length adjustment threshold F_min, the focal length f is automatically increased by 5%-15%.

9. The photovoltaic system optimization design method for optimizing benefits according to claim 1, characterized in that S4 include: S41. Collect the area blocked by photovoltaic panels , occlusion time And the angle of occlusion , after dimensionless processing, the occlusion loss index is calculated by the following formula : In the formula, represents the area blocked by the photovoltaic panel, Indicates the occlusion time, represents the angle of occlusion, cos represents the cosine function, represents the total area of ​​photovoltaic panels, Indicates the total duration of daily occlusion; S42, preset occlusion loss threshold, if the occlusion loss index > shading loss threshold, it means that the shading effect is abnormal, the lighting efficiency of the photovoltaic panel is greatly lost, and a correction instruction is generated; If the occlusion loss index ≤ the shading loss threshold, it means that the shading effect is normal, the photovoltaic panels can still effectively receive light, and the existing layout is maintained without the need for correction.

10. A photovoltaic system optimization design method for optimizing benefits according to claim 9, characterized in that: The correction instructions include: The first optimization instruction is modified from reducing the original spacing by 5%-15% to reducing it by 16%-30%, so as to generate a fourth array spacing d4 and execute it; The second optimization instruction is modified from the original spacing adjustment expansion of 5%-15% to 3%-8%, forming the fifth array spacing d5 and executing it.

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