Photovoltaic system optimization design method for income optimization

Abstract

The invention discloses a profit-optimized photovoltaic system optimization design method, and relates to the technical field of photovoltaic system optimization, and the method can effectively focus sunlight through installing a micro-lens array on the surface of a traditional photovoltaic panel, and can significantly improve the power generation efficiency of a photovoltaic system especially in low illumination intensity periods (such as the morning and the evening). Different from a micro-lens array designed at a fixed interval in the traditional technology, the micro-lens array has the advantages that the layout and the interval of the micro-lens array are dynamically adjusted in combination with real-time illumination conditions, temperature changes and other environmental factors, the optimal condensation effect is always kept in different illumination environments, and energy loss is avoided. In addition, the shielding condition of the photovoltaic panel is monitored in real time, a shielding loss index is constructed and evaluated, and the micro-lens array is adjusted in time when shielding is abnormal, so that the influence of shielding on the efficiency of the photovoltaic system is reduced. By integrating illumination, temperature, array spacing and shielding data, the system can dynamically optimize configuration.

Description

Technical Field

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

[0002] As global energy demand continues to grow, photovoltaic power generation, as a green and renewable energy source, has become a crucial component of the global energy transition. As the core component of a photovoltaic system, the conversion efficiency of photovoltaic panels directly impacts the overall system's power generation efficiency. However, traditional photovoltaic panels face numerous challenges in practical application, particularly during periods of low sunlight, such as morning and evening, when panels' ability to receive less sunlight decreases, leading to reduced power generation efficiency.

[0003] To address this issue, microlens arrays, as an effective optical focusing technology, are gradually being applied to the surface of photovoltaic panels. By focusing sunlight, microlens arrays can effectively improve the light utilization rate of photovoltaic panels, especially during periods of poor lighting conditions, such as morning and evening, and can significantly improve the power generation efficiency of photovoltaic systems. Traditional microlens arrays mostly use a fixed-pitch design. Although this can improve lighting efficiency, because they do not dynamically adjust according to real-time lighting conditions, this fixed-design microlens array may not perform optimally when lighting conditions change, and may even cause energy loss due to inappropriate focal length and pitch configuration. Summary of the Invention

[0004] In view of the shortcomings of the existing technology, 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 through the following technical solutions: A photovoltaic system optimization design method for optimizing benefits, comprising the following steps: S1. Installing a microlens array on the surface of a photovoltaic panel to concentrate sunlight onto the photovoltaic panel, wherein the microlens array is specifically a first initial microlens array formed by arranging a plurality of microlenses. 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 spacing between lenses in each row and each column is equidistant, thereby obtaining a first array spacing d. S2. Based on the first initial microlens array and the first array spacing d, real-time data on illumination conditions is collected and relevant performance parameters are calculated to obtain the surface reflectivity R and the absorption capacity X of the photovoltaic panel. S3. Calculate the focusing efficiency based on the lighting condition data , and introduce a 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, and if the evaluation result is unqualified, generating a first optimization instruction and a second optimization instruction, and 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.

[0006] Preferably, S1 includes: S11, collecting size information of photovoltaic panels; The size information includes the width W and length L of the photovoltaic panel. The effective absorption area A of the photovoltaic panel is obtained by calculation, and the total number N of microlenses required to be covered is calculated using the following formula: Where A represents the effective absorption area of ​​the photovoltaic panel, Indicates the width of the photovoltaic panel, L indicates the length of the photovoltaic panel, Indicates the area utilization rate of the photovoltaic panel, 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 fill 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, set between 80-95%, determined experimentally based on the material properties of the photovoltaic panel and microlens.

[0007] Preferably, S1 further 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 settings: S121, arrangement of a first initial microlens array: Based on the total number N of microlenses to be covered calculated in S11, a plurality of microlenses are arranged in a first rectangular network array, the position of each microlens in the array being determined by spacing parameters on the x and y axes, and the spacing between lenses in each row and column being equidistant, to obtain a first array spacing d; 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, determined experimentally based on the material properties of the photovoltaic panels and microlenses.

[0008] Preferably, S2 includes: S21. Based on the first initial microlens array and the first array spacing d, light condition data closely related to the operating state of the photovoltaic panel is collected through a light sensor and an environmental monitoring device. The light condition data includes: Solar radiation intensity I: the ability of a unit area to receive solar radiation, in W / m 2 ; Solar 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 data and the optical characteristics of the microlens array, calculate the surface reflectivity R of the photovoltaic panel using the following formula: Where R represents the surface reflectivity of the photovoltaic panel, Indicates the intensity of reflected light detected by the optical sensor. Indicates the intensity of collected solar radiation; the surface reflectivity R of a photovoltaic panel indicates the proportion of solar energy reflected by the photovoltaic panel, and is used to evaluate the performance of the anti-reflective coating and the surface condition 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: Where, It represents the area utilization rate of the photovoltaic panel and is set between 90% and 95%. The absorption capacity X of the photovoltaic panel represents the actual utilization efficiency of the photovoltaic panel for solar radiation energy, excluding reflection loss.

[0009] Preferably, S3 includes: 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: Where, 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: Where T represents temperature, Indicates the reference temperature, set to 25℃, is the temperature influence coefficient; S33. Use convolutional neural network to build the initial model of convolutional neural network, and train and test the initial model of convolutional neural network with illumination condition data, and use the trained initial model of convolutional neural network as the microlens efficiency prediction model. At the same time, use the intermediate layer output of the microlens efficiency prediction model as the feature vector to identify feature information, and train and test the microlens efficiency prediction model through the obtained feature information, combining the absorption capacity X and concentration efficiency of photovoltaic panels. , 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 : ; Where X represents the absorption capacity of the photovoltaic panel, represents the light-gathering efficiency, Represents the temperature correction factor.

[0010] Preferably, the microlens light characteristic index in S31 is obtained by the following steps: S311. Collect 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 passing through the microlens, including the refractive index of the following different materials: Quartz glass: ; Polymethyl methacrylate PMMA: ; Polycarbonate PC: ; S312. Collect 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 curvature radius 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 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 microlens surface is a spherical lens with uniform light focusing performance. When j≠1, it indicates that the microlens surface is an aspherical lens, which can reduce spherical aberration. S314. Use 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 calculate the light beam diffusion angle of the microlens using the following formula: : Where, arctan represents the inverse tangent function; represents the diameter of the light beam irradiation area, 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 , microlens focal length f, microlens surface shape coefficient j and microlens beam spread angle , after dimensionless processing, the microlens light characteristic index n is calculated using the following formula: Where, 、 、 and Represent the refractive index of the microlens , microlens focal length f, microlens surface shape coefficient j and microlens beam spread angle The weight of , , ,and .

[0011] Preferably, S3 further includes: S34, preset the target lighting efficiency threshold G, and convert the real-time lighting efficiency index Compare with the target lighting efficiency threshold G to obtain a first evaluation result, including: If the real-time lighting efficiency index < target illumination efficiency threshold G, it indicates that the current light is insufficient, resulting in unqualified illumination efficiency. 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%, indicating 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 If the target illumination efficiency threshold G*150% is exceeded, it indicates that the illumination efficiency is unqualified due to excessive illumination, and there is a risk of hotspot effect in the photovoltaic panel. The second optimization instruction is generated, including: maintaining the current first rectangular network array, adjusting the first array spacing d to expand by 5%-15% to form a third array spacing d3, and further adjusting the microlens focal length f.

[0012] Preferably, 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: Where 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%.

[0013] Preferably, S4 includes: S41. Collect the area blocked by photovoltaic panels , occlusion time and the angle of occlusion After dimensionless processing, the occlusion loss index is calculated using the following formula: : Where, represents the area blocked by the photovoltaic panels, Indicates the time of occlusion, Indicates the angle of occlusion, cos represents the cosine function, represents the total area of ​​photovoltaic panels, Indicates the total daily occlusion duration; S42, preset occlusion loss threshold, if the occlusion loss index > the shading loss threshold, indicating that the shading effect is abnormal, the lighting efficiency of the photovoltaic panel is greatly reduced, and a correction instruction is generated; If the occlusion loss index If the value is less than or equal to 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.

[0014] Preferably, the correction instruction includes: The first optimization instruction is modified from reducing the original spacing by 5%-15% to reducing it by 16%-30%, so as to generate the 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.

[0015] The present invention provides a photovoltaic system optimization design method with the best benefits. It has the following beneficial effects: (1) The present invention not only relies on the focusing effect of the microlens array, but also incorporates the performance parameters of the photovoltaic panel itself, such as surface reflectivity and the absorption capacity of the photovoltaic panel, which vary with changing 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 photovoltaic system's photoelectric conversion efficiency.

[0016] (2) In practical applications, shading is a common factor that causes 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 a shading loss index, and performs an evaluation. When the shading effect is abnormal, the optimization instructions of the microlens array can be corrected in a timely manner, thereby effectively reducing the efficiency loss of the photovoltaic system caused by shading. By combining real-time data on 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 benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 The figure is a schematic diagram of the steps of a photovoltaic system optimization design method for optimizing benefits according to the present invention. DETAILED DESCRIPTION

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0019] Example 1 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.

[0020] See also Figure 1 The present invention provides a photovoltaic system optimization design method for optimizing benefits, comprising the following steps: S1. Installing a microlens array on the surface of a photovoltaic panel to concentrate sunlight onto the photovoltaic panel, wherein the microlens array is specifically a first initial microlens array formed by arranging a plurality of microlenses. 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 spacing between lenses in each row and each column is equidistant, thereby obtaining a first array spacing d. S2. Based on the first initial microlens array and the first array spacing d, real-time data on illumination conditions is collected and relevant performance parameters are calculated to obtain the surface reflectivity R and the absorption capacity X of the photovoltaic panel. S3. Calculate the focusing efficiency based on the lighting condition data , and introduce a 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, and if the evaluation result is unqualified, generating a first optimization instruction and a second optimization instruction, and 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.

[0021] In this embodiment, traditional photovoltaic panels are unable to fully utilize sunlight under low-light conditions, such as in the morning and evening, resulting in low power generation efficiency. However, the present invention, by installing a microlens array on the surface of the photovoltaic panel, can focus sunlight and effectively improve the photovoltaic panel's light utilization rate, significantly enhancing the photovoltaic system's power generation capacity, especially during periods of low light intensity.

[0022] Existing microlens arrays typically use a fixed pitch design. While this can improve efficiency in some situations, it lacks the ability to dynamically adjust to real-time lighting conditions, temperature fluctuations, and other environmental factors. The innovative design of this invention combines real-time lighting data and temperature correction factors to dynamically adjust the layout and spacing of the microlens array, ensuring that the microlens array maintains optimal focusing performance in varying lighting environments, avoiding the energy loss that can occur in traditional designs.

[0023] This invention not only relies on the focusing effect of the microlens array but also incorporates the performance parameters of the photovoltaic panel itself, such as surface reflectivity and absorption capacity, which vary with lighting conditions. By calculating these performance parameters in real time and incorporating a convolutional neural network (CNN) prediction model, the invention can dynamically adjust the microlens array according to varying lighting conditions to maximize the photovoltaic system's photoelectric conversion efficiency.

[0024] In practical applications, shading is a common factor in 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 a shading loss index and performs an evaluation. When the shading effect is abnormal, the optimization instructions of the microlens array can be corrected in time, thereby effectively reducing the efficiency loss of the photovoltaic system caused by shading. By combining real-time data on lighting conditions, ambient temperature, microlens array spacing, and shading factors, the system can dynamically optimize the configuration in 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 high economic benefits in different seasons and time periods, thereby maximizing benefits.

[0025] Example 2 This embodiment is explained in Example 1. Specifically, S1 includes: S11, collecting size information of photovoltaic panels; The size information includes the width W and length L of the photovoltaic panel. The effective absorption area A of the photovoltaic panel is obtained by calculation, and the total number N of microlenses required to be covered is calculated using the following formula: Where A represents the effective absorption area of ​​the photovoltaic panel, Indicates the width of the photovoltaic panel, L indicates the length of the photovoltaic panel, Indicates the area utilization rate of the photovoltaic panel, 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 fill 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, set between 80-95%, determined experimentally based on the material properties of the photovoltaic panel and microlens.

[0026] By accurately obtaining the width W and length L of the photovoltaic panel and combining this dimensional information to calculate the effective absorption area A of the photovoltaic panel, we ensure that the design of the microlens array can maximize coverage of the effective area of ​​the photovoltaic panel, thereby optimizing light utilization. Based on the effective absorption area A of the photovoltaic panel, combined with the area utilization, lens unit area, and array arrangement density, 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 photovoltaic panel surface. 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 excessive beam size that does not cover the photovoltaic panel surface, further improving the photovoltaic panel's photoelectric conversion efficiency.

[0027] 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 settings: S121, arrangement of a first initial microlens array: Based on the total number N of microlenses to be covered calculated in S11, a plurality of microlenses are arranged in a first rectangular network array, the position of each microlens in the array being determined by spacing parameters on the x and y axes, and the spacing between lenses in each row and column being equidistant, to obtain a first array spacing d; Based on the previously calculated total number of microlenses, N, the microlenses are arranged in a first rectangular grid array. This arrangement ensures the proper placement of each microlens, avoids redundant or missing lenses, and ensures optimal optical efficiency for each microlens. With equal spacing between lenses in each row and column, the microlens array evenly covers the entire active area of ​​the photovoltaic panel, ensuring sufficient focused light in all areas of the panel, avoiding localized light shortages and improving the overall power generation efficiency of the photovoltaic system.

[0028] 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, determined experimentally based on the material properties of the photovoltaic panels and microlenses.

[0029] In this embodiment, based on the experimental results of the material properties of photovoltaic panels and microlenses, it can be ensured that the microlens array can always be in the optimal spacing range in actual applications. Too large a spacing may lead to uneven light coverage, while too small a spacing may cause excessive or overlapping light focus. Reasonable spacing setting avoids both of these 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.

[0030] Through accurate size acquisition, reasonable lens number calculation, optimized micro-lens array arrangement, and dynamically adjusted array spacing settings, not only is the efficiency of photovoltaic panels guaranteed to be improved during low-light periods, but the overall power generation efficiency of the photovoltaic system can also be maximized under different working conditions. Each of the above steps ensures the optimal performance of the photovoltaic system under different environments by comprehensively considering the arrangement of the micro-lens array, lens spacing, and experimental data on material properties, further optimizing the economic benefits and energy utilization efficiency of photovoltaic power generation. Example 3 This embodiment is explained in Example 1. Specifically, S2 includes: S21. Based on the first initial microlens array and the first array spacing d, light condition data closely related to the operating state of the photovoltaic panel is collected through a light sensor and an environmental monitoring device. The light condition data includes: Solar radiation intensity I: the ability of a unit area to receive solar radiation, in W / m 2 ; Solar 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; Light sensors and environmental monitoring equipment are used to collect real-time lighting condition data closely related to the operating status of photovoltaic panels, such as solar radiation intensity I, solar angle θ, and temperature T. This data can fully reflect the environmental changes in the photovoltaic panels, enabling the microlens array to be dynamically optimized and adjusted according to the lighting conditions.

[0031] S22. Based on the collected illumination data and the optical characteristics of the microlens array, calculate the surface reflectivity R of the photovoltaic panel using the following formula: Where R represents the surface reflectivity of the photovoltaic panel, Indicates the intensity of reflected light detected by the optical sensor. Indicates the intensity of collected solar radiation; the surface reflectivity R of a photovoltaic panel indicates the proportion of solar energy reflected by the photovoltaic panel, and is used to evaluate the performance of the anti-reflective coating and the surface condition of the photovoltaic panel; By calculating the surface reflectivity R, the light-collecting efficiency of the microlens array can be more precisely adjusted. 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.

[0032] 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: Where, The absorption capacity X represents the area utilization of the photovoltaic panel and is set between 90% and 95%. The absorption capacity X of the photovoltaic panel represents the actual efficiency of the photovoltaic panel in utilizing solar radiation energy, eliminating reflection losses. By calculating the absorption capacity X of the photovoltaic panel and combining it with the surface reflectivity R, the design of the microlens array can be dynamically optimized. For example, if the absorption capacity is low, the parameters of the microlens array can be adjusted to enhance the photovoltaic panel's solar radiation absorption capacity, thereby improving power generation efficiency.

[0033] In this embodiment, by collecting precise illumination data, calculating surface reflectivity and absorption capacity, and dynamically adjusting the optical characteristics of the microlens array, the photovoltaic panel's power generation efficiency can be effectively optimized while also adapting to varying environmental conditions. These optimization measures ensure that the photovoltaic system can consistently operate at optimal performance in a variety of lighting environments, thereby maximizing power generation efficiency and improving the system's economic benefits and energy efficiency.

[0034] Example 4 This embodiment is explained in Example 1. Specifically, S3 includes: 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: Where, 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; Concentrating efficiency This directly impacts the light-gathering effectiveness of the microlens array. By calculating the focusing efficiency, which is related to the sun's angle, array spacing, and optical material properties, we can more accurately evaluate and optimize the performance of the microlens array, ensuring that more solar energy is effectively concentrated onto the photovoltaic panel surface, thereby improving power generation efficiency.

[0035] S32, introduce temperature correction factor , the calculation formula is: Where T represents temperature, Indicates the reference temperature, set to 25℃, Temperature is a key factor affecting the efficiency of photovoltaic panels and microlenses. High temperatures typically lead to decreased photoelectric conversion efficiency. By introducing a temperature correction factor, we can accurately compensate for the impact of temperature changes on photovoltaic system performance, ensuring that system efficiency is appropriately adjusted under different temperature conditions.

[0036] S33. Use convolutional neural network to build the initial model of convolutional neural network, and train and test the initial model of convolutional neural network with illumination condition data, and use the trained initial model of convolutional neural network as the microlens efficiency prediction model. At the same time, use the intermediate layer output of the microlens efficiency prediction model as the feature vector to identify feature information, and train and test the microlens efficiency prediction model through the obtained feature information, combining the absorption capacity X and concentration efficiency of photovoltaic panels. , 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 : ; Where X represents the absorption capacity of the photovoltaic panel, represents the light-gathering efficiency, Represents the temperature correction factor.

[0037] 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 status of the microlens array in time, thereby maximizing the ,photoelectric conversion efficiency of the photovoltaic system.

[0038] In this embodiment, by calculating the concentration efficiency, introducing a temperature correction factor, and predicting the microlens efficiency using a convolutional neural network (CNN), this not only optimizes the design of the microlens array but also enhances the photovoltaic system's adaptability to environmental changes. Specifically, by accurately predicting the microlens efficiency and adjusting the photovoltaic system's operating state in real time, the overall system's operating efficiency can be improved, mitigating performance degradation caused by temperature fluctuations and changing lighting conditions. Ultimately, this optimized design can significantly enhance the performance, stability, and economic benefits of the photovoltaic power generation system, ensuring its efficient operation in a variety of external environments.

[0039] Example 5 This embodiment is explained in Example 1. Specifically, the microlens light characteristic index in S31 is obtained by the following steps: S311. Collect 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 passing through the microlens, including the refractive index of the following different materials: Quartz glass: ; Polymethyl methacrylate PMMA: ; Polycarbonate PC: The refractive index of a microlens is a key factor in determining the angle of light deflection and focusing ability when passing through the lens. Different materials (such as quartz glass, PMMA, and PC) have different refractive indices. By collecting this data, the light focusing performance of the microlens can be optimized based on the material's characteristics.

[0040] S312. Collect 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 curvature radius 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 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 wider but the light gathering effect is weaker. Focal length is the core parameter that affects light focusing: shorter focal lengths result in stronger focusing but a narrower coverage. Longer focal lengths result in weaker focusing but a wider coverage. By collecting focal length data in real time, the focusing effect of the microlens can be precisely controlled to ensure optimal utilization of light energy.

[0041] 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 microlens surface is a spherical lens with uniform light focusing performance. When j≠1, it indicates that the microlens surface is an aspherical lens, which can reduce spherical aberration. The surface shape factor, j, is a key factor influencing the light focusing performance of a microlens. Spherical lenses offer uniform focusing, while aspherical lenses effectively reduce spherical aberration and provide more precise beam focusing. This data helps design more efficient microlens arrays.

[0042] S314. Use 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 calculate the light beam diffusion angle of the microlens using the following formula: : Where, 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; By collecting light intensity data from the microlens, we can calculate the horizontal and vertical angular range of the light beam, thereby determining the beam spread angle. A smaller spread angle indicates a more focused effect, while a larger spread angle indicates a wider beam, covering a wider area. This data helps adjust the microlens design to match the beam spread range required by the photovoltaic panel.

[0043] S315, combining the refractive index of the microlens obtained from S311-S314 , microlens focal length f, microlens surface shape coefficient j and microlens beam spread angle , after dimensionless processing, the microlens light characteristic index n is calculated using the following formula: Where, 、 、 and Represent the refractive index of the microlens , microlens focal length f, microlens surface shape coefficient j and microlens beam spread angle The weight of , , ,and .

[0044] In this embodiment, through the above steps, the light characteristic index n of the microlenses can provide a precise basis for optimizing the light energy concentration effect of the photovoltaic system. Collecting and processing data on multiple factors, such as material refractive index, focal length, surface shape, and beam spread angle, facilitates the design of efficient microlens arrays. Ultimately, based on these optimized designs, the photovoltaic system can achieve higher energy conversion efficiency under various lighting conditions, improving system stability and long-term operational benefits.

[0045] Example 6 This embodiment is explained in Example 4. Specifically, S3 also includes: S34, preset the target lighting efficiency threshold G, and convert the real-time lighting efficiency index Compare with the target lighting efficiency threshold G to obtain a first evaluation result, including: If the real-time lighting efficiency index < target illumination efficiency threshold G, it indicates that the current light is insufficient, resulting in unqualified illumination efficiency. 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%, indicating 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 If the target illumination efficiency threshold G*150% is exceeded, it indicates that the illumination efficiency is unqualified due to excessive illumination, and there is a risk of hotspot effect in the photovoltaic panel. The second optimization instruction is generated, including: maintaining the current first rectangular network array, adjusting the first array spacing d to expand by 5%-15% to form a third array spacing d3, and further adjusting the microlens focal length f.

[0046] Adjustment of the microlens array can be achieved through the microlens mounting rails. 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 rails. By reducing or increasing the array spacing d, the beam coverage of the microlens can be effectively adjusted, optimizing the focus and distribution of light to 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 high-precision adjustment capabilities and 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 optimal receiving position of the photovoltaic panel.

[0047] In this embodiment, when the real-time illumination efficiency is insufficient, the system generates an optimization instruction to switch from the current first rectangular grid array to a second honeycomb array, reduce the array spacing (by 5%-15%), and adjust the microlens focal length. This strategy helps enhance light focusing, thereby improving illumination efficiency and compensating for insufficient illumination. By changing the array arrangement and spacing, light focusing and absorption can be effectively enhanced, improving the photovoltaic panel's photoelectric conversion efficiency. If the current illumination efficiency index is acceptable, maintaining the existing configuration avoids over-adjustment, conserves resources, and reduces unnecessary system adjustments, thereby reducing energy consumption and maintenance costs. When illumination is excessively strong, there is a risk of hotspotting in the photovoltaic panel, resulting in reduced photoelectric conversion efficiency. In this case, the system generates a second optimization instruction to increase the array spacing (by 5%-15%) and adjust the microlens focal length to reduce the excessive illumination, thereby avoiding hotspotting and ensuring stable operation of the photovoltaic system. Increasing the array spacing effectively mitigates the impact of excessive illumination and reduces hotspotting caused by over-focused light, thereby improving the safety and stability of the photovoltaic system.

[0048] Example 7 This embodiment is explained in Example 6. Specifically, the method for further adjusting the focal length f of the microlens is as follows: 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: Where, represents the area blocked by the photovoltaic panels, Indicates the time of occlusion, represents the angle of occlusion, cos represents the cosine function, and 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%.

[0049] In this embodiment, the method dynamically adjusts the focal length of the microlenses based on real-time light intensity and solar angle, thereby maintaining the optimal operating state of the photovoltaic panel under different lighting conditions and improving photoelectric conversion efficiency. By adjusting the focal length, the focusing effect of light is optimized, allowing the photovoltaic panel to maximize solar energy absorption under different lighting conditions, thereby improving overall lighting efficiency.

[0050] Example 8 This embodiment is explained in Example 1. Specifically, S4 includes: S41. Collect the area blocked by photovoltaic panels , occlusion time and the angle of occlusion After dimensionless processing, the occlusion loss index is calculated using the following formula: : Where, represents the total area of ​​photovoltaic panels, Indicates the total daily occlusion duration; S42, preset occlusion loss threshold, if the occlusion loss index > the shading loss threshold, indicating that the shading effect is abnormal, the lighting efficiency of the photovoltaic panel is greatly reduced, and a correction instruction is generated; If the occlusion loss index If the value is less than or equal to 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.

[0051] The correction instructions include: The first optimization instruction was modified from reducing the spacing by 5%-15% to a 16%-30% reduction, generating and executing the fourth array spacing d4. This larger reduction in the array spacing (16%-30%) effectively reduces shading and ensures that the photovoltaic panels receive more sunlight. This helps restore the photovoltaic panel's lighting efficiency, especially after prolonged periods of shading.

[0052] 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, which leads to 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 compensate for the occlusion loss caused by the occlusion anomaly.

[0053] In this embodiment, the shading loss index The larger the value is, the more serious the shading effect on the photovoltaic panel is, and the greater the loss of lighting efficiency may be. If the calculated shading loss index is greater than the shading loss threshold If the distance is too large, it is considered that the photovoltaic panel's lighting efficiency has been greatly reduced, and the photovoltaic panel is abnormally affected by the shading, and adjustment is required. When adjusting the spacing, the spacing will be increased and the range will be reduced to ensure that the photovoltaic panel's illumination area is not excessively blocked.

[0054] 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 set of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0055] The above formulas are obtained by collecting a large amount of data and performing software simulation, and a formula close to the actual value is selected. The coefficients in the formula are set by those skilled in the art according to actual conditions. The above is only a preferred specific implementation method of the present invention, but the protection scope of the present invention is not limited to this. Any technician familiar with this technical field, within the technical scope disclosed by the present invention, can make equivalent replacements or changes based on the technical solution and inventive concept of the present invention, which should be covered by 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: : 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; 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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