Photovoltaic power interval prediction method

Abstract

The application discloses a photovoltaic power interval prediction method, and belongs to the technical field of photovoltaic power prediction.The method comprises the following steps: collecting photovoltaic power, meteorological data, photovoltaic panel surrounding obstacle data, photovoltaic panel layout data and solar position data; constructing a solar position change model, analyzing the light intensity difference of photovoltaic panels at different positions, establishing a solar position-light intensity-power relationship model, and obtaining different power caused by light differences; calculating the blocking area based on the obstacle information and the solar position change model, and correlating the area change rule with the power for analysis to reflect the blocking influence, while setting a recalculation critical value.The application solves the problems of shadow calculation deviation caused by seasonal changes of the solar position and mis-calculation of overlapping blocks, balances the calculation accuracy and efficiency through dynamic tracking and critical value mechanism, realizes accurate prediction of photovoltaic power intervals, and is suitable for power prediction scenes in complex lighting environments.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic power generation prediction technology, and more specifically, to a method for predicting photovoltaic power generation range. Background Technology

[0002] In photovoltaic (PV) power generation systems, accurate power generation prediction is crucial for grid dispatch, energy management, and system optimization. However, the sun's position, including its elevation and azimuth angles, changes slowly with the seasons. This leads to dynamic changes in the shadows cast by obstacles and PV panels. Traditional prediction methods, which assume a static sun position, struggle to capture the differences in shadows at the same time across different seasons. This results in significant shadow range errors due to the cumulative changes in the sun's position over long periods, leading to large discrepancies between predicted and actual power generation. Furthermore, shading between PV panels and external obstacles may overlap. Traditional methods often simply superimpose the areas of these two types of shading without considering the problem of double-counting overlapping areas, easily resulting in inflated shading area calculations and further reducing prediction accuracy. Therefore, there is an urgent need for a PV power generation range prediction method that can dynamically track changes in the sun's position, accurately handle shading overlaps, and balance computational efficiency and accuracy.

[0003] No effective solutions have yet been proposed to address the problems in the relevant technologies. Summary of the Invention

[0004] To address the problems in related technologies, this invention proposes a method for predicting photovoltaic power generation ranges, thereby overcoming the problems of calculation deviations in shading and shadows caused by seasonal changes in the sun's position, and the problem of repeated calculations of shading area in existing related technologies.

[0005] Therefore, the specific technical solution adopted by the present invention is as follows:

[0006] A method for predicting photovoltaic power generation range, the method comprising the following steps:

[0007] S1. Collect data on photovoltaic power generation, weather, obstacles around photovoltaic panels, photovoltaic panel layout, and solar position;

[0008] S2. Construct a model of solar position variation, analyze the difference in solar irradiance at different positions, establish a model of the relationship between solar position, irradiance, and power generation, and derive the different power generation caused by the difference in irradiance.

[0009] S3. The shading area is calculated based on obstacle information and the solar position change model, and the area change pattern is correlated with the power generation to reflect the shading effect. At the same time, a recalculation threshold is set.

[0010] S4. Combine the photovoltaic panel installation layout with the changes in the sun's position to analyze the shading situation between photovoltaic panels. When the changes in the sun's position reach a critical value, recalculate the shading area.

[0011] S5. Taking into account obstacles and shading between photovoltaic panels, calculate the total shading area of ​​photovoltaic panels at different times and determine the power generation variation law under comprehensive shading.

[0012] S6. Based on the data of comprehensive light intensity and shading area, calculate and output the predicted range of photovoltaic power generation.

[0013] In a preferred embodiment, the collection of photovoltaic power generation, meteorological, obstacles around the photovoltaic panel, photovoltaic panel layout, and solar position data includes the following steps:

[0014] S11. Collect photovoltaic power generation data and meteorological data; collect information on the location, height, and shape of photovoltaic panels and surrounding obstacles; and collect inherent parameters and operating parameters of photovoltaic modules, as well as installation layout information of photovoltaic panels, including the number of photovoltaic panels, arrangement, spacing, orientation, and tilt angle.

[0015] S13. Collect information on the sun's altitude and azimuth at different times.

[0016] In a preferred embodiment, the steps of constructing a solar position variation model, analyzing the differences in solar panel illumination intensity at different locations, establishing a solar position-illuminance-power generation relationship model, and determining the different power generation caused by illumination differences include the following steps:

[0017] S21. Use Julian days to accurately represent time; calculate the solar declination angle and hour angle based on astronomical formulas, and then combine them with the local latitude to establish a model of how the solar altitude angle and azimuth angle change with time using trigonometric functions;

[0018] S22. Calculate the atmospheric mass based on the solar altitude angle, combine it with the local atmospheric composition, and use empirical formulas to analyze the influence of atmospheric scattering and absorption on sunlight to obtain the direct and scattered light intensity reaching the ground. Then, combine the orientation and tilt angle of the photovoltaic panel to calculate its surface light intensity.

[0019] S23. Calculate the temperature coefficient correction efficiency of the photovoltaic panel using the inherent parameters and operating parameters of the photovoltaic module. Combine the photoelectric conversion characteristics of the photovoltaic panel with the temperature coefficient correction efficiency, and establish a solar position-light intensity-power generation relationship model based on the light intensity, correction efficiency and photovoltaic panel area.

[0020] Temperature coefficient correction efficiency refers to the effective efficiency value after dynamically adjusting the efficiency after taking into account the impact of changes in the operating temperature of photovoltaic cells on photoelectric conversion efficiency; and its core function is to quantify the power loss caused by temperature rise, ensuring that the power generation model is closer to the actual physical characteristics.

[0021] As a preferred embodiment, the step of calculating the shading area based on obstacle information and a solar position change model, and correlating the area change pattern with power generation to reflect the shading impact, while setting a recalculation threshold, includes the following steps:

[0022] S31. Combine the solar position change model with the position information of obstacles and photovoltaic panels to establish a geometric relationship model between them in three-dimensional space. When establishing the relationship model, the obstacles and photovoltaic panels are abstracted into simple geometric figures, and their relative positions and shapes in space are determined using a coordinate system.

[0023] S32. Based on the principle of geometric optics, under a given sun position, the shadow area formed by the obstacle on the photovoltaic panel is calculated by ray tracing. Parallel rays are emitted from the sun position, and it is determined whether the rays are blocked by the obstacle. If they are blocked, the boundary of the shadow formed on the photovoltaic panel is determined. Then, the area of ​​the shadow area is calculated using computer graphics algorithms, and the shading area data at different time points are calculated at the same time.

[0024] S33. Analyze the calculated shading area data at different time points, plot the curve of shading area changing with time, use trend analysis to find the changing pattern, and analyze the main factors affecting the change of shading area.

[0025] S34. Match the photovoltaic power generation data with the corresponding shading area data, analyze the relationship between shading area and power generation through regression analysis algorithm, and establish a relationship model between shading area and power generation to describe the degree of influence of obstacle shading area on power generation.

[0026] S35. Based on the degree of influence of the obstruction area on the power generation, determine the amount of change in obstruction area corresponding to the significant change in the power generation of the photovoltaic panel, and use the changes in solar altitude angle and azimuth angle at this time as the recalculation threshold. When the cumulative change reaches the set threshold, the operation of recalculating the obstruction area of ​​the photovoltaic panel is triggered.

[0027] In a preferred embodiment, determining the change in shading area corresponding to a significant change in photovoltaic panel power generation based on the degree of influence of the obstacle shading area on power generation, and using the changes in solar altitude angle and azimuth angle at this time as recalculation thresholds, includes the following steps:

[0028] S351. The relationship model between the shaded area and power generation is as follows:

[0029] Where P is the power generation capacity, S is the shading area, h is the solar altitude angle, and A is the azimuth angle.

[0030] The intercept is... For regression coefficients, It is the error term, and All of these results were obtained by fitting and calculating actual data.

[0031] S352. For different changes in shading area ΔS, calculate the change in power generation ΔP based on the established correlation model, and calculate the rate of change in power generation r. The specific formula is as follows:

[0032] ;in This represents the initial power generation capacity.

[0033] Based on the actual operation of the photovoltaic panels and economic analysis, the threshold r1 for significant changes in power generation is determined. Different time points are analyzed in chronological order, and the change in shading area ΔS and the corresponding rate of change in power generation r at each time point are calculated. The time points when the rate of change in power generation r exceeds the threshold r1 are identified, and the change in shading area ΔS corresponding to these time points is recorded.

[0034] S353. For each selected point of significant change, record the corresponding change in solar altitude angle and azimuth angle. Calculate the average value to determine the change in solar altitude angle Δh and azimuth angle ΔA corresponding to the significant change in photovoltaic power generation. Use these two changes as the recalculation threshold.

[0035] In a preferred embodiment, the step of analyzing the shading situation between photovoltaic panels by combining the photovoltaic panel installation layout with changes in the sun's position, and recalculating the shading area when the change in the sun's position reaches a critical value, includes the following steps:

[0036] S41. Recalculate the solar altitude and azimuth using astronomical algorithms; based on the established geometric relationship model, further refine the position and shape information of each photovoltaic panel, and clarify the impact of the orientation and tilt angle of the photovoltaic panel on light reception.

[0037] S43. Emit parallel rays from the current sun position, and determine whether the intersection of the rays with the plane of other photovoltaic panels is within the boundary of the photovoltaic panel by calculating the intersection point of the rays. If the intersection point is within the boundary, it is considered that there is shading.

[0038] S44. When there is shading between photovoltaic panels, the area of ​​the shaded part is calculated using computer graphics algorithms;

[0039] S45. Match the calculated shading area data between photovoltaic panels with the corresponding photovoltaic power generation data. Use regression analysis algorithm to analyze the relationship between shading area and power generation, and establish a relationship model between shading area and power generation to describe the degree of influence of photovoltaic panel shading on power generation. When the cumulative change of solar altitude angle and azimuth angle reaches the preset recalculation threshold, trigger the operation of recalculating the shading area of ​​photovoltaic panel body.

[0040] As a preferred embodiment, the process of comprehensively considering obstacles and shading between photovoltaic panels, calculating the total shading area of ​​photovoltaic panels at different times, and determining the power generation variation pattern under comprehensive shading includes the following steps:

[0041] S51. Integrate the shading area data of the obstacle and the shading area data of the photovoltaic panel itself. Use spatial geometry algorithm to determine whether there is an overlap between the shading area data of the obstacle and the shading area data of the photovoltaic panel itself. If there is an overlap, extract the boundary of the overlap area and calculate the overlap area.

[0042] If you need to obtain the total shading area data of the photovoltaic panels at different times, you can directly calculate the total shading area using the method described above.

[0043] S52. When there is an overlapping area, add the shading area data of the obstacle and the shading area data of the photovoltaic panel itself, and subtract the overlapping area to obtain the total shading area; when there is no overlapping area, directly add the shading area data of the obstacle and the shading area data of the photovoltaic panel itself to obtain the total shading area.

[0044] S53. Match the photovoltaic power generation data with the corresponding total shading area data to ensure that each total shading area data has a corresponding power generation data. Then, use the regression analysis algorithm again to analyze the relationship between the total shading area and the power generation, and establish a relationship model between the total shading power generation and the power generation.

[0045] In a preferred embodiment, the process of integrating the shading area data of the obstacle and the shading area data of the photovoltaic panel, and using a spatial geometric algorithm to determine whether there is an overlap between the shading area data of the obstacle and the shading area data of the photovoltaic panel, and extracting the boundary of the overlapping area and calculating the overlapping area when there is an overlap, includes the following steps:

[0046] S521. Abstract the shading areas of the obstacle and the photovoltaic panel into polygons on a two-dimensional plane, and label them as obstacle shading polygon P1 and photovoltaic panel shading polygon P2 respectively, and obtain the coordinates of their respective vertices.

[0047] S522. Calculate the normal vectors of each side of the two polygons as the separating axes. Then project the two polygons onto each separating axis to obtain the projection interval. When the projection intervals on all separating axes overlap, it is determined that the two polygons intersect and there is an overlapping part.

[0048] S523. When two polygons overlap, use the intersection operation in Boolean operations to compare and combine the edges of the two polygons, remove the edges that are not in the overlapping area, extract the boundary of the overlapping area, and calculate its area using the shoelace formula. The specific formula is as follows:

[0049] ;in, The calculated overlapping area is n, where n is the number of vertices. Let x and y be the x and y coordinates of the i-th vertex of the polygon, respectively. The polygons are respectively The x and y coordinates of each vertex, Let x and y be the x and y coordinates of the last vertex of the polygon, respectively. These are the x and y coordinates of the first vertex of the polygon, respectively.

[0050] As a preferred embodiment, the calculation and output of the predicted photovoltaic power generation range based on the comprehensive data of light intensity and shading area includes the following steps:

[0051] S61. Comprehensively acquire real-time information on the sun's position, meteorological data, and the area of ​​obstruction;

[0052] S62. Using the solar position-light intensity-power generation relationship model, input the light intensity, the temperature coefficient correction efficiency of the photovoltaic panel, and the area of ​​the photovoltaic panel to obtain multiple data values ​​of the power generation predicted based on the light intensity under the unshaded condition within the prediction time. Take the maximum and minimum values ​​as the photovoltaic power generation range. When there is no shading, directly output the power generation range.

[0053] S63. When there is shading, the influence value is obtained directly by using the relationship model between the total shading power generation and the predicted power generation range is corrected by using the influence value, and the corrected power generation range is output.

[0054] The beneficial effects of this invention are as follows:

[0055] 1. Because the position of the sun changes slowly with the seasons, and the shadows cast by obstacles and photovoltaic panels vary in different seasons, the cumulative changes in the sun's position can significantly increase the error in the shadow range when making long-term power generation predictions. This results in a large deviation between the predicted and actual power generation values, affecting the accuracy of the prediction. Therefore, this invention effectively solves the problem of power generation calculation deviation caused by the slow seasonal changes in the sun's altitude and azimuth angles by dynamically tracking the correlation mechanism between changes in the sun's position and the effects of shading, making the subsequent predicted power generation range more accurate.

[0056] 2. By setting a recalculation threshold, this invention can ensure a dynamic balance between calculation accuracy and cost control. By analyzing the impact of changes in the shading area on power generation and combining the cumulative changes in solar altitude and azimuth angles, it accurately determines the value that triggers the recalculation of the shading area. This not only optimizes calculation efficiency and avoids the waste of resources from indiscriminate continuous calculation, but also avoids the distortion of shading caused by slow changes in the sun's position. At the same time, it can also improve the model's adaptability to seasonal shading changes, ensuring that the prediction process remains efficient and accurate under different lighting conditions.

[0057] 3. This invention avoids the problem of inflated shading area caused by repeated calculations by analyzing the possible overlap between shading by the photovoltaic panel itself and shading by external obstacles. In the specific analysis, the algorithm integrates and analyzes the two types of shading areas, accurately removes the overlapping parts, further improves the accuracy of power generation prediction, and ensures the reliability of prediction results in complex shading scenarios. Attached Figure Description

[0058] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0059] Figure 1 This is a flowchart of a photovoltaic power generation range prediction method according to an embodiment of the present invention;

[0060] Figure 2 This is a critical value judgment diagram of a photovoltaic power generation range prediction method according to an embodiment of the present invention. Detailed Implementation

[0061] To further illustrate the various embodiments, the present invention provides accompanying drawings, which are part of the disclosure of the present invention. These drawings are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these drawings, those skilled in the art should be able to understand other possible implementation methods and the advantages of the present invention. The components in the drawings are not drawn to scale, and similar component symbols are generally used to represent similar components.

[0062] According to an embodiment of the present invention, a method for predicting photovoltaic power generation range is provided.

[0063] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments, such as... Figures 1-2 As shown, a photovoltaic power generation range prediction method according to an embodiment of the present invention includes the following steps:

[0064] S1. Collect data on photovoltaic power generation, weather, obstacles around photovoltaic panels, photovoltaic panel layout, and solar position;

[0065] Further, collecting data on photovoltaic power generation, weather, obstacles around the photovoltaic panels, photovoltaic panel layout, and solar position includes the following steps:

[0066] S11. Collect photovoltaic power generation data and meteorological data; collect information on the location, height, and shape of photovoltaic panels and surrounding obstacles; and collect inherent parameters and operating parameters of photovoltaic modules, as well as installation layout information of photovoltaic panels, including the number of photovoltaic panels, arrangement, spacing, orientation, and tilt angle.

[0067] S13. Collect information on the sun's altitude and azimuth at different times.

[0068] S2. Construct a model of solar position variation, analyze the difference in solar irradiance at different positions, establish a model of the relationship between solar position, irradiance, and power generation, and derive the different power generation caused by the difference in irradiance.

[0069] Furthermore, a solar position variation model is constructed to analyze the differences in solar irradiance at different locations, and a solar position-irradiance-power generation relationship model is established. The results show that the different power generation caused by irradiance differences include the following steps:

[0070] S21. Use Julian days to accurately represent time; calculate the solar declination angle and hour angle based on astronomical formulas, and then combine them with the local latitude to establish a model of how the solar altitude angle and azimuth angle change with time using trigonometric functions;

[0071] S22. Calculate the atmospheric mass based on the solar altitude angle, combine it with the local atmospheric composition, and use empirical formulas to analyze the influence of atmospheric scattering and absorption on sunlight to obtain the direct and scattered light intensity reaching the ground. Then, combine the orientation and tilt angle of the photovoltaic panel to calculate its surface light intensity.

[0072] S23. Calculate the temperature coefficient correction efficiency of the photovoltaic panel using the inherent parameters and operating parameters of the photovoltaic module. Combine the photoelectric conversion characteristics of the photovoltaic panel with the temperature coefficient correction efficiency, and establish a solar position-light intensity-power generation relationship model based on the light intensity, correction efficiency and photovoltaic panel area.

[0073] It should be noted that after the model is established, it is necessary to collect actual solar position, light intensity and power generation data to compare the model predictions with the actual values, analyze the errors and optimize the model parameters in order to accurately reflect the impact of light differences caused by changes in solar position on power generation.

[0074] S3. Calculate the shading area based on obstacle information and solar position change model, and analyze the correlation between the area change law and power generation to reflect the shading effect, while setting the recalculation critical value.

[0075] Furthermore, based on obstacle information and a solar position change model, the shading area is calculated, and the area change pattern is correlated with power generation to reflect the impact of shading. Simultaneously, a recalculation threshold is set, including the following steps:

[0076] S31. Combine the solar position change model with the position information of obstacles and photovoltaic panels to establish a geometric relationship model between them in three-dimensional space. When establishing the relationship model, the obstacles and photovoltaic panels are abstracted into simple geometric figures, and their relative positions and shapes in space are determined using a coordinate system.

[0077] S32. Based on the principle of geometric optics, under a given sun position, the shadow area formed by the obstacle on the photovoltaic panel is calculated by ray tracing. Parallel rays are emitted from the sun position, and it is determined whether the rays are blocked by the obstacle. If they are blocked, the boundary of the shadow formed on the photovoltaic panel is determined. Then, the area of ​​the shadow area is calculated using computer graphics algorithms, and the shading area data at different time points are calculated at the same time.

[0078] S33. Analyze the calculated shading area data at different time points, plot the curve of shading area changing with time, use trend analysis to find the changing pattern, and analyze the main factors affecting the change of shading area.

[0079] S34. Match the photovoltaic power generation data with the corresponding shading area data, analyze the relationship between shading area and power generation through regression analysis algorithm, and establish a relationship model between shading area and power generation to describe the degree of influence of obstacle shading area on power generation.

[0080] S35. Based on the degree of influence of the obstruction area on the power generation, determine the amount of obstruction area change corresponding to the significant change in the power generation of the photovoltaic panel, and use the changes in the solar altitude angle and azimuth angle at this time as the recalculation threshold. When the cumulative change reaches the set threshold, the operation of recalculating the obstruction area on the photovoltaic panel is triggered.

[0081] It should be noted that the recalculation threshold refers to the cumulative change in solar altitude angle or azimuth angle set during the calculation of the area of ​​photovoltaic panel shading by obstacles based on the principle of geometric optics. Although the impact of these changes on the shading area is small in the short term, they can lead to significant changes in the shadow shape and coverage in the long term.

[0082] When the change in the sun's position reaches this critical value, the shading of the photovoltaic panels by obstacles will change significantly, which may affect the power generation efficiency of the photovoltaic panels. Therefore, when the cumulative change in the sun's position reaches this critical value, the shading area of ​​the photovoltaic panels by obstacles will be recalculated to ensure the accuracy of the shading area data.

[0083] Furthermore, based on the degree of impact of the obstruction area on power generation, the change in obstruction area corresponding to a significant change in photovoltaic panel power generation is determined, and the changes in solar altitude angle and azimuth angle at this time are used as the recalculation critical values, including the following steps:

[0084] S351. The relationship model between the shaded area and power generation is as follows:

[0085] Where P is the power generation capacity, S is the shading area, h is the solar altitude angle, and A is the azimuth angle.

[0086] The intercept is... For regression coefficients, It is the error term, and All of these results were obtained by fitting and calculating actual data.

[0087] S352. For different changes in shading area ΔS, calculate the change in power generation ΔP based on the established correlation model, and calculate the rate of change in power generation r. The specific formula is as follows:

[0088] ;in This represents the initial power generation capacity.

[0089] Based on the actual operation of the photovoltaic panels and economic analysis, the threshold r1 for significant changes in power generation is determined. Different time points are analyzed in chronological order, and the change in shading area ΔS and the corresponding rate of change in power generation r at each time point are calculated. The time points when the rate of change in power generation r exceeds the threshold r1 are identified, and the change in shading area ΔS corresponding to these time points is recorded.

[0090] S353. For each obvious change point selected, record the corresponding change in solar altitude angle and azimuth angle. Calculate the average value to determine the change in solar altitude angle Δh and azimuth angle ΔA corresponding to the obvious change in photovoltaic power generation. Use these two changes as the recalculation threshold.

[0091] S4. Combine the photovoltaic panel installation layout with the changes in the sun's position to analyze the shading situation between photovoltaic panels. When the changes in the sun's position reach a critical value, recalculate the shading area.

[0092] Furthermore, considering the photovoltaic panel installation layout and changes in the sun's position, the shading situation between photovoltaic panels is analyzed. When the change in the sun's position reaches a critical value, the shading area is recalculated, including the following steps:

[0093] S41. Recalculate the solar altitude and azimuth using astronomical algorithms; based on the established geometric relationship model, further refine the position and shape information of each photovoltaic panel, and clarify the impact of the orientation and tilt angle of the photovoltaic panel on light reception.

[0094] S43. Emit parallel rays from the current sun position, and determine whether the intersection of the rays with the plane of other photovoltaic panels is within the boundary of the photovoltaic panel by calculating the intersection point of the rays. If the intersection point is within the boundary, it is considered that there is shading.

[0095] S44. When there is shading between photovoltaic panels, the area of ​​the shaded part is calculated using computer graphics algorithms;

[0096] S45. Match the calculated shading area data between photovoltaic panels with the corresponding photovoltaic power generation data, analyze the relationship between shading area and power generation through regression analysis algorithm, and establish a relationship model between shading area and power generation to describe the degree of influence of photovoltaic panel shading on power generation. When the cumulative change of solar altitude angle and azimuth angle reaches the preset recalculation threshold, trigger the operation of recalculating the shading area of ​​photovoltaic panel body.

[0097] S5. Taking into account obstacles and shading between photovoltaic panels, calculate the total shading area of ​​photovoltaic panels at different times and determine the power generation variation law under comprehensive shading.

[0098] Furthermore, taking into account obstacles and shading between photovoltaic panels, the total shading area of ​​the photovoltaic panels at different times is calculated, and the variation law of power generation under comprehensive shading is determined by the following steps:

[0099] S51. Integrate the shading area data of the obstacle and the shading area data of the photovoltaic panel itself. Use spatial geometry algorithm to determine whether there is an overlap between the shading area data of the obstacle and the shading area data of the photovoltaic panel itself. If there is an overlap, extract the boundary of the overlap area and calculate the overlap area.

[0100] S52. When there is an overlapping area, add the shading area data of the obstacle and the shading area data of the photovoltaic panel itself, and subtract the overlapping area to obtain the total shading area; when there is no overlapping area, directly add the shading area data of the obstacle and the shading area data of the photovoltaic panel itself to obtain the total shading area.

[0101] Furthermore, the shading area data of obstacles and the shading area data of the photovoltaic panel itself are integrated. Using spatial geometric algorithms, it is determined whether there is any overlap between the shading area data of obstacles and the shading area data of the photovoltaic panel itself. If there is an overlap, the boundary of the overlap area is extracted, and the overlap area is calculated. The calculation includes the following steps:

[0102] S521. Abstract the shading areas of the obstacle and the photovoltaic panel into polygons on a two-dimensional plane, and label them as obstacle shading polygon P1 and photovoltaic panel shading polygon P2 respectively, and obtain the coordinates of their respective vertices.

[0103] S522. Calculate the normal vectors of each side of the two polygons as the separating axes. Then project the two polygons onto each separating axis to obtain the projection interval. When the projection intervals on all separating axes overlap, it is determined that the two polygons intersect and there is an overlapping part.

[0104] S523. When two polygons overlap, use the intersection operation in Boolean operations to compare and combine the edges of the two polygons, remove the edges that are not in the overlapping area, extract the boundary of the overlapping area, and calculate its area using the shoelace formula. The specific formula is as follows:

[0105] ;in, The calculated overlapping area is n, where n is the number of vertices. Let x and y be the x and y coordinates of the i-th vertex of the polygon, respectively. The polygons are respectively The x and y coordinates of each vertex, Let x and y be the x and y coordinates of the last vertex of the polygon, respectively. These are the x and y coordinates of the first vertex of the polygon, respectively.

[0106] S53. Match the photovoltaic power generation data with the corresponding total shading area data to ensure that each total shading area data has a corresponding power generation data. Then, use regression analysis algorithm again to analyze the relationship between total shading area and power generation, and establish a relationship model between total shading power generation.

[0107] S6. Based on the data of comprehensive light intensity and shading area, calculate and output the predicted range of photovoltaic power generation.

[0108] Furthermore, by combining data such as irradiance and shading area, the predicted photovoltaic power generation range is calculated and output through the following steps:

[0109] S61. Comprehensively acquire real-time information on the sun's position, meteorological data, and the area of ​​obstruction;

[0110] S62. Using the solar position-light intensity-power generation relationship model, input the light intensity, the temperature coefficient correction efficiency of the photovoltaic panel, and the area of ​​the photovoltaic panel to obtain multiple data values ​​of the power generation predicted based on the light intensity under the unshaded condition within the prediction time. Take the maximum and minimum values ​​as the photovoltaic power generation range. When there is no shading, directly output the power generation range.

[0111] S63. When there is shading, the influence value is obtained directly by using the relationship model between the total shading power generation and the predicted power generation range is corrected by using the influence value, and the corrected power generation range is output.

[0112] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A photovoltaic power generation power interval prediction method characterized by, The method comprises the following steps: S1, collecting photovoltaic power generation power, meteorological data, photovoltaic panel surrounding obstacles, photovoltaic panel layout and solar position data; S2, constructing a solar position change model, analyzing the light intensity difference of photovoltaic panels under different positions, establishing a solar position-illumination intensity-power generation power relationship model, and obtaining different power generation powers caused by light differences; S3, calculating the shielding area based on the obstacle information and the solar position change model, and correlating the area change law with the power generation power to reflect the shielding influence, and setting a recalculation critical value; S31, combining the solar position change model with the position information of the obstacles and the photovoltaic panels to establish a geometric relationship model between them in a three-dimensional space, and abstracting the obstacles and the photovoltaic panels as simple geometric figures to determine their relative positions and shapes in the space by using a coordinate system; S32, according to the geometric optics principle, calculating the shadow area formed by the obstacles on the photovoltaic panels at a given solar position by using the light ray tracing method, determining whether the parallel light rays from the solar position are blocked by the obstacles, and if the light rays are blocked, determining the shadow boundary formed on the photovoltaic panels, and then calculating the area of the shadow area by using a computer graphics algorithm, and calculating the shielding area data at different time points; S33, analyzing the shielding area data at different time points obtained by calculation, drawing a curve of the shielding area changing with time, and finding out the change law by using the trend analysis method, and analyzing the main factors affecting the change of the shielding area; S34, matching the photovoltaic power generation power data with the corresponding shielding area data, analyzing the relationship between the shielding area and the power generation power by using a regression analysis algorithm, and establishing a relationship model between the shielding area and the power generation power to describe the influence degree of the shielding area of the obstacles on the power generation power; S35, determining the shielding area change amount corresponding to the obvious change of the photovoltaic panel power generation power according to the influence degree of the shielding area of the obstacles on the power generation power, and taking the solar altitude angle and azimuth angle change amount at this time as the recalculation critical value, and triggering the operation of recalculating the shielding area of the obstacles to the photovoltaic panels when the cumulative change amount reaches the set critical value; S4, analyzing the shielding condition between the photovoltaic panels in combination with the photovoltaic panel installation layout and the solar position change, and recalculating the shielding area when the solar position change reaches the critical value; S5, comprehensively considering the shielding of the obstacles and the photovoltaic panels to calculate the total shielding area of the photovoltaic panels at different time points, and determining the power generation power change law under the comprehensive shielding; S51, integrating the shielding area data of the obstacles and the shielding area data of the photovoltaic panel bodies, and judging whether there is an overlapping part between the shielding area data of the obstacles and the shielding area data of the photovoltaic panel bodies by using a space geometry algorithm, extracting the boundary of the overlapping area when there is an overlapping area, and calculating the overlapping area; S52, when there is an overlapping area, adding the shielding area data of the obstacles and the shielding area data of the photovoltaic panel bodies and subtracting the overlapping area to obtain the total shielding area; when there is no overlapping area, directly adding the shielding area data of the obstacles and the shielding area data of the photovoltaic panel bodies to obtain the total shielding area. S521, Abstract the obstacles and the shading area of the photovoltaic panel body as polygons on a two-dimensional plane, and mark them as obstacle shading polygon P1 and photovoltaic panel body shading polygon P2 respectively, and obtain the coordinates of the vertices of each polygon; S522, Calculate the normal vector of each side of the two polygons as the separation axis, and then project the two polygons onto each separation axis to obtain the projection interval, when the projection intervals on all separation axes overlap, it is judged that the two polygons intersect, and there is an overlapping part; S523, When the two polygons have an overlap, use the intersection operation in Boolean operation, remove the edges that are not in the overlap area by comparing and combining the edges of the two polygons, extract the boundary of the overlap area, and calculate its area using the shoelace formula, the specific formula is: ; wherein is the calculated overlapping area, n is the number of vertices, are the horizontal and vertical coordinates of the i-th vertex of the polygon, respectively, are the horizontal and vertical coordinates of the i-th vertex of the polygon, respectively, are the horizontal and vertical coordinates of the i-th vertex of the polygon, respectively, are the horizontal and vertical coordinates of the last vertex of the polygon, respectively, are the horizontal and vertical coordinates of the first vertex of the polygon, respectively; S53, Match the photovoltaic power generation data with the corresponding total shading area data, ensure that each total shading area data has corresponding power generation data, and use the regression analysis algorithm again to analyze the relationship between the total shading area and the power generation, and establish a relationship model between the total shading power generation; S6, Integrate the light intensity, shading area and other data to calculate and output the predicted photovoltaic power generation interval.

2. The photovoltaic power generation power interval prediction method according to claim 1, characterized in that, The collection of photovoltaic power generation, meteorological, photovoltaic panel surrounding obstacles, photovoltaic panel layout and solar position data includes the following steps: S11, Collect photovoltaic power generation data, meteorological data; Collect the position, height, shape and other information of photovoltaic panels and obstacles around photovoltaic panels; At the same time, collect the inherent parameters, operation parameters of photovoltaic components, and installation layout information of photovoltaic panels, including the number of photovoltaic panels, arrangement method, spacing, orientation, inclination; S13, Collect the altitude angle and azimuth angle information of the sun at different time points.

3. The photovoltaic power generation power interval prediction method according to claim 2, characterized in that, The construction of the solar position change model, the analysis of the light intensity difference of photovoltaic panels at different positions, the establishment of the solar position-light intensity-power generation relationship model, and the derivation of the different power generations caused by light differences include the following steps: S21, Use Julian day to accurately represent time; Calculate the solar declination angle, hour angle according to the astronomical formula, and then combine the local latitude to establish a model of the change of the solar altitude angle and azimuth angle with time through the trigonometric function; S22, Calculate the atmospheric mass according to the solar altitude angle, combine the local atmospheric composition, use the empirical formula to analyze the influence of atmospheric scattering and absorption on light, obtain the direct and scattered light intensity reaching the ground, and then combine the orientation and inclination of the photovoltaic panel to calculate the surface light intensity; S23, Use the inherent parameters and operation parameters of the photovoltaic components to calculate the temperature coefficient correction efficiency of the photovoltaic panel, combine the temperature coefficient correction efficiency with the photovoltaic panel photoelectric conversion characteristics, and establish a solar position-light intensity-power generation relationship model according to the light intensity, correction efficiency and photovoltaic panel area. 4.The photovoltaic power interval prediction method of claim 1, wherein, The determination of the shading area change amount corresponding to the obvious change of the photovoltaic panel power generation according to the influence degree of the shading area of the obstacle on the power generation, and the use of the change amount of the solar altitude angle and azimuth angle at this time as the recalculation critical value include the following steps: S351, The relationship model between the shading area and the power generation is: ; where P is the power generated, S is the area shaded, h is the solar elevation angle, A is the azimuth angle, is the intercept, is the regression coefficient, is the error term, and are all calculated by fitting the actual data. S352, for different blocking area change amounts ΔS, calculate the power generation power change amount ΔP according to the established correlation model, and calculate the power generation power change rate r, the specific formula is: ; wherein is the initial power generation; Combined with the actual operation of the photovoltaic panel and economic analysis, determine the threshold r1 of the obvious change of the power generation power, analyze different time points in time sequence, calculate the blocking area change amount ΔS and the corresponding power generation power change rate r of each time point, find out the time points of the power generation power change rate r exceeding the threshold r1, and record the blocking area change amount ΔS corresponding to these time points; S353, for each screened obvious change point, record the corresponding solar altitude angle change amount and azimuth angle change amount, determine the solar altitude angle change amount Δh and azimuth angle change amount ΔA corresponding to the obvious change of the photovoltaic panel power generation power by calculating the average value, and take the two change amounts as the recalculation critical value.

5. The photovoltaic power generation power interval prediction method according to claim 1, characterized in that, The combination of photovoltaic panel installation layout and solar position change, analysis of the shading between photovoltaic panels, recalculation of the shading area when the solar position change reaches the critical value includes the following steps: S41, the altitude angle and azimuth angle of the sun are recalculated by using astronomical algorithm; based on the established geometric relationship model, the position and form information of each photovoltaic panel is further refined, and at the same time, the influence of the orientation and inclination angle of the photovoltaic panel on the light receiving is determined; S43, parallel light is emitted from the current solar position, and the intersection of the light and the plane where other photovoltaic panels are located is calculated to determine whether the intersection is within the boundary of the photovoltaic panel. If the intersection is within the boundary, it is considered that there is shading; S44, when there is shading between photovoltaic panels, the area of the shaded part is calculated by using computer graphics algorithm; S45, match the calculated shading area data between photovoltaic panels with the corresponding photovoltaic power generation power data, analyze the relationship between shading area and power generation power by regression analysis algorithm, and establish the relationship model between shading area and power generation power to describe the influence degree of photovoltaic panel body shading on power generation power. When the cumulative change amount of solar altitude angle and azimuth angle reaches the preset recalculation critical value, trigger the operation of recalculating the photovoltaic panel body shading area.

6. The photovoltaic power generation power interval prediction method according to claim 1, characterized in that, The combination of illumination intensity, shading area and other data, calculation and output of the predicted photovoltaic power generation power interval includes the following steps: S61, integrate real-time acquisition of solar position information, meteorological data, shading area and other data; S62, use the solar position-illumination intensity-power generation power relationship model, input the illumination intensity, temperature coefficient correction efficiency of photovoltaic panel and photovoltaic panel area, obtain multiple data values of the predicted power generation power based on illumination intensity within the prediction time under the condition of no shading, and take the maximum and minimum values as the photovoltaic power generation power interval. When there is no shading, the power generation power interval is directly output; S63, when there is shading, directly use the relationship model between total shading power generation power to obtain the influence value, and use the influence value to correct the predicted power generation power interval, and output the corrected power generation power interval.

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