Optical power prediction method and system considering snow covering photovoltaic panels

Abstract

The present invention discloses a method and system for predicting light power considering snow-covered photovoltaic panels, including: step one, determining the influencing factors that cause photovoltaic power loss of photovoltaic panels under snow-covered conditions, and establishing a relationship model between each influencing factor and the photovoltaic power loss rate; step two, establishing a prediction model for photovoltaic power under snow-covered conditions based on the relationship model between each influencing factor and the photovoltaic power loss rate; step three, based on the constructed photovoltaic power prediction model, predicting the photovoltaic power of photovoltaic panels under snow-covered conditions. It can be seen that the present invention particularly considers the influence of snow accumulation on photovoltaic panels, and fully considers the various key influencing factors of photovoltaic power loss rate under snow-covered conditions, with high prediction accuracy, thereby significantly improving the power prediction accuracy of photovoltaic power stations in snow-covered environments, and effectively reducing the risk of large-scale photovoltaic power supply shortages caused by snow.

Description

Technical Field

[0001] The invention relates to a method and system for predicting light power considering snow covering of photovoltaic panels, belonging to the technical field of photovoltaic power prediction. Background Art

[0002] In order to further promote the realization of the dual carbon goals, photovoltaic power generation has been gaining momentum in recent years, and installed capacity has increased rapidly. However, since photovoltaic power stations are located in the wild and are exposed to various types of disastrous weather such as rain, snow, ice, thunderstorms, strong winds, and short-term heavy precipitation all year round, their normal operation is greatly challenged, especially the ice and snow in winter and spring every year, which will seriously affect the safe and stable operation of photovoltaic power stations. Ice and snow adhere to and accumulate on photovoltaic panels, which greatly hinders the photovoltaic panels from receiving solar radiation, resulting in a significant reduction in the power generation efficiency and power generation of photovoltaic modules, seriously affecting the power supply guarantee business of the power grid. On the other hand, the photovoltaic power generation power prediction error caused by the snow covering the photovoltaic panels has increased significantly, and the photovoltaic output level cannot be accurately estimated, which has brought huge challenges to the grid connection and consumption of new energy and power dispatching.

[0003] At present, the prediction of power generation of photovoltaic modules under snow cover has not received enough attention and in-depth research. Although a small number of existing studies have analyzed the impact of snow on photovoltaic power generation efficiency from a macro perspective, these studies often require a large amount of data and do not fully consider the snow melting process, which limits their application in the economic analysis of artificial deicing of photovoltaic power stations and the accurate prediction and precise scheduling of renewable energy power generation by the power sector. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for predicting the optical power of photovoltaic panels with snow. The method establishes a mathematical model between the influencing factors and the photovoltaic panel power generation power by deeply analyzing the key factors and parameters that affect the photovoltaic power generation power under snow conditions. The method particularly considers the influence of snow on the photovoltaic panel. This method is of great significance for improving the operating efficiency of photovoltaic power stations and the dispatching accuracy of power systems.

[0005] In order to achieve the above technical objectives, the present invention will adopt the following technical solutions:

[0006] A method for predicting the optical power of photovoltaic panels with snow covering is provided, comprising the following steps:

[0007] Step 1: determine the influencing factors that cause photovoltaic power loss of photovoltaic panels under snow-covered conditions, and establish a relationship model between each influencing factor and photovoltaic power loss rate;

[0008] Step 2: Establish a prediction model for photovoltaic power generation under snow-covered conditions based on the relationship model between various influencing factors and photovoltaic power loss rate;

[0009] Step 3: Based on the constructed photovoltaic power generation prediction model, the photovoltaic power generation of photovoltaic panels under snow-covered conditions is predicted. Make predictions.

[0010] Preferably, in step one, the determined influencing factors include: the thickness of snow on the surface of the photovoltaic panel; the solar radiation incident on the surface of the photovoltaic panel; the temperature, humidity, wind speed and wind direction of the environment where the photovoltaic panel is located.

[0011] Preferably, the relationship model between the thickness of snow on the surface of the photovoltaic panel and the photovoltaic power loss rate is expressed as:

[0012] ;

[0013] The relationship model between the solar radiation incident on the surface of the photovoltaic panel and the photovoltaic power loss rate is expressed as:

[0014] ;

[0015] The relationship model between the temperature of the environment where the photovoltaic panels are located and the photovoltaic power loss rate is expressed as:

[0016] ;

[0017] The relationship model between the humidity of the environment where the photovoltaic panels are located and the photovoltaic power loss rate is expressed as:

[0018] ;

[0019] The relationship model between the wind speed, wind direction and photovoltaic power loss rate in the environment where the photovoltaic panels are located is expressed as:

[0020] ;

[0021] ;

[0022] In the above formula: is the thickness of snow on the ground where the photovoltaic panel is located, and θ is the inclination angle of the photovoltaic panel; Represents the solar radiation incident on the surface of the photovoltaic panel; is the temperature of the environment where the photovoltaic panels are located; is the critical temperature; is the humidity of the environment where the photovoltaic panels are located; is the critical humidity; Indicates the wind speed in the environment where the photovoltaic panels are located; Represents the angle between the wind speed and the vertical direction of the photovoltaic panel.

[0023] Preferably, the photovoltaic power generation power prediction model constructed in step 2 is as follows:

[0024] = )

[0025] ;

[0026] Where: represents the power generated by the clean photovoltaic panel; A is the area of ​​the photovoltaic panel, is the thickness of snow on the ground where the photovoltaic panel is located, and θ is the inclination angle of the photovoltaic panel; is the relationship index between the snow coverage of photovoltaic panels and the photovoltaic power loss rate; Represents the solar radiation incident on the surface of the photovoltaic panel; is the relationship index between incident solar radiation and photovoltaic power loss rate; is the temperature of the environment where the photovoltaic panels are located; is the critical temperature; It is the relationship index between the temperature of the environment where the photovoltaic panels are located and the photovoltaic power loss rate; is the humidity of the environment where the photovoltaic panels are located; is the critical humidity; It is the relationship index between the humidity of the environment where the photovoltaic panels are located and the photovoltaic power loss rate; Indicates the wind speed in the environment where the photovoltaic panels are located; Represents the angle between the wind speed and the vertical direction of the photovoltaic panel; is the relationship index between the vertical component of wind speed and the photovoltaic power loss rate; It is the relationship index between the parallel component of wind speed and the photovoltaic power loss rate.

[0027] Preferably, the photovoltaic power loss rate of the photovoltaic panel caused by the thickness of snow on the surface of the photovoltaic panel is expressed as:

[0028] ;

[0029] Where: A is the area of ​​the photovoltaic panel, is the thickness of snow on the ground where the photovoltaic panel is located, and θ is the inclination angle of the photovoltaic panel; is the relationship index between the snow coverage of photovoltaic panels and the photovoltaic power loss rate;

[0030] The photovoltaic power loss rate of the photovoltaic panel caused by solar radiation incident on the surface of the photovoltaic panel is expressed as:

[0031] ;

[0032] Where: Represents the solar radiation incident on the surface of the photovoltaic panel; is the relationship index between incident solar radiation and photovoltaic power loss rate;

[0033] The photovoltaic power loss rate of the photovoltaic panel caused by the temperature of the environment where the photovoltaic panel is located is expressed as:

[0034] ;

[0035] Where: is the temperature of the environment where the photovoltaic panels are located; is the critical temperature; It is the relationship index between the temperature of the environment where the photovoltaic panels are located and the photovoltaic power loss rate;

[0036] The photovoltaic power loss rate of the photovoltaic panel caused by the humidity of the environment where the photovoltaic panel is located is expressed as:

[0037] ;

[0038] Where: is the humidity of the environment where the photovoltaic panels are located; is the critical humidity; It is the relationship index between the humidity of the environment where the photovoltaic panels are located and the photovoltaic power loss rate;

[0039] The photovoltaic power loss rate of the photovoltaic panel caused by the vertical component of wind speed is expressed as:

[0040] ;

[0041] The photovoltaic power loss rate of the photovoltaic panel caused by the parallel component of wind speed is expressed as:

[0042] ;

[0043] Where: Indicates the wind speed in the environment where the photovoltaic panels are located; Represents the angle between the wind speed and the vertical direction of the photovoltaic panel; is the relationship index between the vertical component of wind speed and the photovoltaic power loss rate; It is the relationship index between the parallel component of wind speed and the photovoltaic power loss rate.

[0044] Preferably, the photovoltaic power generation power prediction model constructed in step 2 is as follows:

[0045] = )

[0046] = );

[0047] Where: Indicates the power generated by clean photovoltaic panels; Indicates the photovoltaic power loss rate of the photovoltaic panel caused by the thickness of snow on the surface of the photovoltaic panel; It represents the photovoltaic power loss rate of the photovoltaic panel caused by the solar radiation incident on the surface of the photovoltaic panel; Indicates the photovoltaic power loss rate of the photovoltaic panel caused by the temperature of the environment where the photovoltaic panel is located; Indicates the photovoltaic power loss rate of the photovoltaic panel caused by the humidity of the environment where the photovoltaic panel is located; Indicates the photovoltaic power loss rate of the photovoltaic panel caused by the vertical component of wind speed; It represents the photovoltaic power loss rate of the photovoltaic panel caused by the parallel component of wind speed.

[0048] Another technical object of the present invention is to provide a light power prediction system considering snow covering photovoltaic panels, comprising:

[0049] A data acquisition module is used to collect influencing factors that cause photovoltaic power loss of photovoltaic panels under snow-covered conditions, and transmit the collected influencing factors to the calculation module;

[0050] The calculation module integrates the relationship model between various influencing factors and the photovoltaic power loss rate; based on the influencing factors transmitted from the data acquisition module, the photovoltaic power loss rate of the photovoltaic battery panel under the snow-covered condition caused by the corresponding influencing factors can be calculated through the relationship model between the corresponding influencing factors and the photovoltaic power loss rate, and the calculated photovoltaic power loss rate is transmitted to the prediction module;

[0051] The prediction module integrates a prediction model for photovoltaic power generation under snow-covered conditions; the prediction model can predict the photovoltaic power generation of photovoltaic panels under snow-covered conditions based on the photovoltaic power loss rate transmitted from the calculation module.

[0052] Preferably, the influencing factors collected by the data collection module include: the thickness of snow on the surface of the photovoltaic panel; the solar radiation incident on the surface of the photovoltaic panel; the temperature, humidity, wind speed and wind direction of the environment where the photovoltaic panel is located.

[0053] Preferably, the photovoltaic power loss rate of the photovoltaic panel caused by the thickness of snow on the surface of the photovoltaic panel is expressed as:

[0054] ;

[0055] Where: A is the area of ​​the photovoltaic panel, is the thickness of snow on the ground where the photovoltaic panel is located, θ is the inclination angle of the photovoltaic panel; k is the relationship index between the snow coverage of the photovoltaic panel area and the photovoltaic power loss rate;

[0056] The photovoltaic power loss rate of the photovoltaic panel caused by solar radiation incident on the surface of the photovoltaic panel is expressed as:

[0057] ;

[0058] Where: Represents the solar radiation incident on the surface of the photovoltaic panel; is the relationship index between incident solar radiation and photovoltaic power loss rate;

[0059] The photovoltaic power loss rate of the photovoltaic panel caused by the temperature of the environment where the photovoltaic panel is located is expressed as:

[0060] ;

[0061] Where: is the temperature of the environment where the photovoltaic panels are located; is the critical temperature; It is the relationship index between the temperature of the environment where the photovoltaic panels are located and the photovoltaic power loss rate;

[0062] The photovoltaic power loss rate of the photovoltaic panel caused by the humidity of the environment where the photovoltaic panel is located is expressed as:

[0063] ;

[0064] Where: is the humidity of the environment where the photovoltaic panels are located; is the critical humidity; It is the relationship index between the humidity of the environment where the photovoltaic panels are located and the photovoltaic power loss rate;

[0065] The photovoltaic power loss rate of the photovoltaic panel caused by the vertical component of wind speed is expressed as:

[0066] ;

[0067] The photovoltaic power loss rate of the photovoltaic panel caused by the parallel component of wind speed is expressed as:

[0068] ;

[0069] Where: Indicates the wind speed in the environment where the photovoltaic panels are located; Represents the angle between the wind speed and the vertical direction of the photovoltaic panel; is the relationship index between the vertical component of wind speed and the photovoltaic power loss rate; It is the relationship index between the parallel component of wind speed and the photovoltaic power loss rate.

[0070] Preferably, the prediction model of photovoltaic power generation is as follows:

[0071] = )

[0072] = );

[0073] Where: Indicates the power generated by clean photovoltaic panels; Indicates the photovoltaic power loss rate of the photovoltaic panel caused by the thickness of snow on the surface of the photovoltaic panel; It represents the photovoltaic power loss rate of the photovoltaic panel caused by the solar radiation incident on the surface of the photovoltaic panel; Indicates the photovoltaic power loss rate of the photovoltaic panel caused by the temperature of the environment where the photovoltaic panel is located; Indicates the photovoltaic power loss rate of the photovoltaic panel caused by the humidity of the environment where the photovoltaic panel is located; Indicates the photovoltaic power loss rate of the photovoltaic panel caused by the vertical component of wind speed; It represents the photovoltaic power loss rate of the photovoltaic panel caused by the parallel component of wind speed.

[0074] Based on the above technical objectives, the present invention has the following advantages over the prior art:

[0075] The present invention fully considers the multiple key influencing factors of photovoltaic power loss rate under snow-covered conditions, establishes a mathematical model between the influencing factors and the photovoltaic panel power generation, and develops a new optical power prediction method, which especially considers the influence of snow accumulation on photovoltaic panels, significantly improves the power prediction accuracy of photovoltaic power stations under snow-covered environments, and effectively reduces the risk of large-scale photovoltaic power supply shortages caused by snow. It is of great significance to improve the operating efficiency of photovoltaic power stations and the dispatching accuracy of power systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 It is a schematic diagram of a photovoltaic power station;

[0077] Figure 2 is a flow chart of the optical power prediction method considering snow covering photovoltaic panels according to the present invention;

[0078] Figure 3 It is a comparison chart of the predicted photovoltaic power generation curve and the actual photovoltaic power generation curve under the condition of snow cover in a photovoltaic power station relative to the clean photovoltaic panel power generation curve;

[0079] In the figure: 1 - snow on the ground; 2 - photovoltaic panels; 3 - snow on the surface of photovoltaic panels; θ represents the inclination angle of the photovoltaic panels. DETAILED DESCRIPTION

[0080] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. The following description of at least one exemplary embodiment is actually only illustrative and is by no means any limitation to the present invention and its application or use. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Unless otherwise specified, the relative arrangement, expressions and numerical values ​​of the components and steps described in these embodiments do not limit the scope of the present invention. The techniques, methods and equipment known to ordinary technicians in the relevant fields may not be discussed in detail, but in appropriate cases, the techniques, methods and equipment should be regarded as part of the specification. In all examples shown and discussed here, any specific value should be interpreted as merely exemplary, rather than as a limitation. Therefore, other examples of the exemplary embodiments may have different values.

[0081] Example 1

[0082] like Figure 1 , Figure 2 As shown, the optical power prediction method considering snow covering photovoltaic panels of the present invention comprises the following steps:

[0083] Step 1: determine the influencing factors that cause photovoltaic power loss of photovoltaic panels under snow-covered conditions, and establish a relationship model between each influencing factor and photovoltaic power loss rate;

[0084] Through in-depth analysis of the key factors affecting photovoltaic power generation under snow conditions, the influencing factors determined include: the thickness of snow on the surface of the photovoltaic panels; the solar radiation incident on the surface of the photovoltaic panels; the temperature, humidity, wind speed and wind direction of the environment where the photovoltaic panels are located.

[0085] Specifically, assuming that under certain sunshine conditions, the power generated by the clean photovoltaic panel is , the power loss rate of photovoltaic power generation under snow cover is defined as Snow reflects and absorbs solar radiation, thus greatly reducing the amount of radiation reaching the surface of photovoltaic panels. It can be seen that the thickness of snow on the surface of photovoltaic panels is an important factor affecting the light power, and a relationship model between the thickness of snow on the surface of photovoltaic panels and the photovoltaic power loss rate is established, which is expressed as:

[0086] ;

[0087] By obtaining historical photovoltaic power generation data and the thickness of snow on the surface of photovoltaic panels, the relationship index k between the snow coverage of photovoltaic panels and the photovoltaic power loss rate can be fitted, and then the photovoltaic power loss rate of photovoltaic panels caused by the thickness of snow on the surface of photovoltaic panels can be obtained, which can be expressed as:

[0088] ;

[0089] Where: A is the area of ​​the photovoltaic panel, is the thickness of snow on the ground where the photovoltaic panel is located, and θ is the inclination angle of the photovoltaic panel.

[0090] Attached Figure 1 The schematic diagram of the structure of a photovoltaic panel 2 in a photovoltaic power station under snow-covered conditions is disclosed. In the figure, the photovoltaic panel 2 has a surface snow with a thickness of H. At this time, the thickness of the snow 1 on the ground where the photovoltaic panel 2 is located is , the inclination angle of the photovoltaic panel is θ, then the surface snow thickness H of the photovoltaic panel 2 can be expressed as: H= * .

[0091] The solar radiation incident on the photovoltaic panel is , the stronger the solar radiation, the less conducive it is to snow cover. Therefore, a relationship model between the solar radiation incident on the photovoltaic panels and the photovoltaic power loss rate can be established, expressed as:

[0092] ;

[0093] By obtaining historical photovoltaic power data and solar radiation incident on photovoltaic panels, we can fit the relationship index between solar radiation incident on photovoltaic panels and photovoltaic power loss rate. Then we can get the photovoltaic power loss rate of photovoltaic panels caused by solar radiation incident on the surface of photovoltaic panels, which can be expressed as:

[0094] ;

[0095] Where: Represents the solar radiation incident on the surface of the photovoltaic panel; It is the relationship index between incident solar radiation and photovoltaic power loss rate.

[0096] When the temperature is lower than the critical temperature, it is conducive to snow accumulation, and the light power loss rate of the photovoltaic panels increases. When the temperature is higher than the critical temperature, it is not conducive to snow formation, and the light power loss rate of the photovoltaic panels decreases. Therefore, a relationship model between the temperature of the environment where the photovoltaic panels are located and the photovoltaic power loss rate can be established, which is expressed as:

[0097] ;

[0098] By obtaining the historical photovoltaic power generation data and the temperature of the environment where the photovoltaic panels are located, the relationship index between the temperature of the environment where the photovoltaic panels are located and the photovoltaic power loss rate can be fitted, and then the photovoltaic power loss rate of the photovoltaic panels caused by the temperature of the environment where the photovoltaic panels are located can be obtained, which can be expressed as:

[0099] ;

[0100] Where: is the temperature of the environment where the photovoltaic panels are located; is the critical temperature; It is the relationship index between the temperature of the environment where the photovoltaic panels are located and the photovoltaic power loss rate.

[0101] When the humidity is higher than the critical humidity, it is conducive to snow accumulation and the optical power loss rate increases. When the humidity is lower than the critical humidity, it is not conducive to snow formation and the optical power loss rate decreases. Therefore, a relationship model between the humidity of the environment where the photovoltaic panels are located and the photovoltaic power loss rate can be established, which is expressed as:

[0102] ;

[0103] By obtaining the historical photovoltaic power generation data and the humidity of the environment where the photovoltaic panels are located, the relationship index between the humidity of the environment where the photovoltaic panels are located and the photovoltaic power loss rate can be fitted, and then the photovoltaic power loss rate of the photovoltaic panels caused by the humidity of the environment where the photovoltaic panels are located can be expressed as:

[0104] ;

[0105] Where: is the humidity of the environment where the photovoltaic panels are located; is the critical humidity; It is the relationship index between the humidity of the environment where the photovoltaic panels are located and the photovoltaic power loss rate.

[0106] The wind speed vector Projected on the plane of the photovoltaic panel and the vertical normal, assuming that the angle between the wind speed vector and the vertical direction of the photovoltaic panel is , then the wind speed component perpendicular to the photovoltaic panel is Vcos , the wind speed component parallel to the photovoltaic panel is Vsin .

[0107] For the wind speed component perpendicular to the photovoltaic panel , when the angle Less than 90 When the wind speed component is positive, the wind direction is downward, which is conducive to snow cover. Greater than 90 When the wind speed component is negative, the wind blows upward, which is not conducive to snow cover. , the larger the wind speed component parallel to the photovoltaic panel, the more unfavorable it is for snow cover.

[0108] Therefore, a relationship model between the wind speed, wind direction and photovoltaic power loss rate in the environment where the photovoltaic panels are located can be established, which can be expressed as:

[0109] ;

[0110] ;

[0111] By obtaining the historical photovoltaic power generation data and the wind speed and wind direction of the environment where the photovoltaic panels are located, we can fit the relationship index between the vertical component of wind speed and the photovoltaic power loss rate, and the relationship index between the parallel component of wind speed and the photovoltaic power loss rate, and then construct the photovoltaic power loss rate of the photovoltaic panel caused by the vertical component of wind speed as follows:

[0112] ;

[0113] The photovoltaic power loss rate of the photovoltaic panel caused by the parallel component of wind speed is expressed as:

[0114] ;

[0115] Where: Indicates the wind speed in the environment where the photovoltaic panels are located; Represents the angle between the wind speed and the vertical direction of the photovoltaic panel; is the relationship index between the vertical component of wind speed and the photovoltaic power loss rate; It is the relationship index between the parallel component of wind speed and the photovoltaic power loss rate.

[0116] Step 2: Establish a prediction model for photovoltaic power generation under snow-covered conditions based on the relationship model between various influencing factors and photovoltaic power loss rate;

[0117] Photovoltaic power generation under snow-covered conditions It can be estimated as:

[0118] = )

[0119] = );

[0120] ;

[0121] Where: Indicates the power generated by clean photovoltaic panels; Indicates the photovoltaic power loss rate of the photovoltaic panel caused by the thickness of snow on the surface of the photovoltaic panel; It represents the photovoltaic power loss rate of the photovoltaic panel caused by the solar radiation incident on the surface of the photovoltaic panel; Indicates the photovoltaic power loss rate of the photovoltaic panel caused by the temperature of the environment where the photovoltaic panel is located; Indicates the photovoltaic power loss rate of the photovoltaic panel caused by the humidity of the environment where the photovoltaic panel is located; Indicates the photovoltaic power loss rate of the photovoltaic panel caused by the vertical component of wind speed; It represents the photovoltaic power loss rate of the photovoltaic panel caused by the parallel component of wind speed.

[0122] Step 3: Based on the constructed photovoltaic power generation prediction model, the photovoltaic power generation of photovoltaic panels under snow-covered conditions is predicted. Make predictions.

[0123] A photovoltaic power station was selected, and the historical power data were fitted with incident solar radiation, ambient temperature, ambient humidity and wind speed to obtain the nonlinear relationship index of each variable, so as to establish a light power prediction model under snow-covered conditions. Figure 3 The prediction results of photovoltaic power stations covered with snow during the cold wave process in Jiangsu on February 25, 2024 are given. When the photovoltaic panels are covered with snow, the photovoltaic power generation power drops significantly. The model can not only accurately predict this power reduction, but also the prediction results are basically consistent with the actual power generation trend, indicating that the model has certain prediction performance.

[0124] Example 2

[0125] Based on the same inventive concept, this embodiment discloses a light power prediction system considering snow covering photovoltaic panels, including:

[0126] A data acquisition module is used to collect influencing factors that cause photovoltaic power loss of photovoltaic panels under snow-covered conditions, and transmit the collected influencing factors to the calculation module;

[0127] Factors that affect the photovoltaic power loss of photovoltaic panels under snow-covered conditions include the thickness of snow on the surface of the photovoltaic panels; the solar radiation incident on the surface of the photovoltaic panels; and the temperature, humidity, wind speed and direction of the environment in which the photovoltaic panels are located.

[0128] The calculation module integrates the relationship model between various influencing factors and the photovoltaic power loss rate; based on the influencing factors transmitted from the data acquisition module, it can calculate the photovoltaic power loss rate of the photovoltaic panel under snow-covered conditions caused by the corresponding influencing factors through the relationship model between the corresponding influencing factors and the photovoltaic power loss rate, and transmit the calculated photovoltaic power loss rate to the prediction module.

[0129] Specifically, the photovoltaic power loss rate of the photovoltaic panel caused by the thickness of snow on the surface of the photovoltaic panel is expressed as:

[0130] ;

[0131] Where: A is the area of ​​the photovoltaic panel, is the thickness of snow on the ground where the photovoltaic panel is located, θ is the inclination angle of the photovoltaic panel; k is the relationship index between the snow coverage of the photovoltaic panel area and the photovoltaic power loss rate;

[0132] The photovoltaic power loss rate of the photovoltaic panel caused by solar radiation incident on the surface of the photovoltaic panel is expressed as:

[0133] ;

[0134] Where: Represents the solar radiation incident on the surface of the photovoltaic panel; is the relationship index between incident solar radiation and photovoltaic power loss rate;

[0135] The photovoltaic power loss rate of the photovoltaic panel caused by the temperature of the environment where the photovoltaic panel is located is expressed as:

[0136] ;

[0137] Where: is the temperature of the environment where the photovoltaic panels are located; is the critical temperature; It is the relationship index between the temperature of the environment where the photovoltaic panels are located and the photovoltaic power loss rate;

[0138] The photovoltaic power loss rate of the photovoltaic panel caused by the humidity of the environment where the photovoltaic panel is located is expressed as:

[0139] ;

[0140] Where: is the humidity of the environment where the photovoltaic panels are located; is the critical humidity; It is the relationship index between the humidity of the environment where the photovoltaic panels are located and the photovoltaic power loss rate;

[0141] The photovoltaic power loss rate of the photovoltaic panel caused by the vertical component of wind speed is expressed as:

[0142] ;

[0143] The photovoltaic power loss rate of the photovoltaic panel caused by the parallel component of wind speed is expressed as:

[0144] ;

[0145] Where: Indicates the wind speed in the environment where the photovoltaic panels are located; Represents the angle between the wind speed and the vertical direction of the photovoltaic panel; is the relationship index between the vertical component of wind speed and the photovoltaic power loss rate; It is the relationship index between the parallel component of wind speed and the photovoltaic power loss rate.

[0146] The prediction module integrates a prediction model for photovoltaic power generation under snow-covered conditions; the prediction model can predict the photovoltaic power generation of photovoltaic panels under snow-covered conditions based on the photovoltaic power loss rate transmitted from the calculation module.

[0147] Specifically, the prediction model of photovoltaic power generation is as follows:

[0148] = )

[0149] = );

[0150] Where: Indicates the power generated by clean photovoltaic panels; Indicates the photovoltaic power loss rate of the photovoltaic panel caused by the thickness of snow on the surface of the photovoltaic panel; It represents the photovoltaic power loss rate of the photovoltaic panel caused by the solar radiation incident on the surface of the photovoltaic panel; Indicates the photovoltaic power loss rate of the photovoltaic panel caused by the temperature of the environment where the photovoltaic panel is located; Indicates the photovoltaic power loss rate of the photovoltaic panel caused by the humidity of the environment where the photovoltaic panel is located; Indicates the photovoltaic power loss rate of the photovoltaic panel caused by the vertical component of wind speed; It represents the photovoltaic power loss rate of the photovoltaic panel caused by the parallel component of wind speed.

[0151] Embodiment 3:

[0152] Based on the same inventive concept, the present invention also provides a computer device, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding functions, so as to implement the steps of the method in the above embodiment.

[0153] Embodiment 4:

[0154] Based on the same inventive concept, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It is understandable that the computer-readable storage medium here can include both a built-in storage medium in a computer device and an extended storage medium supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of the method in the above embodiment.

[0155] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The schemes in the embodiments of the present invention may be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0156] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0157] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0158] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0159] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0160] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for predicting the optical power of photovoltaic panels with snow, characterized in that: The steps include: Step 1: determine the influencing factors that cause photovoltaic power loss of photovoltaic panels under snow-covered conditions, and establish a relationship model between each influencing factor and photovoltaic power loss rate; Step 2: Establish a prediction model for photovoltaic power generation under snow-covered conditions based on the relationship model between various influencing factors and photovoltaic power loss rate; Step 3: Based on the constructed photovoltaic power generation prediction model, the photovoltaic power generation of photovoltaic panels under snow-covered conditions is predicted. Make predictions; In step 1, the influencing factors determined include: the thickness of snow on the surface of the photovoltaic panel; the solar radiation incident on the surface of the photovoltaic panel; the temperature, humidity, wind speed and wind direction of the environment where the photovoltaic panel is located; The relationship model between the thickness of snow on the surface of photovoltaic panels and the photovoltaic power loss rate is expressed as: ； The relationship model between the solar radiation incident on the surface of the photovoltaic panel and the photovoltaic power loss rate is expressed as: ； The relationship model between the temperature of the environment where the photovoltaic panels are located and the photovoltaic power loss rate is expressed as: ； The relationship model between the humidity of the environment where the photovoltaic panels are located and the photovoltaic power loss rate is expressed as: ； The relationship model between the wind speed, wind direction and photovoltaic power loss rate in the environment where the photovoltaic panels are located is expressed as: ； ； In the above formula: is the thickness of snow on the ground where the photovoltaic panel is located, and θ is the inclination angle of the photovoltaic panel; Represents the solar radiation incident on the surface of the photovoltaic panel; is the temperature of the environment where the photovoltaic panels are located; is the critical temperature; is the humidity of the environment where the photovoltaic panels are located; is the critical humidity; Indicates the wind speed in the environment where the photovoltaic panels are located; Represents the angle between the wind speed and the vertical direction of the photovoltaic panel; The prediction model of photovoltaic power generation constructed in step 2 is as follows: = ) ； Where: represents the power generated by the clean photovoltaic panel; A is the area of ​​the photovoltaic panel, is the thickness of snow on the ground where the photovoltaic panel is located, θ is the inclination angle of the photovoltaic panel; k is the relationship index between the snow coverage of the photovoltaic panel area and the photovoltaic power loss rate; Represents the solar radiation incident on the surface of the photovoltaic panel; is the relationship index between incident solar radiation and photovoltaic power loss rate; is the temperature of the environment where the photovoltaic panels are located; is the critical temperature; It is the relationship index between the temperature of the environment where the photovoltaic panels are located and the photovoltaic power loss rate; is the humidity of the environment where the photovoltaic panels are located; is the critical humidity; It is the relationship index between the humidity of the environment where the photovoltaic panels are located and the photovoltaic power loss rate; Indicates the wind speed in the environment where the photovoltaic panels are located; Represents the angle between the wind speed and the vertical direction of the photovoltaic panel; is the relationship index between the vertical component of wind speed and the photovoltaic power loss rate; It is the relationship index between the parallel component of wind speed and the photovoltaic power loss rate.

2. A light power prediction system considering snow-covered photovoltaic panels, characterized in that: include: A data acquisition module is used to collect influencing factors that cause photovoltaic power loss of photovoltaic panels under snow-covered conditions, and transmit the collected influencing factors to the calculation module; The calculation module integrates the relationship model between various influencing factors and the photovoltaic power loss rate; based on the influencing factors transmitted from the data acquisition module, the photovoltaic power loss rate of the photovoltaic battery panel under the snow-covered condition caused by the corresponding influencing factors can be calculated through the relationship model between the corresponding influencing factors and the photovoltaic power loss rate, and the calculated photovoltaic power loss rate is transmitted to the prediction module; The prediction module integrates a prediction model of photovoltaic power generation under snow-covered conditions; the prediction model can predict the photovoltaic power generation of the photovoltaic panels under snow-covered conditions according to the photovoltaic power loss rate transmitted from the calculation module; The influencing factors collected by the data acquisition module include: the thickness of snow on the surface of the photovoltaic panel; the solar radiation incident on the surface of the photovoltaic panel; the temperature, humidity, wind speed and wind direction of the environment where the photovoltaic panel is located; The photovoltaic power loss rate of the photovoltaic panel caused by the thickness of snow on the surface of the photovoltaic panel is expressed as: ； Where: A is the area of ​​the photovoltaic panel, is the thickness of snow on the ground where the photovoltaic panel is located, θ is the inclination angle of the photovoltaic panel; k is the relationship index between the snow coverage of the photovoltaic panel area and the photovoltaic power loss rate; The photovoltaic power loss rate of the photovoltaic panel caused by solar radiation incident on the surface of the photovoltaic panel is expressed as: ； Where: Represents the solar radiation incident on the surface of the photovoltaic panel; is the relationship index between incident solar radiation and photovoltaic power loss rate; The photovoltaic power loss rate of the photovoltaic panel caused by the temperature of the environment where the photovoltaic panel is located is expressed as: ； Where: is the temperature of the environment where the photovoltaic panels are located; is the critical temperature; It is the relationship index between the temperature of the environment where the photovoltaic panels are located and the photovoltaic power loss rate; The photovoltaic power loss rate of the photovoltaic panel caused by the humidity of the environment where the photovoltaic panel is located is expressed as: ； Where: is the humidity of the environment where the photovoltaic panels are located; is the critical humidity; It is the relationship index between the humidity of the environment where the photovoltaic panels are located and the photovoltaic power loss rate; The photovoltaic power loss rate of the photovoltaic panel caused by the vertical component of wind speed is expressed as: ； The photovoltaic power loss rate of the photovoltaic panel caused by the parallel component of wind speed is expressed as: ； Where: Indicates the wind speed in the environment where the photovoltaic panels are located; Represents the angle between the wind speed and the vertical direction of the photovoltaic panel; is the relationship index between the vertical component of wind speed and the photovoltaic power loss rate; is the relationship index between the parallel component of wind speed and the photovoltaic power loss rate; The prediction model of photovoltaic power generation is as follows: = ) = )； Where: Indicates the power generated by clean photovoltaic panels; Indicates the photovoltaic power loss rate of the photovoltaic panel caused by the thickness of snow on the surface of the photovoltaic panel; It represents the photovoltaic power loss rate of the photovoltaic panel caused by the solar radiation incident on the surface of the photovoltaic panel; Indicates the photovoltaic power loss rate of the photovoltaic panel caused by the temperature of the environment where the photovoltaic panel is located; Indicates the photovoltaic power loss rate of the photovoltaic panel caused by the humidity of the environment where the photovoltaic panel is located; Indicates the photovoltaic power loss rate of the photovoltaic panel caused by the vertical component of wind speed; It represents the photovoltaic power loss rate of the photovoltaic panel caused by the parallel component of wind speed.

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