Shadow estimation method for photovoltaic power generation system and computer program product

Abstract

The invention provides a shadow estimation method of a photovoltaic power generation system and a computer program product. The method comprises the following steps: determining a photovoltaic installation area where a photovoltaic power generation system is located; historical climate data of the photovoltaic installation area are collected; establishing an illumination simulation model of the photovoltaic installation area according to the historical climate data; predicting an illumination change trend of the photovoltaic installation area in a future time period according to the illumination simulation model; acquiring an obstacle model of the photovoltaic installation area; and estimating the shadow state of the photovoltaic installation area in the future time period according to the illumination change trend and the obstacle model. Through the method, the shadow state in the future time period can be accurately predicted in combination with the illumination change trend and the obstacle model, and the shadow prediction accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the field of power station design, and in particular to a shadow estimation method and a computer program product for a photovoltaic power generation system. Background Art

[0002] Solar photovoltaic power generation has been widely used around the world as a clean and renewable form of energy. However, the power generation efficiency of photovoltaic power stations is affected by many factors, among which shadow is an important influencing factor. Shadows not only reduce the power generation of photovoltaic modules, but may also cause hot spot effects, which in turn affect the service life of photovoltaic modules. Existing shadow estimation methods mainly rely on simple geometric models and empirical formulas. Although these methods can predict shadow distribution to a certain extent, they have the following shortcomings: Limited accuracy: Traditional geometric models usually assume that sunlight is parallel and ignore the influence of factors such as atmospheric scattering and clouds, resulting in inaccurate prediction results; Static modeling: Most existing methods use static modeling and cannot dynamically reflect the impact of environmental changes (such as tree growth and new building construction) on shadows; Lack of comprehensive evaluation: Existing shadow estimation methods often only focus on the shadow area, but ignore the comprehensive impact of shadows on power generation loss and hot spot effects. Summary of the invention

[0003] In view of the above problems, the present invention proposes a shadow estimation method and a computer program product for a photovoltaic power generation system that overcome the above problems or at least partially solve the above problems.

[0004] One object of the present invention is to more accurately predict shadows at future time points.

[0005] A further object of the present invention is to determine areas where photovoltaic modules are not allowed to be arranged, so as to avoid excessive losses.

[0006] A further object of the present invention is to optimize photovoltaic module installation planning.

[0007] In particular, the present invention provides a shadow estimation method for a photovoltaic power generation system, comprising:

[0008] Determine the photovoltaic installation area where the photovoltaic power generation system is located;

[0009] Collect historical climate data in the PV installation area;

[0010] Establish a light simulation model for the photovoltaic installation area based on historical climate data;

[0011] Predict the future trend of light changes in the photovoltaic installation area based on the light simulation model;

[0012] Obtain obstacle model of photovoltaic installation area;

[0013] Estimate the shadow status of the photovoltaic installation area in the future period based on the lighting change trend and obstacle model.

[0014] Optionally, the obstacle model includes a plant growth model;

[0015] The steps of evaluating the shadow status of the photovoltaic installation area in the future period based on the light change trend and obstacle model include:

[0016] Determine the target time point;

[0017] Predict the growth status of natural obstacles in the photovoltaic installation area at the target time point based on the plant growth model;

[0018] Determine the lighting status at the target time point based on the lighting change trend;

[0019] The shadow state caused by natural obstacles at the target time point is estimated according to the illumination state and the growth state of natural obstacles.

[0020] Optionally, the obstacle model includes a building change model;

[0021] The steps of evaluating the shadow status of the photovoltaic installation area in the future period based on the light change trend and obstacle model include:

[0022] Determine the target time point;

[0023] Predict the architectural form of building obstacles in the PV installation area at the target time point based on the building change model;

[0024] Determine the lighting status at the target time point based on the lighting change trend;

[0025] The shadow state of the building obstacle at the target time point is estimated according to the lighting state and the architectural form of the building obstacle.

[0026] Optionally, after the step of estimating the shadow state of the photovoltaic installation area in the future period according to the illumination variation trend and the obstacle model, the step further includes:

[0027] Obtain the photovoltaic module installation plan for the photovoltaic installation area;

[0028] Determine the position and posture of the target photovoltaic components affected by the shadow at the same time point according to the photovoltaic component installation plan and the shadow status;

[0029] According to the shadow status and the position and posture of the target PV module, the power generation loss and hot spot impact degree at the corresponding time point are evaluated;

[0030] Determine the comprehensive impact score based on the power generation loss assessment and hot spot impact assessment;

[0031] Simulate the adjustment of the target PV panel’s posture to reduce the comprehensive impact score.

[0032] Optionally, after the step of simulating and adjusting the posture of the target photovoltaic component, the step further includes:

[0033] Determine whether the comprehensive impact score exceeds the preset threshold;

[0034] If so, the area corresponding to the shadow whose comprehensive impact score exceeds the preset threshold is divided into an area where photovoltaic modules are not allowed to be arranged.

[0035] Optionally, after the step of determining whether the comprehensive impact score exceeds a preset threshold, the step further includes:

[0036] If the adjusted comprehensive impact score does not exceed the preset threshold, the PV module installation plan is optimized according to the simulation adjustment results of the target PV modules.

[0037] Optionally, the historical climate data includes historical light data and historical meteorological data;

[0038] The steps to establish a solar simulation model for a photovoltaic installation area based on historical climate data include:

[0039] Extract the lighting characteristics of the photovoltaic installation area based on historical lighting data;

[0040] Extract weather characteristics of the photovoltaic installation area based on historical meteorological data;

[0041] The lighting characteristics and weather characteristics are combined and input into a preset training model for training to obtain a lighting simulation model.

[0042] Optionally, the step of predicting the illumination variation trend of the photovoltaic installation area in the future period according to the illumination simulation model includes:

[0043] Get weather forecast data for future periods;

[0044] The meteorological forecast data and the light simulation model are used to predict the light change trend in the future period.

[0045] According to another aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above-mentioned shadow estimation methods for photovoltaic power generation systems are implemented.

[0046] According to another aspect of the present invention, a computer program product is provided, which includes a computer program. When the computer program is executed by a processor, the steps of any one of the above-mentioned shadow estimation methods for a photovoltaic power generation system are implemented.

[0047] According to another aspect of the present invention, a computer device is provided, comprising a memory, a processor, and a machine executable program stored in the memory and running on the processor, and the processor implements the steps of any one of the above-mentioned shadow estimation methods for a photovoltaic power generation system when executing the machine executable program.

[0048] The shadow estimation method of the photovoltaic power generation system of the present invention first determines the photovoltaic installation area where the photovoltaic power generation system is located; collects historical climate data of the photovoltaic installation area; establishes a light simulation model of the photovoltaic installation area based on the historical climate data; predicts the light change trend of the photovoltaic installation area in the future period based on the light simulation model; obtains the obstacle model of the photovoltaic installation area; and estimates the shadow state of the photovoltaic installation area in the future period based on the light change trend and the obstacle model. This method can combine the light change trend and the obstacle model to accurately predict the shadow state in the future period, thereby improving the accuracy of shadow prediction.

[0049] Furthermore, the shadow estimation method of the photovoltaic power generation system of the present invention can also determine the position and posture of the target photovoltaic component affected by the shadow at the same time point according to the photovoltaic component installation plan and the shadow state after obtaining the photovoltaic component installation plan of the photovoltaic installation area; perform the power generation loss assessment and hot spot impact assessment at the corresponding time point according to the shadow state and the position and posture of the target photovoltaic component; determine the comprehensive impact score according to the power generation loss assessment and the hot spot impact assessment; simulate and adjust the posture of the target photovoltaic component to reduce the comprehensive impact score, and also determine whether the comprehensive impact score exceeds the preset threshold; if so, the area corresponding to the shadow whose comprehensive impact score exceeds the preset threshold is divided into an area where photovoltaic components are not allowed to be arranged. This method can optimize the photovoltaic component installation plan, and determine the area where photovoltaic components are not allowed to be arranged to avoid excessive losses.

[0050] Based on the following detailed description of specific embodiments of the present invention in conjunction with the accompanying drawings, those skilled in the art will become more aware of the above and other objects, advantages and features of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Hereinafter, some specific embodiments of the present invention will be described in detail in an exemplary and non-limiting manner with reference to the accompanying drawings. The same reference numerals in the accompanying drawings indicate the same or similar components or parts. It should be understood by those skilled in the art that these drawings are not necessarily drawn to scale. In the accompanying drawings:

[0052] Figure 1 is a schematic flow chart of a shadow estimation method for a photovoltaic power generation system according to an embodiment of the present invention;

[0053] Figure 2is a schematic diagram of a process for estimating a shadow state corresponding to a natural obstacle in a shadow estimation method for a photovoltaic power generation system according to an embodiment of the present invention;

[0054] Figure 3 is a schematic diagram of a process for estimating a shadow state corresponding to a building obstacle in a shadow estimation method for a photovoltaic power generation system according to an embodiment of the present invention;

[0055] Figure 4 is a schematic diagram of a process for evaluating the degree of influence caused by shadows in a shadow estimation method for a photovoltaic power generation system according to an embodiment of the present invention;

[0056] Figure 5 is a schematic diagram of a computer program product according to an embodiment of the present invention;

[0057] Figure 6 is a schematic diagram of a computer-readable storage medium according to an embodiment of the present invention; and

[0058] Figure 7 is a schematic diagram of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0059] It should be understood by those skilled in the art that the embodiments described below are only some embodiments of the present invention, rather than all embodiments of the present invention, and these embodiments are intended to explain the technical principles of the present invention, rather than to limit the protection scope of the present invention. Based on the embodiments provided by the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should still fall within the protection scope of the present invention.

[0060] It should be noted that the logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, device or equipment (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or equipment and execute instructions), or used in combination with these instruction execution systems, devices or equipment.

[0061] Figure 1 FIG. 1 is a flow chart of a method for estimating shadows of a photovoltaic power generation system according to an embodiment of the present invention. Figure 1 As shown, the process of the shadow estimation method of the photovoltaic power generation system at least includes the following steps S101 to S106.

[0062] Step S101, determining a photovoltaic installation area where a photovoltaic power generation system is located.

[0063] Step S102, collect historical climate data of the photovoltaic installation area. In the specific geographical area where the photovoltaic power generation system is planned to be installed, systematically obtain and organize historical climate data over a period of time. These data generally include key parameters such as temperature, humidity, wind speed, cloud cover, precipitation and solar radiation. By collecting these historical climate data, a more comprehensive understanding of the climate characteristics of the area can be obtained.

[0064] Step S103: establishing a lighting simulation model for the photovoltaic installation area based on historical climate data.

[0065] In some optional embodiments, the historical climate data generally includes historical light data and historical meteorological data. The step of establishing a light simulation model for a photovoltaic installation area based on historical climate data generally may include: extracting light characteristics of the photovoltaic installation area based on historical light data; extracting weather characteristics of the photovoltaic installation area based on historical meteorological data; combining the light characteristics and weather characteristics, and inputting them into a preset training model for training to obtain a light simulation model.

[0066] Among them, light characteristics include: the distribution of daily light intensity, seasonal changes, sunshine duration and other key indicators. These data can help us understand the potential of regional light resources and their changes with seasons and weather conditions. Weather characteristics cover information such as temperature changes, precipitation, wind speed, cloud cover, etc. in different seasons. For example, how the thickness of the cloud layer affects the amount of solar radiation reaching the ground. The extracted light characteristics are then combined with weather characteristics to form a data set containing multi-dimensional variables. This data set comprehensively reflects the light-weather correlation in the photovoltaic installation area and is the basic material for establishing an accurate prediction model. Finally, this composite feature data set is input into a preset or customized training model, such as a random forest, neural network, or support vector machine. Through iterative learning of a large amount of historical data, the model can learn the complex relationship between light and weather, thereby outputting a light simulation model that can predict future light conditions.

[0067] Step S104, predicting the illumination change trend of the photovoltaic installation area in the future period according to the illumination simulation model. In some optional embodiments, the step of predicting the illumination change trend of the photovoltaic installation area in the future period according to the illumination simulation model may generally include: obtaining meteorological forecast data for the future period; and predicting the illumination change trend of the future period by the illumination simulation model of the meteorological forecast data. Among them, obtaining the meteorological forecast data for the future period can generally collect weather forecast information for the upcoming time period from a reliable meteorological forecast service agency or platform. These data usually include daily or even hourly forecasts, covering key parameters such as temperature, humidity, precipitation probability, wind speed and direction, and cloud cover. Based on the future meteorological forecast data, these data are input into the previously established illumination simulation model. The model is based on the association learning of historical illumination and meteorological characteristics, and can process the input meteorological variables, and then predict the corresponding illumination change trends such as illumination intensity and sunshine duration. The model may output a series of data points or charts to intuitively show the expected trend of illumination conditions in the future period, which is extremely important for the short-term operation scheduling, energy output estimation and maintenance plan adjustment of photovoltaic power stations.

[0068] Step S105, obtaining an obstacle model of the photovoltaic installation area.

[0069] Step S106, estimating the shadow state of the photovoltaic installation area in the future period according to the illumination variation trend and the obstacle model.

[0070] This method can combine the lighting change trend and the obstacle model to accurately predict the shadow state in the future period, thereby improving the accuracy of shadow prediction.

[0071] In some optional embodiments, the obstacle model generally includes a plant growth model; the step of evaluating the shadow state of the photovoltaic installation area in the future period according to the illumination change trend and the obstacle model may generally include: determining the target time point; predicting the growth state of the natural obstacles in the photovoltaic installation area at the target time point according to the plant growth model; determining the illumination state at the target time point according to the illumination change trend; estimating the shadow state of the natural obstacles at the target time point according to the illumination state and the growth state of the natural obstacles. Among them, the plant growth model is used to consider the influence of seasonal changes, moisture, temperature and other factors on plant growth, and according to the plant species, age, growth rate and historical climate data, the growth height, branch and leaf density and other characteristics of the natural obstacles at the target time point can be predicted, so as to estimate the vegetation morphology at that time. Then, based on the illumination simulation model and the future meteorological forecast data obtained, the illumination intensity, angle and duration at the target time point are estimated. Combined with the predicted growth state of the natural obstacles and the illumination conditions at the target time point, the size, position and change of the shadow cast by the natural obstacles at that time point can be analyzed and estimated. This estimation helps to identify which photovoltaic panels may be blocked and the degree of the blockage, providing a basis for photovoltaic planning.

[0072] In some other optional embodiments, the obstacle model generally includes a building change model; the step of evaluating the shadow state of the photovoltaic installation area in the future period according to the illumination change trend and the obstacle model generally may include: determining the target time point; predicting the architectural form of the architectural obstacles in the photovoltaic installation area at the target time point according to the building change model; determining the illumination state at the target time point according to the illumination change trend; estimating the shadow state of the architectural obstacles at the target time point according to the illumination state and the architectural form of the architectural obstacles. First, the building change model is used, which generally needs to consider multiple factors such as planning permits, construction project progress, and expected growth in building height and width in the region. By integrating existing building data with future construction planning information, the specific form of the buildings around the photovoltaic installation area at the target time point is predicted, including their height, position changes, and possible new structures. Next, the illumination simulation model and the future weather forecast data obtained are used to determine the illumination intensity, angle, and duration at the target time point, so as to accurately estimate the illumination environment at the time point, laying the foundation for subsequent shadow analysis. Finally, combined with the prediction results of the architectural form and the analysis of illumination conditions, the range and intensity of the shadows that may be generated by the architectural obstacles at the target time point can be estimated.

[0073] Optionally, after the step of estimating the shadow state of the photovoltaic installation area in the future time period based on the lighting change trend and the obstacle model, the following steps may generally be included: obtaining the photovoltaic module installation plan of the photovoltaic installation area; determining the position and posture of the target photovoltaic modules affected by the shadow at the same time point based on the photovoltaic module installation plan and the shadow state; performing power generation loss assessment and hot spot impact degree assessment at the corresponding time point based on the shadow state and the position and posture of the target photovoltaic modules; determining a comprehensive impact degree score based on the power generation loss assessment and the hot spot impact degree assessment; and simulating adjustment of the posture of the target photovoltaic modules to reduce the comprehensive impact degree score.

[0074] Optionally, after the step of simulating the adjustment of the posture of the target photovoltaic component, the step may generally further include: determining whether the comprehensive impact score exceeds a preset threshold; if so, dividing the area corresponding to the shadow whose comprehensive impact score exceeds the preset threshold as an area where photovoltaic components are not allowed to be arranged.

[0075] Optionally, after the step of determining whether the comprehensive impact score exceeds a preset threshold, the following step may generally be included: if the adjusted comprehensive impact score does not exceed the preset threshold, optimizing the photovoltaic component installation plan according to the simulation adjustment result of the target photovoltaic component.

[0076] Figure 2 FIG. 1 is a flow chart of a shadow estimation method for a photovoltaic power generation system according to an embodiment of the present invention. The shadow estimation method for a photovoltaic power generation system may generally include:

[0077] Step S201, determining a target time point.

[0078] Step S202, predicting the growth status of natural obstacles in the photovoltaic installation area at the target time point according to the plant growth model. The plant growth model is usually based on biological principles and past observation data, taking into account a variety of factors, including plant species, current size, growth cycle, soil conditions, average temperature, precipitation and other environmental factors. The model can simulate the dynamic process of plant growth through mathematical formulas or algorithms. Then, the target time point to be predicted is clarified, which may be based on seasonal maintenance plans, long-term energy output forecasts or other strategic planning needs. Then, the current growth status of natural obstacles (such as height, crown diameter) and the expected environmental conditions during the prediction period (such as expected temperature fluctuations, rainfall forecasts) are input into the model. The plant growth model will calculate and predict the expected growth status of natural obstacles at the target time point based on the input information, including but not limited to height, branch and leaf density, crown shape and size, etc.

[0079] Step S203, determining the illumination state at the target time point according to the illumination variation trend.

[0080] Step S204, estimating the shadow state caused by the natural obstacle at the target time point according to the illumination state and the growth state of the natural obstacle.

[0081] To illustrate the above steps clearly, we take willow as an example. First, we collect the growth data of willow, including the average height, diameter at breast height, crown diameter and other information of willows of different ages. These data can be obtained from forestry research institutions, past literature or field measurements. Then, we record the environmental factors that affect the growth of willow, such as the average annual temperature, precipitation, soil type, etc. These can be obtained from meteorological stations and soil databases. Based on the collected data, we select a suitable growth model, such as the Logistic model or the Chapman-Richards growth model, which can better describe the changes in the growth of organisms over time. Then, we use statistical software to estimate the model parameters, and fit the model by the least squares method or other optimization methods to ensure that the model can accurately reflect the growth trend of willow. Then, we use the selected and parameterized willow plant growth model to input the virtual age or growth cycle stage at the target time point to predict the expected height, crown width and other growth states of the willow at that time point. Finally, professional software (such as SketchUp's Shadow Analysis plug-in, PVSYST, etc.) is used to simulate the angle of sunlight at the target time point, calculate the shadow area created by the willow tree, and evaluate its specific shading effect on the photovoltaic panel, that is, the shadow state.

[0082] This method can be used to scientifically predict and evaluate the potential shadowing effect of natural obstacles on photovoltaic devices at a certain point in the future, thereby guiding actual operational management decisions.

[0083] Figure 3 1 is a flow chart of estimating the shadow state corresponding to a building obstacle in a shadow estimation method for a photovoltaic power generation system according to an embodiment of the present invention. The method for estimating the shadow state corresponding to a building obstacle may generally include:

[0084] Step S301, determining a target time point.

[0085] Step S302, predicting the architectural form of the building obstacles in the photovoltaic installation area at the target time point according to the building change model. Using the architectural form predicted by the building change model, the height, width, position and any protruding structures (such as balconies and cornices) of each building obstacle at the target time point can be understood in detail, reflecting the actual outline of the building.

[0086] Step S303, determine the illumination state at the target time point according to the illumination change trend. The illumination state generally includes the sun altitude angle, azimuth angle, and illumination intensity, etc. With this information, an accurate illumination model can be constructed to understand how the light hits the surface at that time point.

[0087] Step S304, estimating the shadow state of the building obstacle at the target time point according to the illumination state and the architectural form of the building obstacle. Through simulation calculation, the shadow area of ​​each building obstacle at the target time point can be obtained, including the length and width of the shadow and its specific distribution in the photovoltaic installation area.

[0088] Through this method, the shadow effect caused by building obstacles at a specified time can be scientifically predicted, providing data support for optimizing the layout design of photovoltaic systems and improving energy output.

[0089] Figure 4 1 is a flow chart of evaluating the degree of influence caused by shadows in a shadow estimation method for a photovoltaic power generation system according to an embodiment of the present invention. The method for evaluating the degree of influence caused by shadows may generally include:

[0090] Step S401, obtaining a photovoltaic module installation plan for a photovoltaic installation area, wherein the photovoltaic module installation plan generally includes a predetermined position, orientation angle (inclination and azimuth), and arrangement layout of each photovoltaic module.

[0091] Step S402, determine the position and posture of the target photovoltaic components affected by the shadow at the same time point according to the photovoltaic component installation plan and the shadow state. In step S402, it is first necessary to superimpose the estimated shadow state layer with the layout plan of the photovoltaic components. This intuitively shows which photovoltaic components or parts of components will be obscured by the shadows of buildings or natural obstacles at the target time point. Then, for each photovoltaic component determined to be affected by the shadow, further analyze its exact posture in the shadow state. This includes checking whether the component is in a non-ideal orientation due to the surrounding environment or design reasons, and evaluating whether this posture exacerbates the impact of shadow occlusion.

[0092] Step S403, based on the shadow state and the position and posture of the target photovoltaic component, the power generation loss assessment and the hot spot impact assessment at the corresponding time point are performed. For the simulated power generation loss assessment, factors such as the photovoltaic conversion efficiency of the component, the sunshine intensity, and the reduction in effective illumination time caused by shading need to be considered to calculate the difference in power generation of each shaded component at that time point relative to the unshaded state. By summarizing these data, the total power generation loss of the entire photovoltaic system can be obtained. For the hot spot impact assessment, the possibility and severity of hot spots are identified by analyzing the thermal images of the components affected by the shadows or simulating the heat distribution. The component material properties, heat dissipation conditions, and shading patterns need to be considered in the assessment, because continuous local overheating not only affects the immediate power generation efficiency, but also damages the long-term performance of the components.

[0093] Step S404, determining a comprehensive impact score based on the power generation loss assessment and the hot spot impact assessment. When planning the layout of photovoltaic modules, the comprehensive impact score of the photovoltaic modules exceeds the preset safety or efficiency threshold within a specific time period. This "comprehensive impact score" is a comprehensive indicator, which may cover power generation efficiency loss, hot spot effect risk and other adverse factors, and is used to measure the comprehensive impact of a certain area on the performance of photovoltaic modules. Those skilled in the art can set the scoring criteria according to actual conditions. Clarify the factors included in the comprehensive impact score, such as the degree of power generation loss, hot spot effect risk, and maintenance cost, set weights for each factor, and form a comprehensive evaluation system.

[0094] Step S405, simulating the adjustment of the posture of the target photovoltaic module to reduce the comprehensive impact score.

[0095] Based on the existing shadow analysis results, perform preliminary simulations of attitude adjustments for the components that are most severely shaded or have the highest risk of hot spots. The purpose of the adjustment is to maximize the sunshine time of the components while minimizing the possibility of mutual shading and hot spot formation. Then, for each set of adjusted attitude parameters, rerun the evaluation of power generation loss and hot spot impact. Compare the comprehensive impact scores before and after the adjustment, and record the attitude configuration with improved scores.

[0096] Step S406, determining whether the comprehensive impact score exceeds a preset threshold.

[0097] Step S407, if the judgment in step S406 is yes, the area corresponding to the shadow whose comprehensive impact score exceeds the preset threshold is divided into an area where photovoltaic modules are not allowed to be arranged. In order to ensure the efficient and stable operation of the entire photovoltaic power station, through the previous simulation and evaluation process, those areas with too high comprehensive impact scores in a specific time period are identified. This means that these areas are considered unsuitable for installing photovoltaic modules after considering various factors, because they may cause a significant reduction in energy output or increase maintenance costs. Once these high-impact areas are identified, they are clearly marked as "areas where photovoltaic modules are not allowed to be arranged", so as to ensure that the subsequent installation team can clearly know which locations should be avoided during the planning process, so as to avoid possible inefficiency or maintenance problems in the future. After excluding these restricted areas, the layout of photovoltaic modules in the remaining space is replanned and optimized, striving to achieve the maximum power generation efficiency and economic benefits under the existing conditions. Through such a process, long-term operational problems caused by improper layout can be effectively avoided, and the return on investment and sustainability of photovoltaic projects can be ensured.

[0098] Step S408: If the result of step S406 is negative, optimize the photovoltaic module installation plan according to the simulation adjustment results of the target photovoltaic module. Based on the above analysis results, re-plan the layout of modules in certain areas to ensure that each module can achieve the best performance under the adjusted posture. At the same time, it is necessary to ensure that such layout changes will not introduce new shading problems or violate safety regulations.

[0099] Through this method, the optimization of PV panel installation planning can be ensured based on the estimated shadows, which not only improves energy output but also enhances the stability and economy of the PV system.

[0100] The flow chart provided by the present embodiment is not intended to indicate that the operation of the method will be performed in any particular order, or that all operations of the method are included in all every case. In addition, the method may include additional operations. Within the scope of the technical thinking provided by the present embodiment method, additional changes may be made to the above method.

[0101] It should be understood that in some embodiments, each part can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system.

[0102] This embodiment also provides a computer program product 112 , a computer readable storage medium 113 , and a computer device 114 . Figure 5 is a schematic diagram of a computer program product 112 according to one embodiment of the present invention, Figure 6 is a schematic diagram of a computer-readable storage medium 113 according to an embodiment of the present invention, Figure 7 is a schematic block diagram of a computer device 114 according to one embodiment of the present invention.

[0103] The computer program product 112 includes a computer program 111, which implements the steps of any of the above-mentioned shadow estimation methods for photovoltaic power generation systems when executed by the processor 310. The computer readable storage medium 113 stores the above-mentioned computer program 111, which implements the steps of any of the above-mentioned shadow estimation methods for photovoltaic power generation systems when executed by the processor 310. The computer device 114 may include a memory 320, a processor 310, and a computer program 111 stored in the memory 320 and running on the processor 310.

[0104] The computer program 111 used to perform the operations of the present invention can be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, configuration data of an integrated circuit, or source code or object code written in any combination of one or more programming languages ​​and procedural programming languages.

[0105] The computer program 111 may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, in order to perform various aspects of the present invention, electronic circuits including, for example, programmable logic circuits, field programmable gate arrays (FPGAs), or programmable logic arrays (PLAs) may execute computer-readable program instructions by utilizing state information of the computer-readable program instructions to personalize the electronic circuits.

[0106] In the description of this embodiment, the computer program product 112 is a related product including the computer program 111 .

[0107] For the purposes of the description of this embodiment, the computer-readable storage medium 113 is a tangible device capable of retaining and storing the computer program 111, which may be any device that can contain, store, communicate, propagate or transmit the program 111 for use with or in conjunction with an instruction execution system, device or apparatus. More specific examples (a non-exhaustive list) of the computer-readable storage medium 113 include the following: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, and any suitable combination of the foregoing.

[0108] At this point, those skilled in the art should recognize that, although multiple exemplary embodiments of the present invention have been shown and described in detail herein, many other variations or modifications that conform to the principles of the present invention can still be directly determined or derived based on the content disclosed in the present invention without departing from the spirit and scope of the present invention. Therefore, the scope of the present invention should be understood and recognized as covering all these other variations or modifications.

Claims

1. A shadow estimation method for a photovoltaic power generation system, comprising: Determine a photovoltaic installation area where the photovoltaic power generation system is located; Collecting historical climate data for the photovoltaic installation area; Establishing a lighting simulation model for the photovoltaic installation area according to the historical climate data; Predicting the illumination variation trend of the photovoltaic installation area in the future period according to the illumination simulation model; Obtaining an obstacle model of the photovoltaic installation area; The shadow state of the photovoltaic installation area in the future period is estimated according to the illumination variation trend and the obstacle model.

2. The shadow estimation method for a photovoltaic power generation system according to claim 1, wherein: The obstacle model includes a plant growth model; The step of evaluating the shadow state of the photovoltaic installation area in the future period according to the illumination variation trend and the obstacle model comprises: Determine the target time point; Predicting the growth state of natural obstacles in the photovoltaic installation area at the target time point according to the plant growth model; Determining the illumination state at the target time point according to the illumination change trend; The shadow state generated by the natural obstacle at the target time point is estimated according to the illumination state and the growth state of the natural obstacle.

3. The shadow estimation method for a photovoltaic power generation system according to claim 1, wherein: The obstacle model includes a building change model; The step of evaluating the shadow state of the photovoltaic installation area in the future period according to the illumination variation trend and the obstacle model comprises: Determine the target time point; Predicting the architectural form of the architectural obstacles in the photovoltaic installation area at the target time point according to the building change model; Determining the illumination state at the target time point according to the illumination change trend; The shadow state generated by the building obstacle at the target time point is estimated according to the illumination state and the building form of the building obstacle.

4. The shadow estimation method for a photovoltaic power generation system according to claim 1, wherein: After the step of estimating the shadow state of the photovoltaic installation area in the future period according to the illumination variation trend and the obstacle model, the method further includes: Obtaining a photovoltaic module installation plan for the photovoltaic installation area; Determine the position and posture of the target photovoltaic component affected by the shadow at the same time point according to the photovoltaic component installation plan and the shadow state; Performing power generation loss assessment and hot spot impact assessment at a corresponding time point according to the shadow state and the position and posture of the target photovoltaic assembly; Determining a comprehensive impact score based on the power generation loss assessment and the hot spot impact assessment; The posture of the target photovoltaic component is adjusted in a simulated manner to reduce the comprehensive impact score.

5. The shadow estimation method for a photovoltaic power generation system according to claim 4, wherein: After the step of simulating and adjusting the posture of the target photovoltaic component, the step further includes: Determining whether the comprehensive impact score exceeds a preset threshold; If yes, the area corresponding to the shadow whose comprehensive impact score exceeds the preset threshold is divided into an area where photovoltaic components are not allowed to be arranged.

6. The shadow estimation method of the photovoltaic power generation system according to claim 5, wherein: After the step of determining whether the comprehensive impact score exceeds a preset threshold, the following step further includes: If the adjusted comprehensive impact score does not exceed the preset threshold, the photovoltaic component installation plan is optimized according to the simulation adjustment result of the target photovoltaic component.

7. The shadow estimation method for a photovoltaic power generation system according to claim 1, wherein: The historical climate data includes historical light data and historical meteorological data; The step of establishing the illumination simulation model of the photovoltaic installation area according to the historical climate data comprises: Extracting illumination characteristics of the photovoltaic installation area according to the historical illumination data; Extracting weather characteristics of the photovoltaic installation area according to the historical meteorological data; The illumination characteristics and the weather characteristics are combined and input into a preset training model for training to obtain the illumination simulation model.

8. The shadow estimation method for a photovoltaic power generation system according to claim 1, wherein: The step of predicting the illumination change trend of the photovoltaic installation area in the future period according to the illumination simulation model comprises: Obtaining weather forecast data for the future period; The weather forecast data and the illumination simulation model are used to predict the illumination change trend in the future period.

9. A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of the shadow estimation method for a photovoltaic power generation system according to any one of claims 1 to 8 are implemented.

10. A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the steps of the shadow estimation method for a photovoltaic power generation system according to any one of claims 1 to 8 are implemented.

11. A computer device, comprising a memory, a processor, and a machine executable program stored in the memory and running on the processor, wherein the processor implements the steps of the shadow estimation method for a photovoltaic power generation system according to any one of claims 1 to 8 when executing the machine executable program.

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