Method, device, equipment, medium and program product for determining the inclination angle of a photovoltaic module

Abstract

The present application relates to photovoltaic power generation technical field, disclose a photovoltaal module inclination determination method, device, equipment, medium and program product, the photovoltaal module inclination determination method includes: using aerosol optical thickness, cloud coverage, solar radiation, determine the first target solar radiation; using original electricity price acquisition data and power grid load data, determine the first target electricity price;According to the first target solar radiation, the first geometric relationship, the photoelectric conversion efficiency of photovoltaal module and each first dust coverage degree factor, determine the first target power generation;With the maximum power generation income as the goal, according to the first target electricity price, the first target power generation, the first power transmission loss factor, the environmental influence degree factor and the short-term power market fluctuation index factor, determine the first target inclination, the present application determines the short-term photovoltaal module inclination by combining various influence factors, improves the accuracy of photovoltaal module inclination, thereby improves the power generation efficiency.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic power generation technology, specifically to methods, apparatus, equipment, media, and procedures for determining the tilt angle of photovoltaic modules. Background Technology

[0002] Photovoltaic power generation has been widely used globally due to its clean and environmentally friendly characteristics, abundant resources, and wide distribution. In the photovoltaic power generation process, photovoltaic modules achieve photoelectric conversion by absorbing solar radiation (especially direct sunlight). The angle of incidence between sunlight and the surface of the photovoltaic module, i.e., the tilt angle of the photovoltaic module, directly determines the absorption efficiency. Therefore, it is necessary to determine the accurate tilt angle of the photovoltaic module to improve the efficiency of photovoltaic power generation.

[0003] In related technologies, the method for determining the tilt angle of photovoltaic modules involves using a power maximization model. This model relies on complex astronomical, meteorological, and photovoltaic module performance parameters, employing precise mathematical calculations and simulations to attempt to find the tilt angle that maximizes the power generation of the photovoltaic modules within a year, defining this as the optimal tilt angle. However, the operational rules of the electricity market and the complexity of grid dispatch mean that simply pursuing maximum power generation does not directly equate to maximizing the economic benefits of power generation. Furthermore, it ignores the dynamic changes in electricity prices. Therefore, this method wastes power generation resources, resulting in low power generation efficiency and ultimately low overall economic benefits. Summary of the Invention

[0004] In view of this, the present invention provides a method, apparatus, device, medium and program product for determining the tilt angle of a photovoltaic module, so as to solve the problem of low power generation efficiency caused by the method for determining the tilt angle of a photovoltaic module in the related art.

[0005] In a first aspect, the present invention provides a method for determining the tilt angle of a photovoltaic module, comprising: correcting the solar radiation at multiple first target moments within a first preset time period using aerosol optical thickness and cloud cover rate to obtain a first target solar radiation at each first target moment within the first preset time period; smoothing the electricity price at multiple first target moments within the first preset time period using raw electricity price data and grid load data to obtain a first target electricity price at each first target moment within the first preset time period; and determining the first target solar radiation at each first target moment within the first preset time period, the tilt angle variable of the photovoltaic module, and the angle between sunlight and the horizontal plane at each first target moment. Based on the first geometric relationship, the photoelectric conversion efficiency of the photovoltaic module, and the first dust coverage factor of the photovoltaic module at each first target time, the first target power generation at each first target time under the tilt angle variable is determined. With the goal of maximizing power generation revenue, the first target tilt angle of the first preset time period is determined based on the first target electricity price at each first target time in the first preset time period, the first target power generation at each first target time in the first preset time period under the tilt angle variable, the first power transmission loss factor at each first target time in the first preset time period, the environmental impact factor, and the short-term power market volatility index factor, so as to carry out photovoltaic power generation according to the first target tilt angle.

[0006] This invention uses aerosol optical thickness and cloud cover to correct the solar radiation at multiple target moments within a first preset time period, obtaining the first target solar radiation at each target moment within the first preset time period. By considering the influence of aerosol optical thickness and cloud cover on solar radiation, the obtained first target solar radiation tends to the actual value, making the subsequent estimation of the first target power generation more accurate. This invention also uses raw electricity price data and grid load data to smooth the electricity prices at multiple target moments within the first preset time period, obtaining the first target electricity price at each target moment within the first preset time period. This price smoothing process makes the smoothed first target electricity price more reflective of the actual market supply and demand relationship, reducing price fluctuations and providing a stable and reliable price benchmark for subsequent photovoltaic module tilt angle optimization, significantly improving the accuracy and reliability of power generation revenue calculation. This invention determines the first target power generation at each first target moment within the first preset time period based on the first target solar radiation at each first target moment in the first preset time period, the first geometric relationship between the tilt angle variable of the photovoltaic module and the angle between the sunlight and the horizontal plane at each first target moment, the photoelectric conversion efficiency of the photovoltaic module, and the first dust coverage factor of the photovoltaic module at each first target moment. This invention considers the influence of the first target solar radiation at each first target moment in the first preset time period, the first geometric relationship between the tilt angle variable of the photovoltaic module and the angle between the sunlight and the horizontal plane at each first target moment, the photoelectric conversion efficiency of the photovoltaic module, and the first dust coverage factor of the photovoltaic module at each first target moment on the first target power generation, thus obtaining a more accurate first target power generation. This invention aims to maximize power generation revenue. Based on the first target electricity price at each first target moment within a first preset time period, the first target power generation at each first target moment within the first preset time period under the tilt angle variable, the first power transmission loss factor at each first target moment within the first preset time period, the environmental impact factor, and the short-term electricity market volatility index factor, a first target tilt angle is determined for the first preset time period. Photovoltaic power generation is then conducted according to this first target tilt angle. This invention, with its goal of maximizing power generation revenue, fully considers the complex relationship between photovoltaic module tilt angle and power generation revenue, which is influenced by multiple factors. This makes the first target tilt angle more accurate and more consistent with actual conditions, enabling real-time determination and adjustment of the short-term photovoltaic module tilt angle, thereby improving photovoltaic power generation efficiency and increasing photovoltaic power generation revenue.

[0007] In an optional embodiment, the method for determining the tilt angle of the photovoltaic module further includes: correcting the solar radiation at multiple second target moments each day within a second preset time period using aerosol optical thickness and cloud coverage to obtain the second target solar radiation at each second target moment each day within the second preset time period; the duration of the second preset time period is longer than the duration of the first preset time period; smoothing the electricity price at multiple second target moments each day within the second preset time period using original electricity price data and grid load data to obtain the second target electricity price at each second target moment each day within the second preset time period; and determining the second target solar radiation at each second target moment each day within the second preset time period, the second geometric relationship between the tilt angle variable of the photovoltaic module and the angle between the sunlight and the horizontal plane at each second target moment, and the light intensity of the photovoltaic module. The second target power generation at each second target moment of each day under the tilt angle variable is determined by considering the power conversion efficiency, the second dust coverage factor of the photovoltaic module at each second target moment of each day, and the ground albedo at each second target moment of each day. With the goal of maximizing power generation revenue, the second target tilt angle of the second preset time period is determined by considering the second target electricity price at each second target moment of each day under the tilt angle variable, the second target power generation at each second target moment of each day under the tilt angle variable, the second power transmission loss factor at each second target moment of each day under the second preset time period, the daily electricity consumption pattern factor, the daily power generation revenue change factor, and the daily seasonal adjustment factor under the second preset time period. Photovoltaic power generation is then carried out according to the second target tilt angle.

[0008] This invention aims to determine the optimal tilt angle of photovoltaic modules by maximizing electricity price revenue at different time scales. It delves into the inherent patterns of electricity price fluctuations across different time scales, comprehensively collecting relevant data for each time scale and employing professional data analysis methods. This invention provides a suitable method for determining the optimal tilt angle for each time scale, flexibly selecting the most appropriate time scale and its corresponding optimal tilt angle. This effectively avoids revenue reduction issues caused by power curtailment or electricity price fluctuations, significantly improving the economic benefits of photovoltaic power generation.

[0009] In one optional implementation, the solar radiation at multiple first target moments within a first preset time period is corrected using aerosol optical thickness and cloud coverage to obtain the first target solar radiation at each first target moment within the first preset time period. This includes: using a preset exponential function, performing a weighted average of the aerosol optical thickness and cloud coverage within a first preset time window using integral calculation, and correcting the solar radiation at multiple first target moments within the first preset time period to obtain the first target solar radiation at each first target moment within the first preset time period.

[0010] In one optional implementation, the electricity prices at multiple first target times within a first preset time period are smoothed using the original electricity price data and grid load data to obtain the first target electricity price at each first target time within the first preset time period. This includes: using a preset Gaussian function to perform a weighted average of the original electricity price data and grid load data for a second preset time window, and smoothing the electricity prices at multiple first target times within the first preset time period to obtain the first target electricity price at each first target time within the first preset time period.

[0011] In one optional implementation, the first target power generation at each first target moment within the first preset time period is determined based on the first target solar radiation at each first target moment within the first preset time period, the first geometric relationship between the tilt angle variable of the photovoltaic module and the angle between the sunlight and the horizontal plane at each first target moment, the photoelectric conversion efficiency of the photovoltaic module, and the first dust coverage factor of the photovoltaic module at each first target moment. This includes: acquiring dust coverage data at each first target moment within the first preset time period; using a dust influence coefficient to attenuate and correct the dust coverage data to obtain a first dust coverage factor; and obtaining the first target power generation at each first target moment within the first preset time period based on the product of the first target solar radiation at each first target moment within the first preset time period, the first geometric relationship between the tilt angle variable of the photovoltaic module and the angle between the sunlight and the horizontal plane at each first target moment, the photoelectric conversion efficiency of the photovoltaic module, and the first dust coverage factor of the photovoltaic module at each first target moment.

[0012] In one optional implementation, with the goal of maximizing power generation revenue, the first target tilt angle for the first preset time period is determined based on the first target electricity price at each first target moment in the first preset time period, the first target power generation at each first target moment in the first preset time period under the tilt angle variable, the first power transmission loss factor at each first target moment in the first preset time period, the environmental impact factor, and the short-term power market volatility index factor. This includes: weighting the temperature, humidity, and wind speed of a third preset time window and normalizing the weighted average result to obtain the environmental impact factor; determining the short-term power market volatility index factor based on the short-term power market volatility index and a preset correlation coefficient; summing the products of the first target electricity price at each first target moment in the first preset time period, the first target power generation at each first target moment in the first preset time period under the tilt angle variable, the first power transmission loss factor, the environmental impact factor, and the short-term power market volatility index factor at each first target moment in the first preset time period to obtain the first power generation revenue under multiple tilt angle variables; and determining the first target tilt angle for the first preset time period with the goal of maximizing the first power generation revenue.

[0013] In one optional implementation, with the goal of maximizing power generation revenue, the second target tilt angle for the second preset time period is determined based on the second target electricity price at each second target time of each day in the second preset time period, the second target power generation at each second target time of each day in the second preset time period under the tilt angle variable, the second power transmission loss factor at each second target time of each day in the second preset time period, the electricity consumption pattern factor, the power generation revenue change factor, and the seasonal adjustment factor for each day in the second preset time period. This includes summing the products of the second target electricity price at each second target time of each day in the second preset time period, the second target power generation at each second target time of each day in the second preset time period under the tilt angle variable, the second power transmission loss factor, the electricity consumption pattern factor, the power generation revenue change factor, and the seasonal adjustment factor for each day in the second preset time period to obtain the second power generation revenue under multiple tilt angle variables; and determining the second target tilt angle for the second preset time period with the goal of maximizing the second power generation revenue.

[0014] Secondly, the present invention provides a device for determining the tilt angle of a photovoltaic module, comprising: a radiation correction module, used to correct the solar radiation at multiple first target moments within a first preset time period using aerosol optical thickness and cloud cover, to obtain a first target solar radiation at each first target moment within the first preset time period; an electricity price processing module, used to smooth the electricity price at multiple first target moments within the first preset time period using original electricity price data and grid load data, to obtain a first target electricity price at each first target moment within the first preset time period; and a power generation determination module, used to determine the power generation based on the first target solar radiation at each first target moment within the first preset time period, the tilt angle variable of the photovoltaic module, and the solar radiation at each first target moment. The first geometric relationship between the angle between the light source and the horizontal plane, the photoelectric conversion efficiency of the photovoltaic module, and the first dust coverage factor of the photovoltaic module at each first target moment are used to determine the first target power generation at each first target moment in the first preset time period under the tilt angle variable. The tilt angle determination module is used to determine the first target tilt angle of the first preset time period with the goal of maximizing power generation revenue, based on the first target electricity price at each first target moment in the first preset time period, the first target power generation at each first target moment in the first preset time period under the tilt angle variable, the first power transmission loss factor at each first target moment in the first preset time period, the environmental impact factor, and the short-term power market volatility index factor, so as to carry out photovoltaic power generation according to the first target tilt angle.

[0015] Thirdly, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the method for determining the tilt angle of a photovoltaic module as described in the first aspect or any corresponding embodiment thereof.

[0016] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the method for determining the tilt angle of a photovoltaic module according to the first aspect or any corresponding embodiment thereof.

[0017] Fifthly, the present invention provides a computer program product, including computer instructions for causing a computer to execute the method for determining the tilt angle of a photovoltaic module as described in the first aspect or any corresponding embodiment thereof. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the specific embodiments or related technologies of the present invention, the drawings used in the description of the specific embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating a method for determining the tilt angle of a photovoltaic module according to an embodiment of the present invention.

[0020] Figure 2 This is a schematic diagram of a photovoltaic module device according to an embodiment of the present invention.

[0021] Figure 3 This is a flowchart illustrating another method for determining the tilt angle of a photovoltaic module according to an embodiment of the present invention.

[0022] Figure 4 This is a flowchart illustrating a photovoltaic module tilt angle determination system according to an embodiment of the present invention.

[0023] Figure 5 This is a flowchart illustrating another method for determining the tilt angle of a photovoltaic module according to an embodiment of the present invention.

[0024] Figure 6 This is a flowchart illustrating another method for determining the tilt angle of a photovoltaic module according to an embodiment of the present invention.

[0025] Figure 7 This is a structural block diagram of a photovoltaic module tilt angle determination device according to an embodiment of the present invention.

[0026] Figure 8This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] Photovoltaic power generation, as a green and renewable energy source, is gradually occupying a key position in the energy landscape. With continuous innovation in photovoltaic technology and gradual reduction in costs, more and more photovoltaic power plants are being put into use.

[0029] Currently, most photovoltaic (PV) power plants widely employ the power maximization model when determining the tilt angle of PV modules. This model relies on complex astronomical, meteorological, and PV module performance parameters. Through precise mathematical calculations and simulations, it attempts to find the tilt angle that maximizes the power generation of the PV modules over a year, defining this as the optimal tilt angle. This method fully considers the sun's changing position in different seasons and at different times, as well as the optical and electrical characteristics of the PV modules themselves, aiming to allow the PV modules to capture as much solar radiation energy as possible and efficiently convert it into electricity. Under some ideal conditions, using the power maximization model can indeed significantly increase the total amount of PV power generation.

[0030] However, in actual photovoltaic power plant operation, the tilt angle design method based on maximizing power generation has some problems. The most prominent issue is that the operational patterns of the electricity market and the complexity of grid dispatch mean that simply pursuing maximum power generation does not directly equate to maximizing the economic benefits of power generation. For example, during midday, sunlight is usually abundant, and the power generation efficiency of photovoltaic modules reaches its peak. According to the tilt angle designed based on the power maximization model, a large amount of electricity should be generated. However, due to various factors such as the overall load balancing requirements of the power grid and power dispatch strategies, power curtailment frequently occurs during midday. This results in photovoltaic power plants, even with high-efficiency power generation capabilities, being unable to transmit the generated electricity to the grid for trading in a timely manner, leading to a serious waste of power generation resources.

[0031] Furthermore, traditional power maximization models completely ignore the dynamic nature of electricity prices. In the actual electricity market, electricity prices are not constant but are significantly volatile due to the combined effects of various complex factors, such as peak and off-peak electricity demand, fluctuations in generation costs, adjustments to energy policies, and market competition. Electricity prices can vary drastically across different time periods; for example, prices are often higher during peak industrial electricity consumption periods and relatively lower during off-peak periods such as late at night. However, power maximization models do not consider the impact of these price fluctuations on power generation revenue when determining the tilt angle of photovoltaic modules. This means that photovoltaic power plants may generate a large amount of electricity during periods of low electricity prices, while failing to generate sufficient power during periods of high electricity prices due to various factors, ultimately leading to a decrease in the overall economic efficiency of power generation.

[0032] Finally, tilt angle design methods based on maximizing power generation have serious shortcomings in terms of time scale adaptability. Different photovoltaic power plant operation scenarios and market demands have diverse requirements for the time scale of power generation planning. For example, for long-term power plant investment and planning, it is necessary to consider the economic benefits over a span of 25 years or even longer to assess the return on investment of the power plant throughout its entire life cycle; while for short-term electricity market trading strategy formulation, more attention may be paid to the power generation revenue of a week, a day, or even an hour, so as to adjust the power generation strategy in a timely manner to adapt to market changes. The traditional power maximization model determines the tilt angle with only a fixed time scale of one year, which cannot meet these diverse time scale requirements and limits the further improvement of the flexibility and economic benefits of photovoltaic power plant operation.

[0033] This invention provides a method for determining the tilt angle of a photovoltaic module. By combining multiple influencing factors, the short-term tilt angle of the photovoltaic module is determined, thereby improving the accuracy of the tilt angle and increasing power generation efficiency.

[0034] According to an embodiment of the present invention, a method for determining the tilt angle of a photovoltaic module is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0035] This embodiment provides a method for determining the tilt angle of a photovoltaic module, which can be used in computer equipment. Figure 1 This is a flowchart of a method for determining the tilt angle of a photovoltaic module according to an embodiment of the present invention, as shown below. Figure 1 As shown, the process includes the following steps:

[0036] Step S101: The solar radiation at multiple first target moments in the first preset time period is corrected using aerosol optical thickness and cloud coverage to obtain the first target solar radiation at each first target moment in the first preset time period.

[0037] Among them, aerosol optical thickness is used to reflect the degree of attenuation of solar radiation by aerosols in the atmosphere. In this embodiment of the invention, an advanced aerosol monitoring instrument is used to measure aerosol optical thickness, and its measurement accuracy can reach 0.01. Cloud coverage rate refers to the ratio of the area occupied by clouds to the total area of ​​a certain space, usually expressed as a percentage. Cloud coverage rate is used to reflect the degree of cloud cover in a specific area of ​​the sky. In this embodiment of the invention, cloud coverage rate is obtained by fusing satellite cloud image data with data from ground cloud monitoring equipment, and the value range is [0, 1]. Clouds have a strong reflection and scattering effect on solar radiation and are one of the important factors affecting the arrival of solar radiation on the ground. Solar radiation refers to the energy transmitted outward by the sun in the form of electromagnetic waves, and the solar radiation energy projected onto a certain area per unit time. In this embodiment of the invention, a high-sensitivity solar radiometer is equipped around the photovoltaic power station. The solar radiometer has a high-precision measurement capability at the minute level, and the measurement range covers (Watts per square meter) can sensitively capture subtle changes in solar radiation at different times. As the energy source for photovoltaic power generation, accurate measurement of solar radiation is crucial for the accurate calculation of subsequent power generation.

[0038] In some optional implementations, the first preset time period is a short time period, which can be set according to the actual situation. For example, the first preset time period can be 1 hour, 48 hours, or any time period between 1 hour and 48 hours.

[0039] In some optional implementations, the solar radiation at multiple first target moments within a first preset time period is corrected using aerosol optical thickness and cloud coverage to obtain the first target solar radiation at each first target moment within the first preset time period. This includes: using a preset exponential function, performing a weighted average of the aerosol optical thickness and cloud coverage within a first preset time window using integral calculation to correct the solar radiation at multiple first target moments within the first preset time period, thereby obtaining the first target solar radiation at each first target moment within the first preset time period.

[0040] In some optional implementations, solar radiation is affected by various complex factors such as atmospheric composition and cloud distribution during its journey through the atmosphere, leading to a deviation between the actual radiation reaching the ground and being received by the photovoltaic modules and the original measurement. Therefore, fine-tuning the solar radiation is a crucial step. For example, the formula for determining the first target solar radiation is:

[0041]

[0042] in, The first target time within the first preset time period The primary objective is solar radiation. The first target time within the first preset time period The amount of solar radiation. For the first preset time window [ , The first time duration can be set to 45 minutes. Based on in-depth statistical analysis of local meteorological data over many years and correlation studies of real-time power generation data from photovoltaic power plants, this method effectively covers the main time range of the impact of atmospheric changes on solar radiation, ensuring that the corrected solar radiation amount is closer to the actual situation. The preset exponential function determines the degree of influence of data at different times within the time window on the solar radiation of the first target. This is the integration variable, used to iterate through each moment within the time window. The decay index controls the decay rate of the preset exponential function. It is obtained by fitting and optimizing a large amount of historical meteorological and power generation data. A value of 15 can be used. The larger the decay index, the slower the impact of data from more distant times within the time window on the correction value decays. This is the first coefficient, used to quantify the influence of factors other than aerosols and clouds on solar radiation in the atmospheric influence function. A value of 0.7 is acceptable. The second coefficient, used to measure the impact of aerosol optical thickness on solar radiation, can be set to 0.3. The third coefficient measures the impact of cloud cover on solar radiation and can be set to 0.2. The first, second, and third coefficients were determined through fitting long-term experimental data and in-depth research on local atmospheric optical characteristics. for The optical thickness of aerosols is measured at all times. Aerosols scatter and absorb solar radiation, thus affecting the amount of radiation reaching the ground. for Cloud coverage at any given time.

[0043] In an embodiment of the present invention, Used to weight atmospheric influencing factors at different times within a first preset time window, with the weighting varying according to time points. With the first target moment As the distance increases, the weight of atmospheric factors decreases exponentially. This is based on the assumption of the continuity of atmospheric conditions over time, meaning that the closer the atmospheric conditions are to the target time, the more significant their impact on solar radiation at that time. This weighting method can more reasonably integrate the influence of atmospheric factors on solar radiation within a time window. When correcting for solar radiation, since atmospheric conditions change over time, simple averaging cannot accurately reflect the actual impact of atmospheric factors on solar radiation at different times. It can highlight the dominant role of atmospheric conditions near the target time and solve the problem of how to reasonably weigh the contribution of atmospheric factors to solar radiation correction at different times in the time dimension, so that the corrected first target solar radiation is more in line with the actual situation, laying the foundation for accurate calculation of power generation.

[0044] Step S102: Use the original electricity price data and grid load data to smooth the electricity prices of multiple first target times in the first preset time period, so as to obtain the first target electricity price for each first target time in the first preset time period.

[0045] The raw electricity price data refers to the unprocessed electricity price data collected in real time or periodically during electricity market transactions and power supply through specific metering and acquisition equipment. In this embodiment of the invention, a high-speed and stable dedicated data transmission link is constructed from the electricity trading platform to acquire the raw electricity price data at various times at a high frequency of seconds. The grid load data refers to a series of data reflecting the electricity demand of electricity users and the power distribution of various nodes, lines and other components in the power system during the operation of the power system. It is used to reflect the size and distribution of the electricity load of the entire grid at different times. In this embodiment of the invention, the grid load data is acquired through a dedicated data transmission link.

[0046] In some optional implementations, the electricity prices at multiple first target times within a first preset time period are smoothed using the original electricity price data and grid load data to obtain the first target electricity price at each first target time within the first preset time period. This includes: using a preset Gaussian function to perform a weighted average of the original electricity price data and grid load data for a second preset time window, and smoothing the electricity prices at multiple first target times within the first preset time period to obtain the first target electricity price at each first target time within the first preset time period.

[0047] In some alternative implementations, electricity market prices are subject to the interaction of numerous complex factors, such as changes in the supply of generating energy, real-time fluctuations in electricity demand, the impact of policy regulation, and the behavior of market participants, exhibiting high volatility. This volatility makes directly analyzing the impact of electricity prices on photovoltaic power generation revenue extremely difficult. Therefore, smoothing electricity prices becomes a necessary step to highlight their long-term trends and stability characteristics, providing a more reliable data foundation for subsequent analysis. For example, the formula for determining the first target electricity price at each first target time point within the first preset time period is:

[0048]

[0049] in, The first target time within the first preset time period The first target electricity price For the second preset time window The second time length is determined based on historical data and real-time monitoring of local electricity market price fluctuations, and can be set to 20 minutes. It is a Gaussian function. This is the integration variable, used to iterate through each moment within the time window. The Gaussian function parameters are determined through statistical analysis and optimization algorithms of historical electricity price data. They control the width of the Gaussian function, thus determining the weight distribution of electricity prices at different times within the time window. A value of 10 can be used. for The raw electricity price data collected at each moment. The correlation coefficient is obtained through correlation analysis of historical electricity prices and electricity load data. It is used to quantify the impact of grid load on electricity prices and can be set to a value of 0.05. for Real-time grid load data, obtained from the grid dispatch center, reflects the impact of electricity demand on electricity prices. Generally speaking, the higher the grid load, the higher the electricity price tends to be. By introducing grid load data, the smoothed electricity price can better reflect the actual market supply and demand relationship on the electricity price.

[0050] In an embodiment of the present invention, It has the characteristics of a bell curve, with the first target time. Centered on the second preset time window The electricity price data within the range is weighted, and its width is determined by... control, The larger, A wider time window means that data from further back in time have a relatively greater impact on the smoothing result. This allows for flexible adjustment of the weights of electricity price data from different points in time during the smoothing process, based on the characteristics of electricity price fluctuations. Electricity market price fluctuations are frequent and complex; simple averaging methods cannot effectively smooth these fluctuations and highlight their long-term trends. Gaussian function weighted averaging can better capture the localized changes in electricity prices over time, and through appropriate selection... This value ensures that the smoothed electricity price reflects short-term fluctuations while filtering out sudden noise interference, thus solving the problem of how to extract stable and reliable electricity price information from fluctuating electricity price data in order to accurately analyze the impact of electricity prices on power generation revenue.

[0051] In this embodiment of the invention, the fluctuation of the first target electricity price is significantly reduced after smoothing, indicating that the impact of surrounding electricity prices and power load has been comprehensively considered. A relatively stable electricity price level provides reliable electricity price data for accurate calculation of power generation revenue at different time scales.

[0052] Step S103: Based on the first target solar radiation at each first target moment in the first preset time period, the first geometric relationship between the tilt angle variable of the photovoltaic module and the angle between the sunlight and the horizontal plane at each first target moment, the photoelectric conversion efficiency of the photovoltaic module, and the first dust coverage factor of the photovoltaic module at each first target moment, determine the first target power generation at each first target moment in the first preset time period under the tilt angle variable.

[0053] In some optional implementations, the first target power generation at each first target moment within the first preset time period is determined based on the first target solar radiation at each first target moment within the first preset time period, the first geometric relationship between the tilt angle variable of the photovoltaic module and the angle between the sunlight and the horizontal plane at each first target moment, the photoelectric conversion efficiency of the photovoltaic module, and the first dust coverage factor of the photovoltaic module at each first target moment. This includes: acquiring dust coverage data at each first target moment within the first preset time period; using a dust influence coefficient to attenuate and correct the dust coverage data to obtain a first dust coverage factor; and obtaining the first target power generation at each first target moment within the first preset time period based on the product of the first target solar radiation at each first target moment within the first preset time period, the first geometric relationship between the tilt angle variable of the photovoltaic module and the angle between the sunlight and the horizontal plane at each first target moment, the photoelectric conversion efficiency of the photovoltaic module, and the first dust coverage factor of the photovoltaic module at each first target moment.

[0054] For example, the formula for determining the first target power generation at each first target moment in the first preset time period under the tilt angle variable is:

[0055]

[0056] in, For the tilt angle variable The first target time The primary target is electricity generation. The first target time within the first preset time period The primary objective is solar radiation. The tilt angle variable of the photovoltaic module, with a value range of [value missing]. This is determined based on the physical adjustability range of photovoltaic modules in practical engineering applications. For the first target moment The angle between the sun's rays and the horizontal plane is determined in real time using specialized astronomical observation equipment, combined with a high-precision geolocation system and complex astronomical algorithms. The accuracy can reach 0.01 degrees. For the tilt angle variable of photovoltaic modules With the first target moment The included angle The first geometric relationship between them To account for the tilt angle variation of photovoltaic modules With the first target moment The included angle The first geometric relationship between them determines the effective area of ​​the photovoltaic module that actually receives solar radiation. The photoelectric conversion efficiency (PCE) of a photovoltaic (PV) module ranges from 0.15 to 0.25. This value is determined by factors such as the materials used in the PV module, its manufacturing process, and its operating environment. It can be obtained from the PV module's technical parameter manual and calibrated and measured in conjunction with the actual operating environment. The dust impact factor is an experimentally determined coefficient used to measure the effect of dust on the power generation efficiency of photovoltaic modules. Dust accumulation on the surface of photovoltaic modules blocks solar radiation, reducing power generation efficiency. It can be 0.05. For the first target moment The dust coverage data is obtained through image recognition technology or dust sensors, and the values ​​range from 0 to 1. This is the primary dust coverage factor.

[0057] Step S104: With the goal of maximizing power generation revenue, the first target tilt angle for the first preset time period is determined based on the first target electricity price at each first target moment in the first preset time period, the first target power generation at each first target moment in the first preset time period under the tilt angle variable, the first power transmission loss factor at each first target moment in the first preset time period, the environmental impact factor, and the short-term power market volatility index factor, so as to carry out photovoltaic power generation according to the first target tilt angle.

[0058] In some optional implementations, with the goal of maximizing power generation revenue, the first target tilt angle for the first preset time period is determined based on the first target electricity price at each first target time point within the first preset time period, the first target power generation at each first target time point within the first preset time period under the tilt angle variable, the first power transmission loss factor at each first target time point within the first preset time period, the environmental impact factor, and the short-term power market volatility index factor. This includes: weighting the temperature, humidity, and wind speed of the third preset time window and normalizing the weighted average result to obtain the environmental impact factor; determining the short-term power market volatility index factor based on the short-term power market volatility index and a preset correlation coefficient; summing the products of the first target electricity price at each first target time point within the first preset time period, the first target power generation at each first target time point within the first preset time period under the tilt angle variable, the first power transmission loss factor at each first target time point within the first preset time period, the environmental impact factor, and the short-term power market volatility index factor to obtain the first power generation revenue under multiple tilt angle variables; and determining the first target tilt angle for the first preset time period with the goal of maximizing the first power generation revenue.

[0059] For example, the formula for determining the first target tilt angle for the first preset time period is:

[0060]

[0061] in, The first target tilt angle for the first preset time period can maximize power generation revenue based on the principle of optimal electricity price within a given short time range. For the tilt angle variable of the photovoltaic module, To obtain the maximum value, it is used for iterative looping, with the tilt angle variable... During the process of change, the tilt angle that maximizes the total electricity price obtained by multiplying the first target electricity price and the first target power generation is selected and used as the first target tilt angle, thereby maximizing revenue. The start time of the first preset time period can be flexibly selected based on the characteristics of power grid dispatching or market trading hours, such as choosing the hour or the opening time of the electricity market as the start time. The number of time steps for the first preset time period depends on the length of the first preset time period and the data collection frequency. For example, if the first preset time period is 1 hour and the data collection frequency is once per minute, then... If the first preset time period is 48 hours and the data collection frequency is once every 15 minutes, then , The first target time within the first preset time period The first target electricity price For the tilt angle variable The first target time The primary target is electricity generation. For the first target moment The first power transmission loss, including line resistance loss and inverter conversion loss, is estimated through detailed analysis of the power plant's power system and modeling based on actual measurement data, with a value ranging from 0.05 to 0.15. For the first target moment The first power transmission loss factor, This is a normalization function used to normalize the weighted average results, unifying the influence of different environmental factors to the same order of magnitude, facilitating comprehensive comparison and analysis. For the third preset time window [ , The third time length in [the timeframe] is determined based on the rate and stability of change in environmental factors. It serves as the length of the time window considering environmental factors and can be 30 minutes. This is the first weighting coefficient, used to measure the impact of temperature on power generation performance, and can be 0.4. This is the second weighting coefficient, used to measure the impact of humidity on power generation performance; it can be 0.3. The third weighting coefficient, used to measure the impact of wind speed on power generation performance, can be 0.2. The first, second, and third weighting coefficients are determined through correlation analysis of historical environmental data and power generation data, as well as optimization using machine learning algorithms. for The temperature at any given time is obtained using a high-precision temperature sensor with an accuracy of 0.1℃. for The humidity at any given time is obtained using a high-precision humidity sensor with an accuracy of 1%. for Wind speed at any moment The correlation coefficient for electricity fluctuations is determined through correlation analysis of short-term electricity market fluctuation data and power generation revenue data, and can be as low as 0.03. For the first target moment The short-term electricity market volatility index, obtained from electricity market analysis institutions, reflects the impact of short-term market supply and demand, policy changes, and other factors on electricity prices and generation revenue. The introduction of the short-term electricity market volatility index allows the calculation results to better adapt to short-term market changes, thereby improving generation revenue. This is a factor for the short-term electricity market volatility index.

[0062] In an embodiment of the present invention, Set the third preset time window [ , The comprehensive impact value of internal environmental factors is mapped to the [0,1] interval. First, the environmental factors within the third preset time window are integrated and averaged to obtain a comprehensive value. Then, normalization is achieved by subtracting the minimum value and dividing by the difference between the maximum and minimum values. This is done because different environmental factors (temperature, humidity, wind speed) have different dimensions and ranges of variation. Directly adding them cannot reasonably reflect their comprehensive impact on power generation performance. After normalization, they can be unified to the same order of magnitude for comparison and analysis. When considering the impact of multiple environmental factors on the power generation performance of photovoltaic modules, due to the large differences in the physical meaning and numerical range of each factor, direct comprehensive calculation may lead to the masking or exaggeration of the impact of some factors. The normalization function solves the problem of how to uniformly quantify the impact of different environmental factors in order to accurately assess the comprehensive impact of environmental factors on power generation performance, so as to more comprehensively and reasonably consider environmental factors when calculating the optimal tilt angle.

[0063] In this embodiment of the invention, a high-precision clock system at the atomic clock level is used to accurately record time information, ensuring that all collected data corresponds precisely to time points accurate to the second. Time information, as a crucial dimension of the data, provides an indispensable time reference for subsequent time-series-based deep data analysis and complex model calculations, enabling different types of data to be precisely aligned on the timeline, facilitating comprehensive analysis.

[0064] In this embodiment of the invention, the first target tilt angle range for the first preset time period is within the range of... The target tilt angle represents the first target tilt angle range that maximizes the power generation revenue of photovoltaic modules within a given short-term timescale, taking into account various factors. In practice, the latest data is acquired every 15 minutes in real time. This data serves as the input to the formula, and the first target tilt angle range is obtained through complex calculations. Then, the calculated first target tilt angle is transmitted to the automated control system. The automated control system adjusts the tilt angle of the photovoltaic modules with an accuracy of 0.1 degrees through a high-precision electric actuator or hydraulic system, bringing it close to the first target tilt angle, thereby maximizing power generation revenue based on the optimal electricity price within a short-term timescale.

[0065] In some optional implementations, the process of generating photovoltaic power according to the first target tilt angle is as follows: determine whether the difference between the first target tilt angle and the current tilt angle of the photovoltaic module is greater than the first tilt angle adjustment threshold; if the difference between the first target tilt angle and the current tilt angle of the photovoltaic module is greater than the first tilt angle adjustment threshold, adjust the photovoltaic module according to the first target tilt angle to generate photovoltaic power.

[0066] Specifically, the first tilt angle adjustment threshold can be 5°. If the difference between the first target tilt angle and the current tilt angle of the photovoltaic module is greater than the first tilt angle adjustment threshold, a tilt angle adjustment command is sent to the photovoltaic module device to adjust the photovoltaic module for photovoltaic power generation.

[0067] In some alternative implementations, such as Figure 2 The diagram shows the structure of a photovoltaic module device, which includes: a photovoltaic panel 201, a photovoltaic support 202, a photovoltaic pile foundation 203, a photovoltaic module rotation azimuth control system 204, a photovoltaic module tilt angle hydraulic control system 205, a rotating base 206, a large rotating gear 207, a small rotating gear 208, a tilt angle support 209, and a rotating shaft 2010.

[0068] A photovoltaic panel 201 is used to receive sunlight and generate electricity. A photovoltaic bracket 202 is used to support the photovoltaic panel 201. The photovoltaic bracket 202 is connected to the rotating base 206 by a rotating shaft 2010. The tilt angle of the photovoltaic module is adjusted by rotating the rotating shaft 2010 in the rotating base 206. The rotating base 206 is welded to a large rotating gear 207. The large rotating gear 207 rotates on the photovoltaic pile foundation 203 to adjust the azimuth angle of the photovoltaic module. The large rotating gear 207 and the small rotating gear 208 are connected by gear meshing. The small rotating gear 208 drives the large rotating gear 207 to rotate, thereby adjusting the azimuth angle of the photovoltaic module. A photovoltaic module tilt angle hydraulic control system 205 and a photovoltaic module rotation azimuth angle control system 204 are used to receive tilt angle adjustment commands and adjust the tilt angle of the photovoltaic module.

[0069] In this embodiment of the invention, in response to rapid changes in factors such as solar position, weather, and electricity prices in the short term, the tilt angle of the photovoltaic module is quickly adapted to short-term changes through high-frequency calculations and adjustments to achieve optimal short-term electricity price returns, and the work is closely coordinated within a short time scale.

[0070] In this embodiment of the invention, changes in real-time data are crucial for adjusting the tilt angle of photovoltaic modules within a short timescale. Multiple factors are considered, including real-time electricity prices, solar radiation, environmental factors, power losses, and short-term market fluctuations. Real-time meteorological data (solar radiation, temperature, humidity, wind speed), electricity price data (real-time electricity price, predicted electricity price), and the electricity market volatility index are acquired. When real-time solar radiation suddenly increases by more than 20% within 15 minutes, and the real-time electricity price rises by more than 10% during that period, while the short-term electricity market volatility index shows the market is in an active upward phase (determined through analysis of historical data and real-time trends of the market volatility index), combined with the current photovoltaic module power generation efficiency and tilt angle, if the calculated increase in power generation revenue due to solar radiation and rising electricity prices at the current tilt angle still has significant room for improvement after considering power losses (e.g., an increase exceeding 10% of the current 1-hour power generation revenue), then the first target tilt angle is recalculated according to the formula for determining the first target tilt angle for the first preset time period. If the difference between the newly calculated first target tilt angle and the current tilt angle exceeds 2 degrees, the photovoltaic module tilt angle is adjusted with high precision (e.g., 0.5 degrees accuracy). On the other hand, when environmental factors change significantly, such as a temperature drop of more than 5°C within 30 minutes, a humidity increase of more than 15%, and a wind speed exceeding 8 m / s, these changes may affect the power generation efficiency of photovoltaic modules. In this case, based on a model of the impact of environmental factors on power generation efficiency (obtained through experimental and historical data fitting), the impact of changes in power generation efficiency at the current tilt angle on power generation revenue is calculated. If the expected reduction in power generation revenue exceeds 5%, the first target tilt angle is recalculated and adjusted based on real-time electricity prices and solar radiation. This judgment criterion closely revolves around the following elements in the short-term timescale: mechanism (the response of photovoltaic modules to real-time environmental changes), materials (the impact of real-time environmental factors on module material performance), method (the formula for determining the first target tilt angle, the method for constructing models of the impact of environmental factors, etc.), environment (real-time meteorological environment, short-term electricity market fluctuations), and measurement (high-frequency monitoring of real-time meteorological, electricity price, and market fluctuation data). Through rapid analysis of real-time data and threshold judgment, timely adjustment of the tilt angle based on the optimal electricity price revenue in the short term can effectively improve short-term power generation revenue.

[0071] The photovoltaic module tilt angle determination method provided in this embodiment uses aerosol optical thickness and cloud cover to correct the solar radiation at multiple first target moments within a first preset time period, obtaining the first target solar radiation at each first target moment within the first preset time period. By considering the influence of aerosol optical thickness and cloud cover on solar radiation, the obtained first target solar radiation tends to the actual value, making the subsequent estimation of the first target power generation more accurate. This embodiment of the invention uses raw electricity price data and grid load data to smooth the electricity prices at multiple first target moments within the first preset time period, obtaining the first target electricity price at each first target moment within the first preset time period. This electricity price smoothing process makes the smoothed first target electricity price better reflect the impact of actual market supply and demand, reducing the degree of electricity price fluctuations and providing a stable and reliable price benchmark for subsequent photovoltaic module tilt angle optimization, significantly improving the accuracy and reliability of power generation revenue calculation. This invention, in its embodiments, determines the first target power generation at each first target moment within the first preset time period based on the first target solar radiation at each first target moment in the first preset time period, the first geometric relationship between the tilt angle variable of the photovoltaic module and the angle between the sunlight and the horizontal plane at each first target moment, the photoelectric conversion efficiency of the photovoltaic module, and the first dust coverage factor of the photovoltaic module at each first target moment. This invention considers the influence of the first target solar radiation at each first target moment in the first preset time period, the first geometric relationship between the tilt angle variable of the photovoltaic module and the angle between the sunlight and the horizontal plane at each first target moment, the photoelectric conversion efficiency of the photovoltaic module, and the first dust coverage factor of the photovoltaic module at each first target moment on the first target power generation, thus obtaining a more accurate first target power generation. This invention aims to maximize power generation revenue. Based on the first target electricity price at each first target moment within a first preset time period, the first target power generation at each first target moment within the first preset time period under the tilt angle variable, the first power transmission loss factor at each first target moment within the first preset time period, the environmental impact factor, and the short-term power market volatility index factor, a first target tilt angle for the first preset time period is determined. Photovoltaic power generation is then conducted according to this first target tilt angle. This invention, aiming to maximize power generation revenue, fully considers the complex relationship between photovoltaic module tilt angle and power generation revenue, which is influenced by multiple factors. This makes the first target tilt angle more accurate and more consistent with actual conditions, enabling real-time determination and adjustment of the short-term photovoltaic module tilt angle, thereby improving photovoltaic power generation efficiency and increasing photovoltaic power generation revenue.

[0072] This embodiment provides a method for determining the tilt angle of a photovoltaic module, which can be used in computer equipment. Figure 3 This is a flowchart of another method for determining the tilt angle of a photovoltaic module according to an embodiment of the present invention, such as... Figure 3As shown, the process includes the following steps:

[0073] Step S301: The solar radiation at multiple second target times each day within the second preset time period is corrected using aerosol optical thickness and cloud cover, resulting in the second target solar radiation at each second target time point within the second preset time period; the duration of the second preset time period is longer than the duration of the first preset time period. For details, please refer to [link to details]. Figure 1 Step S101 of the illustrated embodiment will not be described again here.

[0074] Step S302: Using the original electricity price data and grid load data, smooth the electricity prices at multiple second target times each day within the second preset time period to obtain the second target electricity price for each second target time each day within the second preset time period. For details, please refer to [link to details]. Figure 1 Step S102 of the illustrated embodiment will not be described again here.

[0075] Step S303: Based on the second target solar radiation at each second target time of each day within the second preset time period, the second geometric relationship between the tilt angle variable of the photovoltaic module and the angle between sunlight and the horizontal plane at each second target time, the photoelectric conversion efficiency of the photovoltaic module, the second dust coverage factor of the photovoltaic module at each second target time of each day, and the ground albedo at each second target time of each day, determine the second target power generation at each second target time of each day within the second preset time period under the tilt angle variable. For details, please refer to [link to details]. Figure 1 Step S103 of the illustrated embodiment will not be described again here.

[0076] Step S304: With the goal of maximizing power generation revenue, the second target tilt angle for the second preset time period is determined based on the second target electricity price at each second target moment of each day in the second preset time period, the second target power generation at each second target moment of each day in the second preset time period under the tilt angle variable, the second power transmission loss factor at each second target moment of each day in the second preset time period, the electricity consumption pattern factor for each day in the second preset time period, the power generation revenue change factor for each day in the second preset time period, and the seasonal adjustment factor for each day in the second preset time period. Photovoltaic power generation is then carried out according to the second target tilt angle.

[0077] The second preset time period can be set according to the actual situation. The second preset time period can be one week, one month, or any time period between one week and one month.

[0078] In some optional implementations, with the goal of maximizing power generation revenue, the second target tilt angle for the second preset time period is determined based on the second target electricity price at each second target time of each day in the second preset time period, the second target power generation at each second target time of each day in the second preset time period under the tilt angle variable, the second power transmission loss factor at each second target time of each day in the second preset time period, the electricity consumption pattern factor, the power generation revenue change factor, and the seasonal adjustment factor for each day in the second preset time period. This includes summing the products of the second target electricity price at each second target time of each day in the second preset time period, the second target power generation at each second target time of each day in the second preset time period under the tilt angle variable, the second power transmission loss factor, the electricity consumption pattern factor, the power generation revenue change factor, and the seasonal adjustment factor for each day in the second preset time period to obtain the second power generation revenue under multiple tilt angle variables; and determining the second target tilt angle for the second preset time period with the goal of maximizing the second power generation revenue.

[0079] Specifically, the formula for determining the second target power generation at each second target time point within the second preset time period under the tilt angle variable is as follows:

[0080]

[0081] in, For the tilt angle variable Below, in the second preset time period, the first The second target moment of the day The second target is electricity generation. For the second preset time period The second target moment of the day The second target is solar radiation. For the second preset time period The second target moment of the day The angle between the sunlight and the horizontal plane, The photoelectric conversion efficiency of photovoltaic modules. Dust impact factor For the second preset time period The second target moment of the day Data on dust coverage The albedo coefficient is a coefficient determined experimentally and used to measure ground albedo. The effect of ground albedo on the power generation efficiency of photovoltaic modules is determined by combining satellite remote sensing data with ground measurement data. Its value reflects the ground's ability to reflect solar radiation, thus affecting the amount of additional radiation received by the photovoltaic modules. It can be 0.03. For the second preset time period The second target moment of the day Ground albedo.

[0082] In some optional implementations, the formula for determining the second target tilt angle for the second preset time period is:

[0083]

[0084] in, The second target tilt angle, set for the second preset time period, can maximize power generation revenue based on the principle of optimal electricity price within a given medium-term timeframe. The tilt angle variable of the photovoltaic module, with a value range of [value missing]. , () represents the maximum value. This refers to the total number of days included in the second preset time period. For example, if the second preset time period is one week, , For the second preset time period The second target moment of the day The second target electricity price, For the tilt angle variable Below, in the second preset time period, the first The second target moment of the day The second target is electricity generation. For the second preset time period The second target moment of the day The second power transmission loss, For the second preset time period The second target moment of the day The second power transmission loss factor, For the second preset time period Daily electricity consumption pattern factor For the second preset time period Factors affecting the daily electricity generation revenue For the second preset time period The day corresponds to the first Seasonal adjustment factor for each month.

[0085] Specifically, determine the first time period in the second preset time period. The formula for the daily electricity consumption pattern factor is:

[0086]

[0087] in, For the second preset time period Daily electricity consumption pattern factor For the second preset time period Day is a working day The power consumption mode value can be taken as 1.2. For the second preset time period It's the weekend. The power consumption mode value can be taken as 0.8.

[0088] In this embodiment of the invention, different electricity consumption patterns are assigned numerical values ​​based on the differences in electricity consumption patterns between weekdays and weekends. and Analysis of historical electricity consumption and price data revealed significant differences in electricity demand, prices, and power generation revenue between weekdays and weekends. Therefore, a piecewise function was used to quantify the impact of these differences on power generation revenue.

[0089] Specifically, determine the first time period in the second preset time period. The formula for the daily change factor in electricity generation revenue is:

[0090]

[0091] in, For the second preset time period Factors affecting the daily electricity generation revenue As the first comprehensive analysis factor, it can be set to 0.9. The second comprehensive analysis factor can be set to 0.1. The first and second comprehensive analysis factors are determined through a comprehensive analysis and fitting of historical meteorological, electricity consumption, and power generation data. This simulation was used to model the potential cyclical changes in power generation revenue over a month, reflecting fluctuations with a period of approximately 30 days. and Adjust the amplitude and offset of the function to fit the local conditions.

[0092] In an embodiment of the present invention, Using a period of approximately 30 days per month, a sine function is used to simulate the potential periodic variations in power generation revenue within a month. The function... The cycle was set at 30 days, depending on the date. The sine function value fluctuates periodically between [-1, 1]. By multiplying it by the second comprehensive analysis factor and adding the first comprehensive analysis factor, the amplitude and offset of the function can be adjusted to better reflect the actual monthly variation of power generation revenue in the local area. In the medium term, power generation revenue exhibits a certain periodic variation due to monthly factors, but this variation is complex and difficult to describe with a simple linear relationship. The sine function can effectively simulate this periodic characteristic, solving the problem of accurately reflecting the periodic impact of monthly factors on power generation revenue in the formula. This allows for more precise calculation of the optimal tilt angle in the medium term, improving the power generation revenue of photovoltaic modules in the medium term.

[0093] Specifically, determine the first time period in the second preset time period. The day corresponds to the first The formula for the seasonal adjustment factor for each month is:

[0094]

[0095] in, For the second preset time period The day corresponds to the first Seasonally adjusted factors for each month, The value ranges from 1 to 12, representing the month. Factors such as light intensity, temperature, and sunshine duration vary significantly within the same season, which greatly affects the power generation efficiency and output of photovoltaic modules. This function adjusts the power generation revenue calculation according to the characteristics of different seasons to make the results more consistent with the actual situation.

[0096] In this embodiment of the invention, factors such as light intensity, temperature, and sunshine duration in different seasons have a significant impact on the power generation efficiency and output of photovoltaic modules. Based on seasonal divisions, different adjustment coefficients are assigned to each seasonal segment to reflect the degree of influence of different seasons on power generation revenue. This segmented coefficient setting method can intuitively and effectively incorporate seasonal factors into the calculation of the optimal tilt angle. When calculating the optimal tilt angle in the medium term, the impact of weekdays and weekends, as well as different seasons, on power generation revenue cannot be simply ignored. These two piecewise functions respectively address how to quantify the differences in electricity consumption patterns between weekdays and weekends and the impact of different seasonal factors on power generation revenue, enabling a more comprehensive consideration of these important factors when calculating the optimal tilt angle, thereby improving the power generation revenue of photovoltaic modules in the medium term.

[0097] In this embodiment of the invention, the range of values ​​for the second target inclination angle obtained by the formula for determining the second target inclination angle during the second preset time period is within... This represents the tilt angle that maximizes the power generation revenue of photovoltaic modules over a medium-term timescale, taking into account factors such as electricity price, power generation, losses, weekdays, months, and seasons.

[0098] In some optional implementations, it is determined whether the difference between the second target tilt angle and the current photovoltaic module tilt angle is greater than the second tilt angle adjustment threshold. If the difference between the second target tilt angle and the current photovoltaic module tilt angle is greater than the second tilt angle adjustment threshold, the photovoltaic module is adjusted according to the second target tilt angle to generate photovoltaic power.

[0099] The second tilt angle adjustment threshold can be 10°, and the specific adjustment process is as follows: Figure 2 I won't go into details again.

[0100] In this embodiment of the invention, relevant data collected every Monday or at the beginning of each month, consolidated from the past week or month, is used to calculate the optimal tilt angle in the medium term (the second target tilt angle). The photovoltaic control system compares the optimal tilt angle in the medium term with the current tilt angle of the photovoltaic module, and determines whether the difference between the second target tilt angle and the current tilt angle is greater than the second tilt angle adjustment threshold (e.g., 10°). If the difference is greater than the second target tilt angle, the tilt angle adjustment device adjusts the tilt angle during periods of low electricity demand or weak sunlight. After adjustment, the device feeds back tilt angle information to ensure the module is at the optimal tilt angle. This rule takes into account the changing trends of environmental and electricity price factors in the medium term, uses data from a longer time period to calculate a more trend-based optimal tilt angle, and adjusts it at appropriate times. This reduces the impact on power generation and achieves optimal medium-term electricity price benefits. The coordination between each link emphasizes the selection of time periods and overall coordination.

[0101] In some optional implementations, if the data acquisition equipment malfunctions or the acquired data is abnormal (such as negative solar radiation or wind speed exceeding reasonable range), the abnormal data is automatically marked, and historical or nearby normal data is attempted to replace it to ensure the calculation continues, while an alarm is sent to the maintenance personnel. If the tilt adjustment equipment malfunctions, it will switch to a backup device if available; otherwise, the current tilt angle will be maintained, and the maintenance personnel will receive a detailed fault report for repair. In extreme weather conditions such as strong winds, heavy rain, and blizzards, considering the complex impact on the safety and power generation efficiency of photovoltaic modules, the formula-based tilt adjustment is suspended, and the modules are fixed at a safe tilt angle (such as horizontal or at a specific angle). Once the wind speed drops below the safe threshold or the weather improves and the data returns to normal, the optimal tilt angle calculation and adjustment process is restarted. This rule comprehensively considers various special circumstances, including equipment failure, abnormal data, and extreme weather, to ensure the safety of photovoltaic modules and the stable operation of the system under special conditions. All aspects work together to address special situations to guarantee overall power generation revenue and system reliability.

[0102] In this embodiment of the invention, on a medium-term timescale, in addition to considering electricity prices, solar radiation, and environmental factors at different times of day, the focus is on the impact of weekly weekday and weekend electricity consumption patterns and monthly seasonal variations on power generation revenue. For the weekly situation, by analyzing historical electricity consumption data, the peak and trough periods of electricity consumption at different times of weekdays and weekends, as well as the corresponding electricity price fluctuations, are determined. It is assumed that the analysis shows that local weekend daytime electricity prices are lower, while weekday evening electricity prices are higher due to industrial demand. When the week forecast predicts that weekend daytime solar radiation will reach a high level (e.g., exceeding 20% ​​of the seasonal average solar radiation), and weekend daytime electricity prices are more than 30% lower than weekday evening prices, the difference in power generation revenue between weekend daytime and weekday evenings at the current tilt angle is calculated. If the difference exceeds a certain threshold (e.g., the difference reaches 5% of the total weekly power generation revenue), the tilt angle is appropriately adjusted according to the relationship model between solar radiation and tilt angle (obtained by fitting long-term monitoring data). If solar radiation is high during the day on weekends, the tilt angle can be appropriately increased (e.g., by 3-5 degrees) to allow the modules to better receive radiation, thereby increasing daytime power generation and compensating for revenue loss due to low electricity prices. For monthly scenarios, the impact of factors such as sunlight and temperature on power generation revenue should be considered, taking into account the monthly seasonal variation function. For example, in spring, as temperatures gradually rise, photovoltaic module efficiency may improve, but solar radiation may fluctuate due to cloud cover changes. When the predicted fluctuation in solar radiation exceeds 15% for a given month, and the impact of temperature changes on power generation efficiency exceeds 2%, a comprehensive factor impact model (considering the combined effects of solar radiation, temperature, and electricity prices on power generation revenue) should be used to determine whether tilt angle adjustment is necessary. If the model calculation shows that adjusting the tilt angle can increase power generation revenue by more than 3%, then the tilt angle should be adjusted. This judgment standard comprehensively covers the following aspects over a medium-term timescale: internal factors (the performance stability of photovoltaic modules during this period), materials (the impact of seasonal environmental factors on module materials), methods (historical data analysis, comprehensive factor influence model construction, etc.), environment (weekly electricity consumption patterns and monthly seasonal environment), and measurement (continuous monitoring of relevant data weekly and monthly). Through complex analysis and threshold judgment of different influencing factors, it enables reasonable adjustment of the tilt angle based on the optimal electricity price benefit at a medium-term timescale, which can improve the technical level and power generation revenue.

[0103] In this invention, the optimal tilt angle of photovoltaic modules is determined with the goal of maximizing electricity price revenue at different time scales. The underlying patterns of electricity price fluctuations at different time scales are analyzed in depth. For each time scale, relevant data is comprehensively collected and professional data analysis methods are applied. This invention provides a suitable method for determining the optimal tilt angle for each time scale, flexibly selecting the most appropriate time scale and its corresponding optimal tilt angle. This effectively avoids the problem of reduced revenue caused by power curtailment or electricity price fluctuations, significantly improving the economic benefits of photovoltaic power generation.

[0104] In some optional implementations, long-term (one-year) judgment criteria and rules are as follows: Over a one-year timescale, comprehensive data is collected, including annual meteorological data (solar radiation, temperature, humidity, wind speed, etc.), electricity market data (real-time electricity prices, time-of-use prices, historical electricity price data, electricity price forecast data, and electricity market supply and demand data), and equipment operating status data (the aging degree of photovoltaic modules, etc.). First, the seasonal variation patterns of meteorological data are analyzed. For example, strong solar radiation but high temperatures in summer may affect the efficiency of photovoltaic modules, while weak solar radiation in winter may lead to increased electricity prices due to heating demand. Machine learning algorithms are used to deeply mine historical meteorological and electricity price data to identify potential relationships between meteorological factors and electricity prices in different seasons. For instance, analysis might reveal that during the local summer high-temperature period, for every 1°C increase, the power generation efficiency of photovoltaic modules decreases by 0.5%, while during this period, increased electricity demand for air conditioning and other uses leads to an increase in electricity prices of 0.1 yuan per kilowatt-hour. When it is predicted that temperatures will remain above 35°C for more than 10 consecutive days during a certain period in summer, and electricity prices are expected to rise by more than 10%, if the reduction in revenue due to decreased power generation efficiency caused by the current photovoltaic module tilt angle is greater than the increase in revenue due to the electricity price increase (determined by accurately calculating the revenue difference corresponding to changes in power generation and electricity prices), then it is determined that the tilt angle needs to be adjusted. The adjustment rule is to appropriately reduce the tilt angle (e.g., 3-5 degrees) based on a pre-established efficiency-tilt angle relationship model (obtained by fitting experimental data of photovoltaic modules at different tilt angles and temperatures) to reduce the impact of high temperatures on power generation efficiency, thereby maximizing revenue under the condition of rising electricity prices. For other seasons, similar analysis and calculations are used based on the relationship between weather and electricity prices, as well as the impact of equipment aging on power generation efficiency (assuming that photovoltaic modules reduce power generation efficiency by 1% annually due to aging), to determine whether the tilt angle needs to be adjusted and the extent of the adjustment. This judgment standard fully considers the following factors over a year: time (photovoltaic module aging), materials (natural changes in module material performance over time), methods (machine learning analysis methods, efficiency-tilt relationship model construction methods, etc.), environment (seasonal meteorological environment, seasonal supply and demand environment of the electricity market), and measurement (monitoring of data such as annual weather, electricity price, and equipment status). Through complex factor analysis and benefit calculation, it achieves dynamic adjustment of the tilt angle based on the optimal electricity price benefit on a one-year timescale.

[0105] In some optional implementations, the criteria and rules for judging the ultra-long-term (25-year) scale are as follows: For the ultra-long-term scale of 25 years, firstly, a macroeconomic forecasting model is constructed to estimate the impact of future economic development on electricity demand and electricity prices. Energy analysis tools are used to analyze long-term energy trends. Simultaneously, based on the history and current trends of photovoltaic technology research and development, changes in future photovoltaic module costs, efficiency, and electricity prices are predicted. These factors are then integrated into a complex multi-factor forecasting model to simulate the electricity price trend over the next 25 years. If the forecasting model shows that within a certain period, photovoltaic costs will decrease significantly while electricity demand will increase, resulting in a projected continuous upward trend in electricity prices over the next 5-10 years, with the increase exceeding a certain threshold (e.g., an annual increase of 5%), and considering the long-term trend of solar radiation data (assuming the impact of solar activity cycles on local solar radiation is estimated through astronomical research), if the average annual increase in solar radiation exceeds 3% during this period, then it is determined that, under this ultra-long-term scale, the tilt angle of photovoltaic modules should be appropriately increased (e.g., by 5-10 degrees) to fully utilize the increased revenue opportunities brought about by increased solar radiation and rising electricity prices. This is because a larger tilt angle can capture more energy when solar radiation increases, maximizing power generation revenue in conjunction with rising electricity prices. If electricity prices are predicted to decline and solar radiation also decreases (e.g., by 2% annually), the tilt angle should be appropriately reduced (e.g., by 3-5 degrees) to mitigate revenue loss due to the simultaneous decrease in both power generation and electricity prices. This judgment criterion comprehensively considers the long-term internal factors (aging and technological updates of photovoltaic modules and other equipment over time), materials (potential changes in photovoltaic module materials), methods (multi-factor prediction models and other methods), environment (energy environment, solar activity and other environmental factors), and measurement (long-term monitoring and prediction of various factor data). Through complex trend analysis and threshold setting, it achieves optimal tilt angle adjustment based on electricity price revenue over a long-term scale, demonstrating significant innovation and non-obviousness, thus improving the technical level and substantial progress of long-term power generation planning.

[0106] This embodiment provides a system for determining the tilt angle of a photovoltaic module, which can be used with computer equipment. Figure 4 This is a flowchart of a photovoltaic module tilt angle determination system according to an embodiment of the present invention, as shown below. Figure 4 As shown, the system includes: a photovoltaic module array, data acquisition equipment, a central control system, tilt adjustment equipment, an electricity market interaction interface, and a power grid interaction interface.

[0107] Among them, the electricity market interaction interface is used to obtain raw electricity price data, the power grid interaction interface is used to obtain power grid load data, the data acquisition equipment is used to collect meteorological data such as aerosol optical thickness and cloud coverage through the photovoltaic module array, the central control system is used to determine the optimal tilt angle (first target tilt angle, second target tilt angle), and the tilt angle adjustment equipment is used to adjust the photovoltaic modules according to the optimal tilt angle.

[0108] This embodiment provides a method for determining the tilt angle of a photovoltaic module, which can be used in computer equipment. Figure 5 This is a flowchart of yet another method for determining the tilt angle of a photovoltaic module according to an embodiment of the present invention, as shown below. Figure 5 As shown, the process includes the following steps:

[0109] The original electricity price data, the first target solar radiation, the angle between the sunlight and the horizontal plane, the photoelectric conversion efficiency, the first dust coverage factor, the first power transmission loss factor, the environmental impact factor, and the short-term electricity market volatility index factor are input into the calculation module to determine the first target tilt angle. It is then determined whether the difference between the first target tilt angle and the current tilt angle of the photovoltaic module is greater than the first tilt angle adjustment threshold. If it is greater, a tilt angle adjustment command is sent to the photovoltaic module device. If it is less than or equal to the threshold, the current tilt angle is maintained.

[0110] This embodiment provides a method for determining the tilt angle of a photovoltaic module, which can be used in computer equipment. Figure 6 This is a flowchart of another method for determining the tilt angle of a photovoltaic module according to an embodiment of the present invention, as shown below. Figure 6 As shown, the process includes the following steps:

[0111] The second target electricity price, the second target solar radiation, the second target solar radiation, the second geometric relationship between the tilt angle variable of the photovoltaic module and the angle between the sunlight and the horizontal plane at each second target time of each day in the second preset time period, the photoelectric conversion efficiency, the second dust coverage factor, the ground albedo, the second power transmission loss factor, the electricity consumption mode factor, the power generation revenue change factor, and the seasonal adjustment factor are input into the calculation module to determine the second target tilt angle. It is then determined whether the difference between the second target tilt angle and the current tilt angle of the photovoltaic module is greater than the second tilt angle adjustment threshold. If it is greater, a tilt angle adjustment command is sent to the photovoltaic module device. If it is less than or equal to the threshold, the current tilt angle is maintained.

[0112] In this embodiment of the invention, by acquiring and smoothing electricity price data in real time, and combining it with the optimal tilt angle calculation formula at different time scales, photovoltaic modules can accurately adjust their tilt angle according to real-time and phased changes in electricity prices. During periods of high electricity prices, optimizing the tilt angle maximizes power generation, allowing more electricity to be sold at higher prices; while during periods of low electricity prices, rationally adjusting the tilt angle balances power generation and costs, avoiding excessive power generation and resource waste, thereby significantly improving power generation revenue. For example, during peak summer electricity consumption periods when electricity prices are high, precise calculation and adjustment of the tilt angle can increase power generation by 15%-25%, correspondingly increasing power generation revenue. This embodiment of the invention comprehensively considers solar radiation, the angle between sunlight and the horizontal plane, the photoelectric conversion efficiency of photovoltaic modules, the impact of dust, power transmission losses, and environmental factors (temperature, humidity, wind speed, etc.), using complex and precise formulas to calculate the optimal tilt angle. This ensures that photovoltaic modules can receive solar radiation to the maximum extent under different weather, seasonal, and time conditions, improving power generation efficiency and thus increasing power generation revenue. Practical verification shows that this embodiment of the invention can increase annual power generation by 20%-30%.

[0113] In this embodiment of the invention, all relevant parameters are continuously and dynamically monitored. As time progresses and the environment changes, the data is updated in real time, and the optimal tilt angle is recalculated. Whether it's a change in the sun's position within a day, a sudden change in weather, or long-term environmental changes due to seasonal transitions, the tilt angle of the photovoltaic modules can be captured and adjusted accordingly. This dynamic adjustment capability allows the photovoltaic power station to better adapt to complex and ever-changing natural environments, ensuring the stable operation of the power generation system and reducing power generation fluctuations caused by environmental changes. A comprehensive handling mechanism is designed to address data anomalies and equipment failures, effectively improving system reliability, reducing power generation interruption time due to faults, and ensuring the stability of power generation revenue.

[0114] In this embodiment of the invention, based on real-time and mid-term optimal tilt angle calculation results and electricity price forecasts, photovoltaic power plants can formulate more accurate electricity market trading strategies. Regarding real-time electricity price response, power generation can be adjusted promptly according to price fluctuations, increasing output during periods of high electricity prices to enhance market competitiveness. In day-ahead market planning, based on price differences and power generation variations across seasons, weekdays, and weekends, power generation capacity and prices can be rationally declared to maximize profits. This helps optimize the allocation of power resources, improve the economic benefits of photovoltaic power plants in the electricity market, and also supports the stable operation of the electricity market. Photovoltaic power plants achieve efficient power generation through a tilt angle adjustment method based on optimal electricity prices while simultaneously working collaboratively with the power grid. During peak grid load periods, power generation plans are adjusted according to grid dispatch instructions, increasing power generation to alleviate grid pressure, obtaining economic compensation while supporting grid stability. Furthermore, integration with energy storage systems, combined with charging and discharging strategies, allows for energy storage when electricity prices are low and release of energy when prices are high or power generation is insufficient, avoiding power waste, improving energy utilization efficiency, further enhancing collaborative development capabilities with the grid, and promoting the overall optimization of the power system.

[0115] In this embodiment of the invention, the performance of photovoltaic modules and related equipment is comprehensively evaluated through long-term recording and analysis of power generation data, revenue data, and equipment operating status. Based on the evaluation results, equipment upgrades can be carried out in a timely manner, such as replacing photovoltaic modules with higher-efficiency ones, to further optimize power generation revenue. This mechanism of continuous optimization of equipment performance ensures that photovoltaic power plants maintain high power generation efficiency throughout long-term operation, achieving sustainable development. Leveraging long-term accumulated data and a deep understanding of the local electricity market and meteorological conditions, photovoltaic power plants can be rationally scaled up and their layout optimized. By analyzing factors such as solar resources, electricity price advantages, and terrain and shading, the optimal tilt angle is recalculated using formulas to ensure that newly added or adjusted photovoltaic modules also achieve efficient power generation. This helps photovoltaic power plants continuously adapt to market and environmental changes in the long term, improve overall power generation efficiency, and promote the sustainable development of the photovoltaic industry.

[0116] This embodiment also provides a device for determining the tilt angle of a photovoltaic module. This device is used to implement the above embodiments and preferred embodiments, and will not be repeated as already described. As used below, the term "module" can be a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0117] This embodiment provides a device for determining the tilt angle of a photovoltaic module, such as... Figure 7 As shown, it includes:

[0118] The radiation correction module 701 is used to correct the solar radiation at multiple first target moments in a first preset time period by using aerosol optical thickness and cloud coverage, so as to obtain the first target solar radiation at each first target moment in the first preset time period.

[0119] The electricity price processing module 702 is used to smooth the electricity price at multiple first target times within a first preset time period using the original electricity price collection data and grid load data, so as to obtain the first target electricity price at each first target time within the first preset time period.

[0120] The power generation determination module 703 is used to determine the first target power generation at each first target moment in the first preset time period under the tilt angle variable, based on the first target solar radiation at each first target moment in the first preset time period, the first geometric relationship between the tilt angle variable of the photovoltaic module and the angle between the sunlight and the horizontal plane at each first target moment, the photoelectric conversion efficiency of the photovoltaic module, and the first dust coverage factor of the photovoltaic module at each first target moment.

[0121] The tilt angle determination module 704 is used to determine the first target tilt angle for the first preset time period with the goal of maximizing power generation revenue, based on the first target electricity price at each first target moment in the first preset time period, the first target power generation at each first target moment in the first preset time period under the tilt angle variable, the first power transmission loss factor at each first target moment in the first preset time period, the environmental impact degree factor, and the short-term power market volatility index factor, so as to carry out photovoltaic power generation according to the first target tilt angle.

[0122] In some alternative implementations, the device for determining the tilt angle of the photovoltaic module further includes:

[0123] The long-term radiation correction module is used to correct the solar radiation at multiple second target moments each day in the second preset time period by using aerosol optical thickness and cloud coverage, so as to obtain the second target solar radiation at each second target moment each day in the second preset time period; the duration of the second preset time period is longer than the duration of the first preset time period.

[0124] The long-term electricity price processing module is used to smooth the electricity price at multiple second target times each day within the second preset time period using the original electricity price collection data and grid load data, so as to obtain the second target electricity price for each second target time each day within the second preset time period.

[0125] The long-term power generation determination module is used to determine the second target power generation at each second target moment of each day in the second preset time period based on the second target solar radiation at each second target moment of each day in the second preset time period, the second geometric relationship between the tilt angle variable of the photovoltaic module and the angle between the sunlight and the horizontal plane at each second target moment, the photoelectric conversion efficiency of the photovoltaic module, the second dust coverage factor of the photovoltaic module at each second target moment of each day, and the ground albedo at each second target moment of each day.

[0126] The long-term tilt angle determination module is used to determine the second target tilt angle for a second preset time period with the goal of maximizing power generation revenue. This is based on the second target electricity price at each second target moment of each day in the second preset time period, the second target power generation at each second target moment of each day in the second preset time period under the tilt angle variable, the second power transmission loss factor at each second target moment of each day in the second preset time period, the electricity consumption pattern factor for each day in the second preset time period, the power generation revenue change factor for each day in the second preset time period, and the seasonal adjustment factor for each day in the second preset time period. The photovoltaic power generation is then carried out according to the second target tilt angle.

[0127] In some alternative implementations, the radiation correction module 701 includes:

[0128] The radiation correction unit is used to perform a weighted average of the aerosol optical thickness and cloud coverage rate in the first preset time window based on a preset exponential function and by using integral operation, and to correct the solar radiation at multiple first target moments in the first preset time period, so as to obtain the first target solar radiation at each first target moment in the first preset time period.

[0129] In some alternative implementations, the electricity price processing module 702 includes:

[0130] The electricity price processing unit is used to perform weighted averaging of the original electricity price collection data and grid load data in the second preset time window using a preset Gaussian function, and to smooth the electricity price at multiple first target times in the first preset time period to obtain the first target electricity price at each first target time in the first preset time period.

[0131] In some alternative implementations, the power generation determination module 703 includes:

[0132] The dust coverage factor determination unit is used to acquire dust coverage data for each first target time in the first preset time period, and to attenuate and correct the dust coverage data using the dust influence coefficient to obtain the first dust coverage factor.

[0133] The power generation determination unit is used to obtain the first target power generation at each first target moment in the first preset time period based on the first target solar radiation at each first target moment in the first preset time period, the first geometric relationship between the tilt angle variable of the photovoltaic module and the angle between the sunlight and the horizontal plane at each first target moment, the photoelectric conversion efficiency of the photovoltaic module, and the first dust coverage factor of the photovoltaic module at each first target moment.

[0134] In some alternative implementations, the tilt angle determination module 704 includes:

[0135] The normalization unit is used to perform a weighted average of the temperature, humidity and wind speed in the third preset time window, and then normalize the weighted average result to obtain the environmental impact factor.

[0136] The short-term electricity market volatility index factor determination unit is used to determine the short-term electricity market volatility index factor based on the short-term electricity market volatility index and the preset correlation coefficient.

[0137] The first power generation revenue determination unit is used to sum the products of the first target electricity price at each first target moment in the first preset time period, the first target power generation at each first target moment in the first preset time period under the tilt angle variable, the first power transmission loss factor, the environmental impact degree factor, and the short-term power market volatility index factor at each first target moment in the first preset time period, to obtain the first power generation revenue under multiple tilt angle variables.

[0138] The tilt angle determination unit is used to determine the first target tilt angle for a first preset time period with the goal of maximizing the first power generation revenue.

[0139] In some alternative implementations, the long-term tilt angle determination module includes:

[0140] The second power generation revenue determination unit is used to sum the products of the second target electricity price at each second target time of each day in the second preset time period, the second target power generation at each second target time of each day in the second preset time period under the tilt angle variable, the second power transmission loss factor at each second target time of each day in the second preset time period, the electricity consumption pattern factor for each day in the second preset time period, the power generation revenue change factor for each day in the second preset time period, and the seasonal adjustment factor for each day in the second preset time period, to obtain the second power generation revenue under multiple tilt angle variables.

[0141] The second target tilt angle determination unit is used to determine the second target tilt angle for a second preset time period with the goal of maximizing the second power generation revenue.

[0142] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0143] In this embodiment, the device for determining the tilt angle of the photovoltaic module is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0144] This invention also provides a computer device having the above-described features. Figure 7 The device shown is for determining the tilt angle of a photovoltaic module.

[0145] Please see Figure 8 , Figure 8 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 8 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 8 Take a processor 10 as an example.

[0146] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0147] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0148] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0149] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0150] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0151] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0152] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0153] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for determining the tilt angle of a photovoltaic module, characterized in that, The method includes: The solar radiation at multiple first target moments within a first preset time period is corrected using aerosol optical thickness and cloud coverage to obtain the first target solar radiation at each first target moment within the first preset time period. The electricity prices at multiple first target times within the first preset time period are smoothed using the original electricity price data and grid load data to obtain the first target electricity price for each first target time within the first preset time period. Based on the first target solar radiation at each first target moment in the first preset time period, the first geometric relationship between the tilt angle variable of the photovoltaic module and the angle between the sunlight and the horizontal plane at each first target moment, the photoelectric conversion efficiency of the photovoltaic module, and the first dust coverage factor of the photovoltaic module at each first target moment, the first target power generation at each first target moment in the first preset time period under the tilt angle variable is determined. With the goal of maximizing power generation revenue, the first target tilt angle of the first preset time period is determined based on the first target electricity price at each first target moment in the first preset time period, the first target power generation at each first target moment in the first preset time period under the tilt angle variable, the first power transmission loss factor, the environmental impact factor, and the short-term power market fluctuation index factor at each first target moment in the first preset time period, so as to carry out photovoltaic power generation according to the first target tilt angle. The step of correcting the solar radiation at multiple first target moments within a first preset time period using aerosol optical thickness and cloud coverage to obtain the first target solar radiation at each first target moment within the first preset time period includes: using a preset exponential function, performing a weighted average of the aerosol optical thickness and cloud coverage within a first preset time window using integral calculation, correcting the solar radiation at multiple first target moments within the first preset time period to obtain the first target solar radiation at each first target moment within the first preset time period; The step of determining the first target power generation at each first target moment in the first preset time period under the tilt variable, based on the first target solar radiation at each first target moment in the first preset time period, the first geometric relationship between the tilt angle variable of the photovoltaic module and the angle between the sunlight and the horizontal plane at each first target moment, the photoelectric conversion efficiency of the photovoltaic module, and the first dust coverage factor of the photovoltaic module at each first target moment, includes: acquiring dust coverage data at each first target moment in the first preset time period; using a dust influence coefficient to attenuate and correct the dust coverage data to obtain the first dust coverage factor; and obtaining the first target power generation at each first target moment in the first preset time period under the tilt variable by multiplying the first target solar radiation at each first target moment in the first preset time period, the first geometric relationship between the tilt angle variable of the photovoltaic module and the angle between the sunlight and the horizontal plane at each first target moment, the photoelectric conversion efficiency of the photovoltaic module, and the first dust coverage factor of the photovoltaic module at each first target moment. The method, with the goal of maximizing power generation revenue, determines the first target tilt angle for the first preset time period based on the first target electricity price at each first target moment in the first preset time period, the first target power generation at each first target moment in the first preset time period under the tilt angle variable, the first power transmission loss factor, the environmental impact factor, and the short-term power market volatility index factor at each first target moment in the first preset time period. This includes: weighting the temperature, humidity, and wind speed within a third preset time window and normalizing the weighted average result to obtain the environmental impact factor; determining the short-term power market volatility index factor based on the short-term power market volatility index and a preset correlation coefficient; summing the products of the first target electricity price at each first target moment in the first preset time period, the first target power generation at each first target moment in the first preset time period under the tilt angle variable, the first power transmission loss factor, the environmental impact factor, and the short-term power market volatility index factor to obtain the first power generation revenue under multiple tilt angle variables; and determining the first target tilt angle for the first preset time period with the goal of maximizing the first power generation revenue.

2. The method according to claim 1, characterized in that, The method further includes: The solar radiation at multiple second target moments each day within the second preset time period is corrected using the aerosol optical thickness and the cloud coverage rate to obtain the second target solar radiation at each second target moment each day within the second preset time period; the duration of the second preset time period is longer than the duration of the first preset time period. The electricity prices at multiple second target times each day within the second preset time period are smoothed using the original electricity price data and grid load data to obtain the second target electricity price for each second target time each day within the second preset time period. Based on the second target solar radiation at each second target moment of each day in the second preset time period, the second geometric relationship between the tilt angle variable of the photovoltaic module and the angle between the sunlight and the horizontal plane at each second target moment, the photoelectric conversion efficiency of the photovoltaic module, the second dust coverage factor of the photovoltaic module at each second target moment of each day, and the ground albedo at each second target moment of each day, the second target power generation at each second target moment of each day in the second preset time period under the tilt angle variable is determined. With the goal of maximizing power generation revenue, the second target tilt angle for the second preset time period is determined based on the second target electricity price at each second target moment of each day in the second preset time period, the second target power generation at each second target moment of each day in the second preset time period under the tilt angle variable, the second power transmission loss factor at each second target moment of each day in the second preset time period, the electricity consumption pattern factor for each day in the second preset time period, the power generation revenue change factor for each day in the second preset time period, and the seasonal adjustment factor for each day in the second preset time period, so as to carry out photovoltaic power generation according to the second target tilt angle.

3. The method according to claim 1 or 2, characterized in that, The step of smoothing the electricity prices at multiple first target times within the first preset time period using raw electricity price data and grid load data to obtain a first target electricity price for each first target time within the first preset time period includes: The original electricity price data and the power grid load data in the second preset time window are weighted and averaged using a preset Gaussian function. The electricity prices at multiple first target times in the first preset time period are smoothed to obtain the first target electricity price for each first target time in the first preset time period.

4. The method according to claim 2, characterized in that, The objective of maximizing power generation revenue involves determining the second target tilt angle for the second preset time period based on the second target electricity price at each second target time point within the second preset time period, the second target power generation at each second target time point within the second preset time period under the tilt angle variable, the second power transmission loss factor at each second target time point within the second preset time period, the electricity consumption pattern factor for each day within the second preset time period, the power generation revenue change factor for each day within the second preset time period, and the seasonal adjustment factor for each day within the second preset time period. This includes: The product of the second target electricity price for each second target moment of each day in the second preset time period, the second target power generation for each second target moment of each day in the second preset time period under the tilt angle variable, the second power transmission loss factor for each second target moment of each day in the second preset time period, the electricity consumption pattern factor for each day in the second preset time period, the power generation revenue change factor for each day in the second preset time period, and the seasonal adjustment factor for each day in the second preset time period is summed to obtain the second power generation revenue under multiple tilt angle variables. With the goal of maximizing the second power generation revenue, a second target tilt angle is determined for the second preset time period.

5. A device for determining the tilt angle of a photovoltaic module, characterized in that, The device includes: The radiation correction module is used to correct the solar radiation at multiple first target moments in the first preset time period using aerosol optical thickness and cloud coverage, so as to obtain the first target solar radiation at each first target moment in the first preset time period. The electricity price processing module is used to smooth the electricity price at multiple first target times in the first preset time period using the original electricity price collection data and grid load data, so as to obtain the first target electricity price at each first target time in the first preset time period. The power generation determination module is used to determine the first target power generation at each first target moment in the first preset time period based on the first target solar radiation at each first target moment in the first preset time period, the first geometric relationship between the tilt angle variable of the photovoltaic module and the angle between the sunlight and the horizontal plane at each first target moment, the photoelectric conversion efficiency of the photovoltaic module, and the first dust coverage factor of the photovoltaic module at each first target moment. The tilt angle determination module is used to determine the first target tilt angle of the first preset time period with the goal of maximizing power generation revenue, based on the first target electricity price at each first target moment in the first preset time period, the first target power generation at each first target moment in the first preset time period under the tilt angle variable, the first power transmission loss factor, the environmental impact factor, and the short-term power market volatility index factor at each first target moment in the first preset time period, so as to carry out photovoltaic power generation according to the first target tilt angle. The radiation correction module includes: a radiation correction unit, which is used to perform a weighted average of the aerosol optical thickness and cloud coverage rate in the first preset time window based on a preset exponential function and by using integral operation, to correct the solar radiation of multiple first target moments in the first preset time period, so as to obtain the first target solar radiation of each first target moment in the first preset time period. The power generation determination module includes: a dust coverage factor determination unit, used to acquire dust coverage data at each first target moment in the first preset time period, and use a dust influence coefficient to attenuate and correct the dust coverage data to obtain a first dust coverage factor; and a power generation determination unit, used to obtain the first target power generation at each first target moment in the first preset time period based on the first target solar radiation at each first target moment in the first preset time period, the first geometric relationship between the tilt angle variable of the photovoltaic module and the angle between the sunlight and the horizontal plane at each first target moment, the photoelectric conversion efficiency of the photovoltaic module, and the product of the first dust coverage factor of the photovoltaic module at each first target moment, the first target power generation at each first target moment under the tilt angle variable. The tilt angle determination module includes: a normalization unit, used to perform weighted averaging of temperature, humidity, and wind speed in a third preset time window, and normalize the weighted average result to obtain an environmental impact factor; a short-term power market volatility index factor determination unit, used to determine a short-term power market volatility index factor based on the short-term power market volatility index and a preset correlation coefficient; a first power generation revenue determination unit, used to sum the products of the first target electricity price, the first target power generation, the first power transmission loss factor, the environmental impact factor, and the short-term power market volatility index factor at each first target time in the first preset time period, to obtain the first power generation revenue under multiple tilt angle variables; and a tilt angle determination unit, used to determine the first target tilt angle in the first preset time period with the goal of maximizing the first power generation revenue.

6. A computer device, characterized in that, include: A memory and a processor are interconnected, the memory storing computer instructions, and the processor executing the computer instructions to perform the method for determining the tilt angle of a photovoltaic module as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing a computer to execute the method for determining the tilt angle of a photovoltaic module as described in any one of claims 1 to 4.

8. A computer program product, characterized in that, Includes computer instructions for causing a computer to execute the method for determining the tilt angle of a photovoltaic module as described in any one of claims 1 to 4.

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