Grid-exported power assessment method and apparatus, and electronic device and storage medium

Abstract

Provided in the present disclosure are a grid-exported power assessment method and apparatus, and an electronic device and a storage medium. The method comprises: on the basis of site environment data, site meteorological data and project design data of a photovoltaic power station, calculating the average system efficiency of the photovoltaic power station; analyzing the site meteorological data, so as to determine the peak sunshine duration of the photovoltaic power station; analyzing the project design data to determine the power degradation of the photovoltaic power station, and using the power degradation to calculate the peak generation power of the photovoltaic power station; and on the basis of the average system efficiency, the peak sunshine duration and the peak generation power, calculating the power generation capacity of the photovoltaic power station within a preset period of time. Compared with the prior art, in the embodiments of the present disclosure, site environment data, site meteorological data and project design data are taken into comprehensive consideration, such that the actual operation situation of a photovoltaic power station can be reflected more comprehensively; and on the basis of the calculation results, the power generation capacity of the photovoltaic power station within a preset period of time can be accurately estimated, thereby providing an important basis for predicting the income of the power station and assessing the economic benefit of the power station.

Description

Methods and apparatus for assessing internet power consumption, electronic devices and storage media Technical Field

[0001] This disclosure relates to the field of data processing technology, and in particular to a method and apparatus, electronic device and storage medium for assessing online power consumption. Background Technology

[0002] The estimation of power generation from photovoltaic (PV) power plants primarily relies on an assessment of local solar energy resources. This includes factors such as the intensity and duration of solar radiation, as well as seasonal and diurnal variations. Solar radiation is the primary energy source for PV power generation, therefore its intensity and stability directly impact power output. Assessing solar energy resources typically requires the use of specialized meteorological data and satellite remote sensing technology to obtain accurate historical solar radiation data and future forecasts.

[0003] After obtaining solar resource data, it is necessary to consider the system design and component performance of photovoltaic power plants. This includes factors such as the type, quantity, and arrangement of photovoltaic modules, as well as the system's conversion efficiency. Different photovoltaic module and system designs will result in different power generation efficiencies, therefore, these factors need to be evaluated and optimized in detail.

[0004] Power generation forecasts also need to consider environmental conditions and the overall efficiency coefficient. Environmental conditions such as temperature, humidity, and wind speed all affect the power generation efficiency of photovoltaic modules. The overall efficiency coefficient is a correction factor that takes into account the influence of multiple factors, including the conversion efficiency of photovoltaic modules, inverter efficiency, line losses, and module surface contamination. These factors can all lead to a reduction in power generation, so corresponding adjustments need to be made in the forecast. Summary of the Invention

[0005] This disclosure provides a method, apparatus, electronic device, and storage medium for assessing grid-connected power generation. Its main purpose is to address the problem of the inability to accurately assess the grid-connected power generation of photovoltaic power plants.

[0006] According to a first aspect of this disclosure, a method for assessing internet power consumption is provided, comprising:

[0007] The average system efficiency of the photovoltaic power station is calculated based on the site environmental data, site meteorological data, and project design data.

[0008] Analyze the meteorological data of the plant site to determine the peak sunshine duration of the photovoltaic power station;

[0009] The power generation attenuation of the photovoltaic power station is determined by analyzing the project design data, and the peak power generation of the photovoltaic power station is calculated using the power generation attenuation.

[0010] The power generation of the photovoltaic power station within a preset time period is calculated based on the average system efficiency, the peak sunshine duration, and the peak power generation.

[0011] In some embodiments, calculating the average system efficiency of the photovoltaic power station based on site environmental data, site meteorological data, and project design data includes:

[0012] Obtain the assessment accuracy of power generation and determine the time granularity based on the assessment accuracy;

[0013] Based on the site environmental data, the site meteorological data, and the project design data, the average system efficiency of the photovoltaic power station in different time segments is calculated according to the time granularity.

[0014] In some embodiments, calculating the average system efficiency of the photovoltaic power station in different time segments according to the time granularity based on the site environmental data, the site meteorological data, and the project design data includes:

[0015] The project design data is analyzed to determine the photovoltaic array efficiency, inverter conversion efficiency, line loss energy loss rate, AC grid connection efficiency, and other power regulation losses of the photovoltaic power station.

[0016] Based on the site environmental data and the site meteorological data, the unusable losses of the photovoltaic modules of the photovoltaic power station are determined;

[0017] Based on the photovoltaic array efficiency, the inverter conversion efficiency, the line loss energy loss rate, the AC grid connection efficiency, the other power regulation losses, and the unusable losses of the photovoltaic modules, the average system efficiency of the photovoltaic power station in different time segments is generated.

[0018] In some embodiments, analyzing the meteorological data at the plant site to determine the peak sunshine duration of the photovoltaic power station in different time segments includes:

[0019] Based on the time granularity, the meteorological data of the plant site is divided into data segments to obtain the meteorological data of the photovoltaic power station in different time segments;

[0020] Sunshine data are extracted from the meteorological data of the plant site in different time segments to determine the peak sunshine time of the photovoltaic power station in different time segments.

[0021] In some embodiments, analyzing the project design data to determine the power generation attenuation of the photovoltaic power station and using the power generation attenuation to calculate the peak power generation of the photovoltaic power station includes:

[0022] Data analysis was performed on the project design data to determine the degradation curve of the photovoltaic modules in the photovoltaic power station;

[0023] Based on the attenuation curve and the operating time of the photovoltaic power station, the power generation attenuation of the photovoltaic power station is calculated and determined;

[0024] Based on the initial peak power generation of the photovoltaic power station and the power generation attenuation of the photovoltaic power station in different time segments, the peak power generation of the photovoltaic power station in different time segments is calculated.

[0025] In some embodiments, the method further includes:

[0026] Acquire site environmental data, site meteorological data, and project design data for photovoltaic power plants, and perform data preprocessing on the site environmental data and site meteorological data.

[0027] According to a second aspect of this disclosure, an apparatus for assessing internet power consumption is provided, comprising:

[0028] The first calculation unit is used to calculate the average system efficiency of the photovoltaic power station based on the site environmental data, site meteorological data, and project design data.

[0029] The determining unit is used to analyze the meteorological data of the plant site and determine the peak sunshine duration of the photovoltaic power station;

[0030] The analysis unit is used to analyze the project design data to determine the power generation attenuation of the photovoltaic power station, and to calculate the peak power generation of the photovoltaic power station using the power generation attenuation.

[0031] The second calculation unit is used to calculate the power generation of the photovoltaic power station within a preset time period based on the average system efficiency, the peak sunshine duration, and the peak power generation.

[0032] In some embodiments, the first computing unit includes:

[0033] The acquisition module is used to obtain the assessment accuracy of power generation assessment and determine the time granularity based on the assessment accuracy.

[0034] The first calculation module is used to calculate the average system efficiency of the photovoltaic power station in different time segments according to the time granularity, based on the site environmental data, the site meteorological data, and the project design data.

[0035] In some embodiments, the first computing module is further configured to:

[0036] The project design data is analyzed to determine the photovoltaic array efficiency, inverter conversion efficiency, line loss energy loss rate, AC grid connection efficiency, and other power regulation losses of the photovoltaic power station.

[0037] Based on the site environmental data and the site meteorological data, the unusable losses of the photovoltaic modules of the photovoltaic power station are determined;

[0038] Based on the photovoltaic array efficiency, the inverter conversion efficiency, the line loss energy loss rate, the AC grid connection efficiency, the other power regulation losses, and the unusable losses of the photovoltaic modules, the average system efficiency of the photovoltaic power station in different time segments is generated.

[0039] In some embodiments, the determining unit includes:

[0040] The segmentation module is used to segment the meteorological data of the plant site according to the time granularity to obtain the meteorological data of the photovoltaic power station in different time segments.

[0041] The first determining module is used to extract sunshine data from the meteorological data of the plant site in different time segments, and to determine the peak sunshine time of the photovoltaic power station in different time segments.

[0042] In some embodiments, the analysis unit includes:

[0043] The analysis module is used to perform data analysis on the project design data and determine the degradation curve of the photovoltaic modules of the photovoltaic power station.

[0044] The second determining module is used to calculate and determine the power generation attenuation of the photovoltaic power station based on the attenuation curve and the operating time of the photovoltaic power station.

[0045] The second calculation module is used to calculate the peak power generation of the photovoltaic power station in different time segments based on the initial peak power generation of the photovoltaic power station and the power generation attenuation of the photovoltaic power station in different time segments.

[0046] In some embodiments, the apparatus further includes:

[0047] The preprocessing unit is used to acquire site environmental data, site meteorological data and project design data of photovoltaic power plants, and to perform data preprocessing on the site environmental data and the site meteorological data.

[0048] According to a third aspect of this disclosure, an electronic device is provided, comprising:

[0049] At least one processor; and

[0050] A memory communicatively connected to the at least one processor; wherein,

[0051] The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method described in the first aspect above.

[0052] According to a fourth aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions, wherein the computer instructions are configured to cause the computer to perform the method described in the first aspect above.

[0053] According to a fifth aspect of this disclosure, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the method described in the first aspect above.

[0054] This disclosure provides a method, apparatus, electronic device, and storage medium for assessing grid-connected power generation. Based on site environmental data, site meteorological data, and project design data of a photovoltaic (PV) power plant, the method calculates the average system efficiency of the PV power plant; analyzes the site meteorological data to determine the peak sunshine duration of the PV power plant; analyzes the project design data to determine the power generation attenuation of the PV power plant, and uses the power generation attenuation to calculate the peak power generation of the PV power plant; and calculates the power generation of the PV power plant within a preset time period based on the average system efficiency, the peak sunshine duration, and the peak power generation. Compared with related technologies, this disclosure, by comprehensively considering site environmental data, site meteorological data, and project design data, can more comprehensively reflect the actual operating status of the PV power plant; by analyzing the site meteorological data, the peak sunshine duration of the PV power plant can be determined; by analyzing the project design data, the power generation attenuation of the PV power plant can be predicted; based on the calculation results of the average system efficiency and peak power generation, the power generation performance of the PV power plant can be evaluated; and based on the above calculation results, the power generation of the PV power plant within a preset time period can be accurately estimated, providing an important basis for power plant power sales, revenue forecasting, and economic benefit assessment.

[0055] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent from the following description. Attached Figure Description

[0056] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein:

[0057] Figure 1 is a flowchart illustrating a method for evaluating internet battery usage according to an embodiment of this disclosure;

[0058] Figure 2 is a flowchart illustrating another method for evaluating internet power consumption provided in an embodiment of this disclosure;

[0059] Figure 3 is a schematic diagram of the structure of a device for evaluating internet power consumption according to an embodiment of this disclosure;

[0060] Figure 4 is a schematic diagram of another device for evaluating internet power consumption provided in an embodiment of this disclosure;

[0061] Figure 5 is a schematic block diagram of an example electronic device provided in an embodiment of this disclosure. Detailed Implementation

[0062] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0063] The following description, with reference to the accompanying drawings, describes a method, apparatus, electronic device, and storage medium for evaluating internet power consumption according to embodiments of the present disclosure.

[0064] Figure 1 is a flowchart illustrating a method for evaluating internet power consumption according to an embodiment of this disclosure.

[0065] As shown in Figure 1, the method includes the following steps:

[0066] Step 101: Calculate the average system efficiency of the photovoltaic power station based on the site environmental data, site meteorological data, and project design data.

[0067] In the embodiments disclosed herein, the site environmental data of the photovoltaic power plant includes, but is not limited to, geographical location, topography, soil type, vegetation cover, and the distribution of surrounding buildings or obstructions; the site meteorological data of the photovoltaic power plant includes, but is not limited to, key climate indicators covering historical and predicted solar radiation intensity, sunshine duration, temperature range, wind speed and direction, precipitation, atmospheric transparency, humidity level, etc.; the project design data of the photovoltaic power plant may include, but is not limited to, the type and specifications of photovoltaic modules, array layout, inverter efficiency, cable line design and loss, configuration and efficiency of energy storage system, type and performance parameters of tracking system (if any), and operation and maintenance strategy and maintenance cycle of the entire power plant.

[0068] By comprehensively applying mathematical models and algorithms, the average system efficiency of the photovoltaic power station is calculated and evaluated. This calculation process not only considers the direct impact of natural environmental factors on photovoltaic power generation efficiency, but also deeply analyzes the contribution and limitations of power station design details and technical configurations to overall performance, aiming to provide a comprehensive indicator reflecting the long-term operational performance of the power station.

[0069] Step 102: Analyze the meteorological data of the plant site to determine the peak sunshine duration of the photovoltaic power station.

[0070] In the embodiments of this disclosure, the peak sunshine duration of the photovoltaic power plant is determined by organizing the site meteorological data of the area where the photovoltaic power plant is located and by evaluating various meteorological indicators closely related to the operation of the photovoltaic power plant. Specifically, historical meteorological records at the plant site are analyzed, including but not limited to key data such as daily total solar radiation, sunshine duration, cloud cover, atmospheric transparency, temperature change trends, and wind speed and direction. By calling meteorological analysis algorithms or tools, these data are combined with the operating characteristics of the photovoltaic power plant to determine the time period when the solar radiation intensity reaches or approaches its maximum value, i.e., the "peak sunshine duration".

[0071] The analysis should also consider the impact of seasonal variations, geographical location differences, and long-term climate trends on peak sunshine duration. For example, some regions may have longer sunshine duration and higher solar radiation intensity in summer, while the opposite is true in winter. Furthermore, it is necessary to assess the potential interference of specific weather events (such as sudden increases in cloud cover, dust storms, etc.) on peak sunshine duration, and how these factors affect the actual power generation capacity of the photovoltaic power plant. In this way, the sunshine duration of the photovoltaic power plant can be calculated based on historical meteorological data by assigning different weather conditions to the factors influencing sunshine duration.

[0072] Step 103: Analyze the project design data to determine the power generation attenuation of the photovoltaic power station, and use the power generation attenuation to calculate the peak power generation of the photovoltaic power station.

[0073] In the embodiments of this disclosure, analyzing the project design data to determine the power generation degradation of the photovoltaic power station is a process involving multiple complex factors and variables, requiring a high degree of expertise and meticulous work. This involves obtaining key information such as the selection and specifications of the photovoltaic modules, including their conversion efficiency, weather resistance, lifespan, and the degradation guarantee provided by the manufacturer. Over long-term use, the power generation efficiency of photovoltaic modules of different brands and models will gradually decrease due to various factors such as material aging, dust accumulation, temperature effects, and ultraviolet radiation; this degree of decrease is referred to as power generation degradation.

[0074] The actual power generation efficiency and degradation trend of a photovoltaic power station depend on factors such as the array layout and orientation, and the presence of an automatic tracking system to maximize solar radiation absorption. These factors include the rationality of the array layout, the appropriateness of the spacing between modules, and the accuracy and response speed of the tracking system. Furthermore, inverter efficiency, cable line design and losses, and the configuration and charging / discharging efficiency of the energy storage system are also crucial considerations. As a key device converting direct current to alternating current, the efficiency of the inverter directly affects the output power of the entire power station; the design of the cable lines relates to energy losses during transmission; and while the energy storage system can provide power support when sunlight is insufficient, its own charging / discharging efficiency also affects the overall power generation efficiency.

[0075] Based on a comprehensive consideration of all the above factors, a simulation analysis algorithm is used, combined with historical data and predictive analysis, to estimate the power generation degradation curve of a photovoltaic power plant. This curve describes the trend of power plant efficiency changes over time and is an important basis for evaluating the long-term performance and economic benefits of the power plant.

[0076] Based on the estimated power generation attenuation, combined with the initial design capacity of the photovoltaic power station and the theoretical power generation under peak sunshine hours, the actual peak power generation of the photovoltaic power station is calculated.

[0077] Step 104: Calculate the power generation of the photovoltaic power station within a preset time period based on the average system efficiency, the peak sunshine duration, and the peak power generation.

[0078] In the embodiments of this disclosure, based on the previously determined average system efficiency, peak sunshine duration, and calculated peak power generation, the power generation of the photovoltaic power plant within a preset time period can be further derived.

[0079] Average system efficiency reflects the overall efficiency of a photovoltaic (PV) power plant in capturing energy from solar radiation and converting it into usable electrical energy. It integrates the effects of multiple factors, including PV module efficiency, inverter efficiency, line losses, dust accumulation, temperature effects, and system maintenance conditions, and is a key indicator for evaluating the long-term performance of a power plant. Peak sunshine duration defines the timeframe during which solar radiation intensity reaches or approaches its maximum for a given geographical location within a specific season or time period. This data is crucial for estimating the maximum power generation potential of a power plant, as it directly reflects how much solar energy the plant can capture. Peak power output represents the instantaneous output power of a PV power plant under ideal conditions (i.e., when solar radiation intensity is maximum and system efficiency is highest). It is calculated based on factors such as the power plant's design specifications, module type, array layout, and tracking system performance, providing a benchmark for predicting the power generation capacity of the power plant.

[0080] After mastering the above three key parameters, a power generation calculation model is adopted to combine the average system efficiency, peak sunshine duration and peak power generation, while taking into account dynamic factors in actual operation such as weather changes, cloud cover and temperature fluctuations, to accurately estimate the power generation of the photovoltaic power station within a preset period (such as day, week, month, year, etc.).

[0081] This disclosure provides a method for assessing grid-connected power generation. Based on site environmental data, site meteorological data, and project design data, the method calculates the average system efficiency of the photovoltaic (PV) power plant. It analyzes the site meteorological data to determine the peak sunshine duration of the PV power plant. It analyzes the project design data to determine the power generation attenuation of the PV power plant and uses the attenuation to calculate the peak power generation. Based on the average system efficiency, the peak sunshine duration, and the peak power generation, the method calculates the power generation of the PV power plant within a preset time period. Compared with related technologies, this disclosure, by comprehensively considering site environmental data, site meteorological data, and project design data, can more comprehensively reflect the actual operating status of the PV power plant. Analysis of the site meteorological data can determine the peak sunshine duration of the PV power plant. Analysis of the project design data can predict the power generation attenuation of the PV power plant. Based on the calculation results of the average system efficiency and peak power generation, the power generation performance of the PV power plant can be evaluated. Based on the above calculation results, the power generation of the PV power plant within a preset time period can be accurately estimated, providing an important basis for power plant electricity sales, revenue forecasting, and economic benefit assessment.

[0082] To clearly illustrate the embodiments of this disclosure, this embodiment provides a flowchart of another method for evaluating internet power consumption.

[0083] As shown in Figure 2, the method includes the following steps:

[0084] Step 201: Obtain the site environmental data, site meteorological data and project design data of the photovoltaic power station, and perform data preprocessing on the site environmental data and the site meteorological data.

[0085] Specifically, in step 201, in order to comprehensively evaluate the potential power generation capacity and long-term operational performance of the photovoltaic power plant, it is first necessary to systematically acquire environmental data, meteorological data, and project design data of the photovoltaic power plant site. This not only requires the comprehensiveness and accuracy of the data, but also emphasizes the effective management and preprocessing of the data to ensure the smooth progress of subsequent analysis work.

[0086] When acquiring site environmental data, it is crucial to analyze factors such as topography, soil type, vegetation cover, hydrological conditions, and surrounding obstacles. This data is typically collected using various technologies, including on-site surveys, satellite remote sensing, drone aerial photography, and Geographic Information Systems (GIS). Meteorological data acquisition relies primarily on meteorological observation stations, satellite cloud images, historical meteorological records, and professional weather forecasting models. These parameters are essential for assessing the power plant's power generation efficiency, component temperature effects, heat dissipation requirements, and system stability. The frequency and accuracy of meteorological data collection directly impact the accuracy of subsequent power generation predictions and system design. Project design data encompasses detailed information on photovoltaic power plant component selection, array layout, inverter configuration, energy storage system design, cable routing, and monitoring system setup.

[0087] After obtaining the above data, the raw data undergoes cleaning (removing outliers, duplicates, etc.), format conversion (standardizing data format, unit conversion, etc.), missing value handling (interpolation, regression, etc.), and data normalization and standardization. The purpose of data preprocessing is to improve data quality, eliminate noise and bias, and lay a solid foundation for subsequent data analysis and modeling.

[0088] Through this series of rigorous data acquisition and preprocessing steps, we can ensure the comprehensiveness, accuracy, and availability of environmental data, meteorological data, and project design data for photovoltaic power plant sites, providing a scientific basis for the preliminary design, performance evaluation, and long-term operation and maintenance of the power plant.

[0089] Step 202: Obtain the assessment accuracy of power generation assessment and determine the time granularity based on the assessment accuracy.

[0090] Specifically, in step 202, to accurately calculate the average system efficiency of the photovoltaic power plant, it is necessary to comprehensively consider the site environmental data, site meteorological data, and project design data, and follow a series of rigorous calculation steps. It is also necessary to clearly define the accuracy requirements for power generation assessment and determine an appropriate time granularity accordingly to ensure the accuracy and practicality of the calculation results.

[0091] Determining the required accuracy is a prerequisite for calculating average system efficiency. It is based on the power plant's operational needs, the purpose of data analysis, and the acceptable error range, determining the level of accuracy required in subsequent calculations. High accuracy requirements mean we need more detailed time segmentation and more complex data processing, while low accuracy requirements allow for coarser time segmentation and simplified data analysis methods.

[0092] Once the assessment accuracy is determined, the time granularity can be set according to this requirement. Time granularity refers to the fineness of the time division, such as hours, days, months, or years. It directly affects the data sampling frequency and computational complexity when calculating average system efficiency. Smaller time granularity can provide higher resolution power generation efficiency data, but it also increases the difficulty of data processing and computational costs; while larger time granularity simplifies the calculation process, it may sacrifice some accuracy and detail.

[0093] Step 203: Based on the site environmental data, the site meteorological data, and the project design data, calculate the average system efficiency of the photovoltaic power station in different time segments according to the time granularity.

[0094] Step 2031: Analyze the project design data to determine the photovoltaic array efficiency, inverter conversion efficiency, line loss energy loss rate, AC grid connection efficiency, and other power regulation losses of the photovoltaic power station.

[0095] Step 2032: Based on the site environmental data and the site meteorological data, determine the unusable losses of the photovoltaic modules of the photovoltaic power station;

[0096] Step 2033: Based on the photovoltaic array efficiency, the inverter conversion efficiency, the line loss energy loss rate, the AC grid connection efficiency, the other power regulation losses, and the unusable losses of the photovoltaic modules, generate the average system efficiency of the photovoltaic power station in different time segments.

[0097] Specifically, in step 203, after determining the time granularity, the average system efficiency of the photovoltaic power station in different time segments can be calculated based on site environmental data, site meteorological data, and project design data, according to this time granularity. This process involves the precise measurement and calculation of the actual power generation, theoretical power generation, and system losses (such as shading losses, temperature effects, and line losses). Meteorological data is used to simulate the impact of key environmental factors such as solar radiation intensity and temperature on power generation efficiency, and the component efficiency, inverter efficiency, and overall system performance of the power station are evaluated by combining environmental data and project design data.

[0098] Through detailed analysis of the project design data, the core performance parameters of the photovoltaic power station were determined. These parameters include photovoltaic array efficiency, which reflects the efficiency of photovoltaic modules in converting sunlight into electrical energy; inverter conversion efficiency, which measures the energy loss in the process of the inverter converting DC to AC; line loss energy loss rate, which describes the loss of electrical energy during transmission through the line; AC grid connection efficiency, which reflects the efficiency of the power station's output power when connected to the grid; and other power regulation losses, which cover the energy losses generated by the power regulation equipment (such as transformers, filters, etc.) inside the power station during operation.

[0099] This study utilizes site environmental and meteorological data to determine the unusable losses of photovoltaic (PV) modules in a photovoltaic power plant. These losses primarily include energy losses caused by factors such as shading, dust accumulation, temperature effects, and atmospheric transparency. By comprehensively considering environmental factors and meteorological conditions, the actual impact of these factors on the power generation efficiency of PV modules can be assessed more accurately.

[0100] Having grasped the aforementioned key parameters and loss scenarios, I can begin calculating the average system efficiency of the photovoltaic power plant across different time segments. This process involves dividing the power plant's operating time into several time segments according to the required time granularity, and calculating the values ​​of the aforementioned parameters within each time segment. Then, we substitute these parameter values ​​into the calculation model and use iterative and optimization algorithms to solve for the average system efficiency within that time segment. This process will be repeated until we obtain the average system efficiency data for all time segments.

[0101] Step 204: Based on the time granularity, divide the meteorological data of the plant site into data segments to obtain the meteorological data of the photovoltaic power station in different time segments.

[0102] Specifically, in step 204, the meteorological data for the power plant site is divided according to the determined time granularity to ensure accurate acquisition of meteorological condition information for the photovoltaic power plant in different time segments. The specific requirements for time granularity are clarified; it determines how many time segments the meteorological data is divided into and the length of each segment. The choice of time granularity is usually based on the power plant's operational needs, the required accuracy of the data analysis, and the resolution of the available meteorological data. For example, the time granularity might be divided into hours, days, weeks, or months, depending on the actual situation of the power plant and the purpose of the analysis.

[0103] The raw meteorological data is screened, organized, and reorganized. The meteorological data is allocated to corresponding time segments based on time labels, ensuring that the data within each time segment is complete and continuous.

[0104] During the data segmentation process, special attention was paid to meteorological parameters that significantly impact the power generation efficiency of photovoltaic power plants, such as solar radiation intensity, temperature, humidity, wind speed, wind direction, and precipitation. These parameters not only directly affect the power generation efficiency of photovoltaic modules but also indirectly influence the overall performance of the power plant by affecting its heat dissipation conditions, module temperature, and system stability. Ultimately, site meteorological data for the photovoltaic power plant were obtained for different time periods.

[0105] Step 205: Extract sunshine data from the meteorological data of the plant site in different time segments to determine the peak sunshine time of the photovoltaic power station in different time segments.

[0106] Specifically, in step 205, in order to analyze in depth the relationship between the power generation performance of the photovoltaic power station and the sunshine conditions, it is necessary to extract the sunshine data from the meteorological data of the plant site in different time segments, and determine the peak sunshine time of the photovoltaic power station in each time segment.

[0107] First, sunshine data is precisely extracted from the pre-defined time-segmented meteorological data. This sunshine data typically includes key information such as solar radiation intensity, sunshine duration, and sunshine periods, which together constitute a complete dataset describing the sunshine conditions of a region. During the extraction process, high-precision timestamp technology is employed to ensure that each piece of sunshine data accurately corresponds to its respective time segment. This not only improves data accuracy but also allows for in-depth statistical analysis and processing of the extracted sunshine data. Statistical analysis of solar radiation intensity yields key indicators such as average radiation intensity, maximum radiation intensity, and radiation intensity trends within each time segment. Simultaneously, combined with data on sunshine duration and sunshine periods, a comprehensive assessment of the sunshine conditions for photovoltaic power plants is conducted.

[0108] After determining the solar radiation conditions for each time period, the correlation between the power generation efficiency of photovoltaic power plants and solar radiation conditions was further analyzed. During time periods with better solar radiation conditions, the power generation efficiency of photovoltaic power plants was generally higher, and the peak solar radiation time and the peak period of power generation efficiency showed a significant positive correlation.

[0109] Based on these analyses, the peak sunshine duration of the photovoltaic power plant was determined for different time periods. This data not only provides important references for the daily operation and maintenance of the power plant but also offers a scientific basis for performance evaluation, optimized design, and long-term planning. By gaining a deeper understanding of the relationship between the power plant's sunshine conditions and power generation performance, we can more accurately predict the power generation, optimize the power plant's operating strategies, and thus improve the overall economic and social benefits of the power plant.

[0110] Step 206: Perform data analysis on the project design data to determine the degradation curve of the photovoltaic modules of the photovoltaic power station.

[0111] Specifically, in step 206, the project design data includes a series of key information from the planning, design, and construction of the photovoltaic power station, such as the model, specifications, installation angle, arrangement, inverter configuration, cable selection, and layout of the photovoltaic modules. This information provides the necessary background and foundation for analyzing the degradation characteristics of the photovoltaic modules.

[0112] During the data analysis phase, the focus is on extracting data directly related to the performance of photovoltaic modules, especially those that reflect changes in module performance over time. This includes, but is not limited to, the module's initial power output, power measurement results over the years or for specific time periods, records of environmental factors (such as temperature, humidity, and irradiance), and possible fault or maintenance records.

[0113] Statistical methods and specialized software tools are used to process and analyze this data. By comparing the power output of the modules at different time points, the power degradation rate of the modules can be calculated, which is a key indicator for assessing the degree of module degradation. Furthermore, by combining environmental factors and the physical characteristics of the modules (such as materials and manufacturing processes), mathematical models or curves of module degradation can be constructed to more intuitively show the trend of module performance changes over time.

[0114] The degradation curve of a photovoltaic (PV) module typically appears as a gradually decreasing curve, depicting the gradual reduction in power output from the initial installation stage to the end of its expected lifespan. This curve is crucial for predicting the long-term power generation capacity of a power plant, planning maintenance strategies, and assessing the return on investment.

[0115] Step 207: Calculate and determine the power generation attenuation of the photovoltaic power station based on the attenuation curve and the operating time of the photovoltaic power station.

[0116] Specifically, in step 207, the actual operating time of the photovoltaic power station is considered. This refers to the total time the power station has experienced since it was put into operation, which may be measured in hours, days, months, or years. The accuracy of the operating time is crucial for calculating power generation degradation because it directly affects the assessment of the current performance status of the components. After mastering the two key parameters of degradation curve and operating time, we can begin to calculate the power generation degradation of the photovoltaic power station. This process typically involves the following steps:

[0117] Selecting reference points: Identify one or more reference points, which typically represent the power output level at the beginning of the power plant's operation or at a specific point in time. These reference points will serve as the benchmark for calculating attenuation.

[0118] Calculate the current power output: Estimate the power output level at the current time point based on the decay curve and the actual operating time of the power plant. This typically involves mathematical processing of the decay curve, such as interpolation or extrapolation.

[0119] Calculating power degradation: By comparing the current power output with the power output at a reference point, the power generation degradation of the power plant is determined. This degradation is usually expressed as a percentage or an absolute power value, used to quantify the degree to which the power plant's performance changes over time.

[0120] This process enables an accurate assessment of the power generation degradation of photovoltaic power plants. These assessment results are of great significance to power plant operators because they not only help understand the current performance status of the power plant, but also provide a scientific basis for developing future operation and maintenance strategies, optimizing power plant performance, and planning the long-term operation of the power plant.

[0121] Step 208: Based on the initial peak power generation of the photovoltaic power station and the power generation attenuation of the photovoltaic power station in different time segments, calculate the peak power generation of the photovoltaic power station in different time segments.

[0122] Specifically, in step 208, the "initial peak power generation" is defined as the benchmark for evaluating the performance of a photovoltaic power plant. This data represents the maximum power generation that the power plant can achieve in a brand-new state, when all components are in optimal working condition and external environmental conditions (such as solar irradiance and temperature) are most ideal. This value is usually obtained through professional testing during the commissioning phase after the power plant is completed, and it is an important basis for evaluating the power plant's performance, formulating operation and maintenance strategies, and predicting future power generation.

[0123] Power generation degradation refers to the performance decline of a photovoltaic power station during actual operation due to factors such as component aging, environmental factors (e.g., ultraviolet radiation, temperature fluctuations, humidity changes), and potential mechanical stress. This degradation accumulates gradually over time, affecting the power station's power generation efficiency. To quantify this degradation, it is necessary to collect and analyze the actual power generation data of the power station at different time periods. By comparing the initial peak power generation with the current power generation, the power generation degradation rate for each time period can be calculated.

[0124] Based on the physical mechanisms of power generation degradation and actual data, a mathematical model is constructed to reflect the change in power generation capacity of a power plant over time. This model may need to consider various factors, such as component aging effects, changes in environmental factors, and adjustments to operation and maintenance strategies. Using the constructed mathematical model, combined with the initial peak power generation capacity and the power generation degradation rate over different time periods, the peak power generation capacity within each time segment is calculated. This calculation result can provide intuitive information about the change in power plant performance over time, helping to formulate more scientific operation and maintenance strategies and optimize the operating efficiency of the power plant.

[0125] Step 209: Calculate the power generation of the photovoltaic power station within a preset time period based on the average system efficiency, the peak sunshine duration, and the peak power generation.

[0126] Specifically, in step 209, based on the previously determined average system efficiency, peak sunshine duration, and calculated peak power generation, the power generation of the photovoltaic power station within the preset time period can be further derived.

[0127] Average system efficiency reflects the overall efficiency of a photovoltaic (PV) power plant in capturing energy from solar radiation and converting it into usable electrical energy. It integrates the effects of multiple factors, including PV module efficiency, inverter efficiency, line losses, dust accumulation, temperature effects, and system maintenance conditions, and is a key indicator for evaluating the long-term performance of a power plant. Peak sunshine duration defines the timeframe during which solar radiation intensity reaches or approaches its maximum for a given geographical location within a specific season or time period. This data is crucial for estimating the maximum power generation potential of a power plant, as it directly reflects how much solar energy the plant can capture. Peak power output represents the instantaneous output power of a PV power plant under ideal conditions (i.e., when solar radiation intensity is maximum and system efficiency is highest). It is calculated based on factors such as the power plant's design specifications, module type, array layout, and tracking system performance, providing a benchmark for predicting the power generation capacity of the power plant.

[0128] After mastering the above three key parameters, a power generation calculation model is adopted to combine the average system efficiency, peak sunshine duration and peak power generation, while taking into account dynamic factors in actual operation such as weather changes, cloud cover and temperature fluctuations, to accurately estimate the power generation of the photovoltaic power station within a preset period (such as day, week, month, year, etc.).

[0129] E = PR × P × h

[0130] PR=(1-η1)×(1-η2)×(…)×(1-η n )

[0131] In the formula: E—the power generation of the photovoltaic power station during a certain period, in kW-h; P—the sum of the peak power values ​​of all components of the photovoltaic power station, in kW; PR—the average system efficiency of the photovoltaic power station during the period; H—the peak sunshine hours on the array surface during the period.

[0132] It should be noted that the embodiments of this disclosure may include multiple steps. For ease of description, these steps are numbered, but these numbers are not a limitation on the execution time slots or execution order between the steps; these steps can be implemented in any order, and the embodiments of this disclosure do not limit this.

[0133] Corresponding to the above-described method for assessing internet power consumption, this invention also proposes an apparatus for assessing internet power consumption. Since the apparatus embodiments of this invention correspond to the method embodiments described above, details not disclosed in the apparatus embodiments can be referred to the method embodiments described above, and will not be repeated here.

[0134] Figure 3 is a schematic diagram of a device for evaluating internet power consumption according to an embodiment of this disclosure. As shown in Figure 3, it includes:

[0135] The first calculation unit 31 is used to calculate the average system efficiency of the photovoltaic power station based on the site environmental data, site meteorological data and project design data.

[0136] The determining unit 32 is used to analyze the meteorological data of the plant site and determine the peak sunshine duration of the photovoltaic power station;

[0137] Analysis unit 33 is used to analyze the project design data to determine the power generation attenuation of the photovoltaic power station, and to calculate the peak power generation of the photovoltaic power station using the power generation attenuation;

[0138] The second calculation unit 34 is used to calculate the power generation of the photovoltaic power station within a preset time period based on the average system efficiency, the peak sunshine duration, and the peak power generation.

[0139] This disclosure provides an apparatus for assessing grid-connected power generation. Based on site environmental data, site meteorological data, and project design data, it calculates the average system efficiency of the photovoltaic power station; analyzes the site meteorological data to determine the peak sunshine duration of the photovoltaic power station; analyzes the project design data to determine the power generation attenuation of the photovoltaic power station, and uses the power generation attenuation to calculate the peak power generation of the photovoltaic power station; and calculates the power generation of the photovoltaic power station within a preset time period based on the average system efficiency, the peak sunshine duration, and the peak power generation. Compared with related technologies, this disclosure, by comprehensively considering site environmental data, site meteorological data, and project design data, can more comprehensively reflect the actual operating status of the photovoltaic power station; by analyzing the site meteorological data, the peak sunshine duration of the photovoltaic power station can be determined; by analyzing the project design data, the power generation attenuation of the photovoltaic power station can be predicted; based on the calculation results of the average system efficiency and peak power generation, the power generation performance of the photovoltaic power station can be evaluated; and based on the above calculation results, the power generation of the photovoltaic power station within a preset time period can be accurately estimated, providing an important basis for power station power sales, revenue forecasting, and economic benefit assessment.

[0140] Furthermore, in one possible implementation of this embodiment, as shown in FIG4, the first computing unit 31 includes:

[0141] The acquisition module 311 is used to acquire the assessment accuracy of power generation assessment and determine the time granularity based on the assessment accuracy.

[0142] The first calculation module 312 is used to calculate the average system efficiency of the photovoltaic power station in different time segments according to the time granularity based on the site environmental data, the site meteorological data, and the project design data.

[0143] Furthermore, in one possible implementation of this embodiment, as shown in FIG4, the first calculation module 312 is further configured to:

[0144] The project design data is analyzed to determine the photovoltaic array efficiency, inverter conversion efficiency, line loss energy loss rate, AC grid connection efficiency, and other power regulation losses of the photovoltaic power station.

[0145] Based on the site environmental data and the site meteorological data, the unusable losses of the photovoltaic modules of the photovoltaic power station are determined;

[0146] Based on the photovoltaic array efficiency, the inverter conversion efficiency, the line loss energy loss rate, the AC grid connection efficiency, the other power regulation losses, and the unusable losses of the photovoltaic modules, the average system efficiency of the photovoltaic power station in different time segments is generated.

[0147] Furthermore, in one possible implementation of this embodiment, as shown in FIG4, the determining unit 32 includes:

[0148] The segmentation module 321 is used to segment the meteorological data of the plant site according to the time granularity to obtain the meteorological data of the photovoltaic power station in different time segments.

[0149] The first determining module 322 is used to extract sunshine data from the meteorological data of the plant site in different time segments and determine the peak sunshine time of the photovoltaic power station in different time segments.

[0150] Furthermore, in one possible implementation of this embodiment, as shown in FIG4, the analysis unit 33 includes:

[0151] Analysis module 331 is used to perform data analysis on the project design data and determine the degradation curve of the photovoltaic modules of the photovoltaic power station;

[0152] The second determining module 332 is used to calculate and determine the power generation attenuation of the photovoltaic power station based on the attenuation curve and the operating time of the photovoltaic power station.

[0153] The second calculation module 333 is used to calculate the peak power generation of the photovoltaic power station in different time segments based on the initial peak power generation of the photovoltaic power station and the power generation attenuation of the photovoltaic power station in different time segments.

[0154] Furthermore, in one possible implementation of this embodiment, as shown in FIG4, the device further includes:

[0155] The preprocessing unit 35 is used to acquire site environmental data, site meteorological data and project design data of photovoltaic power plants, and to perform data preprocessing on the site environmental data and the site meteorological data.

[0156] It should be noted that the foregoing explanation of the method embodiments also applies to the apparatus of this embodiment, and the principle is the same, so it is not limited in this embodiment.

[0157] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0158] Figure 5 illustrates a schematic block diagram of an example electronic device 400 that can be used to implement embodiments of the present disclosure. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0159] As shown in Figure 5, device 400 includes a computing unit 401, which can perform various appropriate actions and processes based on a computer program stored in ROM (Read-Only Memory) 402 or a computer program loaded from storage unit 408 into RAM (Random Access Memory) 403. RAM 403 can also store various programs and data required for the operation of device 400. The computing unit 401, ROM 402, and RAM 403 are interconnected via bus 404. I / O (Input / Output) interface 405 is also connected to bus 404.

[0160] Multiple components in device 400 are connected to I / O interface 405, including: input unit 406, such as keyboard, mouse, etc.; output unit 407, such as various types of monitors, speakers, etc.; storage unit 408, such as disk, optical disk, etc.; and communication unit 409, such as network card, modem, wireless transceiver, etc. Communication unit 409 allows device 400 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0161] The computing unit 401 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, CPUs (Central Processing Units), GPUs (Graphics Processing Units), various special-purpose AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, DSPs (Digital Signal Processors), and any suitable processor, controller, microcontroller, etc. The computing unit 401 performs the various methods and processes described above, such as the method for assessing internet battery power. For example, in some embodiments, the method for assessing internet battery power may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 408. In some embodiments, part or all of the computer program may be loaded and / or installed on device 400 via ROM 402 and / or communication unit 409. When the computer program is loaded into RAM 403 and executed by the computing unit 401, one or more steps of the methods described above may be performed. Alternatively, in other embodiments, the computing unit 401 may be configured to perform the aforementioned method of online power consumption assessment by any other suitable means (e.g., by means of firmware).

[0162] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, FPGAs (Field Programmable Gate Arrays), ASICs (Application-Specific Integrated Circuits), ASSPs (Application-Specific Standard Products), SOCs (System-on-Chips), CPLDs (Complex Programmable Logic Devices), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0163] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0164] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, RAM, ROM, EPROM (Electrically Programmable Read-Only Memory) or flash memory, optical fiber, CD-ROM (Compact Disc Read-Only Memory), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0165] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (Cathode-Ray Tube) or LCD (Liquid Crystal Display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0166] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include LANs (Local Area Networks), WANs (Wide Area Networks), the Internet, and blockchain networks.

[0167] Computer systems can include clients and servers. Clients and servers are generally geographically separated and typically interact via communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. A server can be a cloud server, also known as a cloud computing server or cloud host, a hosting product within the cloud computing service ecosystem, addressing the shortcomings of traditional physical hosts and VPS (Virtual Private Server, or simply "VPS") services, such as high management difficulty and weak business scalability. Servers can also be servers for distributed systems or servers incorporating blockchain technology.

[0168] It's important to note that artificial intelligence (AI) is the study of enabling computers to simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, and planning). It encompasses both hardware and software technologies. AI hardware technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, and big data processing. AI software technologies primarily include computer vision, speech recognition, natural language processing, machine learning / deep learning, big data processing, and knowledge graph technologies.

[0169] The various numerical designations such as "first," "second," etc., used in this disclosure are merely for ease of description and are not intended to limit the scope of the embodiments of this disclosure, nor do they indicate a sequential order.

[0170] At least one of the features described in this disclosure can also be described as one or more, and multiple features can be two, three, four or more, and this disclosure does not impose any limitations. In the embodiments of this disclosure, for a technical feature, the technical features in that technical feature are distinguished by "first", "second", "third", "A", "B", "C" and "D", etc., and there is no sequential order or size order among the technical features described by "first", "second", "third", "A", "B", "C" and "D".

[0171] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0172] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for assessing internet battery consumption, characterized in that, include: The average system efficiency of the photovoltaic power station is calculated based on the site environmental data, site meteorological data, and project design data. Analyze the meteorological data of the plant site to determine the peak sunshine duration of the photovoltaic power station; The power generation attenuation of the photovoltaic power station is determined by analyzing the project design data, and the peak power generation of the photovoltaic power station is calculated using the power generation attenuation. The power generation of the photovoltaic power station within a preset time period is calculated based on the average system efficiency, the peak sunshine duration, and the peak power generation.

2. The method according to claim 1, characterized in that, The calculation of the average system efficiency of the photovoltaic power station based on site environmental data, site meteorological data, and project design data includes: Obtain the assessment accuracy of power generation and determine the time granularity based on the assessment accuracy; Based on the site environmental data, the site meteorological data, and the project design data, the average system efficiency of the photovoltaic power station in different time segments is calculated according to the time granularity.

3. The method according to claim 2, characterized in that, The step of calculating the average system efficiency of the photovoltaic power station in different time segments according to the site environmental data, the site meteorological data, and the project design data, based on the time granularity, includes: The project design data is analyzed to determine the photovoltaic array efficiency, inverter conversion efficiency, line loss energy loss rate, AC grid connection efficiency, and other power regulation losses of the photovoltaic power station. Based on the site environmental data and the site meteorological data, the unusable losses of the photovoltaic modules of the photovoltaic power station are determined; Based on the photovoltaic array efficiency, the inverter conversion efficiency, the line loss energy loss rate, the AC grid connection efficiency, the other power regulation losses, and the unusable losses of the photovoltaic modules, the average system efficiency of the photovoltaic power station in different time segments is generated.

4. The method according to claim 1, characterized in that, The analysis of meteorological data at the plant site to determine the peak sunshine duration of the photovoltaic power station in different time segments includes: Based on the time granularity, the meteorological data of the plant site is divided into data segments to obtain the meteorological data of the photovoltaic power station in different time segments; Sunshine data are extracted from the meteorological data of the plant site in different time segments to determine the peak sunshine time of the photovoltaic power station in different time segments.

5. The method according to claim 1, characterized in that, The analysis of the project design data to determine the power generation attenuation of the photovoltaic power station, and the calculation of the peak power generation of the photovoltaic power station using the power generation attenuation, includes: Data analysis was performed on the project design data to determine the degradation curve of the photovoltaic modules in the photovoltaic power station; Based on the attenuation curve and the operating time of the photovoltaic power station, the power generation attenuation of the photovoltaic power station is calculated and determined; Based on the initial peak power generation of the photovoltaic power station and the power generation attenuation of the photovoltaic power station in different time segments, the peak power generation of the photovoltaic power station in different time segments is calculated.

6. The method according to claim 1, characterized in that, The method further includes: Acquire site environmental data, site meteorological data, and project design data for photovoltaic power plants, and perform data preprocessing on the site environmental data and site meteorological data.

7. A device for assessing internet battery usage, characterized in that, include: The first calculation unit is used to calculate the average system efficiency of the photovoltaic power station based on the site environmental data, site meteorological data, and project design data. The determining unit is used to analyze the meteorological data of the plant site to determine the peak sunshine duration of the photovoltaic power station; The analysis unit is used to analyze the project design data to determine the power generation attenuation of the photovoltaic power station, and to calculate the peak power generation of the photovoltaic power station using the power generation attenuation. The second calculation unit is used to calculate the power generation of the photovoltaic power station within a preset time period based on the average system efficiency, the peak sunshine duration, and the peak power generation.

8. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.

9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-6.

10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method according to any one of claims 1-6.

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