On-grid electric quantity evaluation method and device, electronic equipment and storage medium

Abstract

The invention provides an on-grid electric quantity evaluation method and device, electronic equipment and a storage medium, and the method comprises the steps: calculating the average system efficiency of a photovoltaic power station according to the site environment data, site meteorological data and project design data of the photovoltaic power station; analyzing the factory site meteorological data, and determining the peak sunshine duration of the photovoltaic power station; analyzing the project design data to determine the power generation attenuation of the photovoltaic power station, and calculating the peak power generation power of the photovoltaic power station by using the power generation attenuation; and based on the average system efficiency, the peak sunshine duration and the peak power generation power, calculating the power generation amount of the photovoltaic power station in the preset time period. Compared with the prior art, the embodiment of the invention comprehensively considers the factory site environment data, the factory site meteorological data and the project design data, so that the actual operation condition of the photovoltaic power station can be more comprehensively reflected; based on the calculation result, the generating capacity of the photovoltaic power station in the preset time period can be accurately estimated, and an important basis is provided for income prediction and economic benefit evaluation of the power station.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technology, and in particular to a method and device for evaluating online power consumption, an electronic device, and a storage medium. Background Art

[0002] The power generation estimation of photovoltaic power plants first depends on the assessment of local solar energy resources. This includes factors such as the intensity, duration, and seasonal and daily changes of solar radiation. Solar radiation is the main energy source for photovoltaic power generation, so its intensity and stability have a direct impact on power generation. The assessment of solar energy resources usually requires the use of professional meteorological data and satellite remote sensing technology to obtain accurate historical solar radiation data and future forecast data.

[0003] After obtaining solar resource data, the system design and component performance of the photovoltaic power plant need to be considered. This includes factors such as the type, quantity, and layout of photovoltaic components, as well as the conversion efficiency of the system. Different photovoltaic components and system designs will result in different power generation efficiencies, so these factors need to be evaluated and optimized in detail.

[0004] The power generation estimate also needs to consider environmental conditions and the comprehensive efficiency coefficient. Environmental conditions such as temperature, humidity, wind speed, etc. will affect the power generation efficiency of photovoltaic modules. The comprehensive 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 loss, and surface contamination of modules. These factors will lead to a reduction in power generation, so corresponding corrections need to be made in the estimate. Summary of the invention

[0005] The present invention provides a method and device for evaluating online power, an electronic device and a storage medium, the main purpose of which is to solve the problem that the online power of a photovoltaic power station cannot be accurately evaluated.

[0006] According to a first aspect of the present disclosure, a method for evaluating online power consumption is provided, comprising:

[0007] Calculate the average system efficiency of the photovoltaic power station based on the site environment data, site meteorological data and project design data of the photovoltaic power station;

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

[0009] Analyzing the project design data to determine power generation attenuation of the photovoltaic power station, and using the power generation attenuation to calculate the peak power generation power of the photovoltaic power station;

[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 time and the peak power generation power.

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

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

[0013] According to the plant site environmental data, the plant 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, the calculating the average system efficiency of the photovoltaic power station in different time segments according to the time granularity based on the plant site environment data, the plant site meteorological data and the project design data includes:

[0015] 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;

[0016] Determining the unusable loss of photovoltaic modules of the photovoltaic power station according to the plant site environmental data and the plant site meteorological data;

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

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

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

[0020] The sunshine data in the meteorological data of the plant site in different time segments are extracted 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 power of the photovoltaic power station includes:

[0022] Performing data analysis on the project design data to determine the attenuation curve of the photovoltaic components of the photovoltaic power station;

[0023] Calculating and determining power generation attenuation of the photovoltaic power station according to the attenuation curve and the operation time of the photovoltaic power station;

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

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

[0026] The plant site environmental data, plant site meteorological data and project design data of the photovoltaic power station are obtained, and data preprocessing is performed on the plant site environmental data and the plant site meteorological data.

[0027] According to a second aspect of the present disclosure, a device for evaluating online power consumption is provided, comprising:

[0028] A first calculation unit, configured to calculate an average system efficiency of the photovoltaic power station according to plant site environmental data, plant site meteorological data and project design data of the photovoltaic power station;

[0029] A determination unit, configured to analyze the meteorological data of the plant site to determine the peak sunshine time of the photovoltaic power station;

[0030] An analysis unit, configured to analyze the project design data to determine power generation attenuation of the photovoltaic power station, and calculate the peak power generation power 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 time and the peak power generation power.

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

[0033] An acquisition module is used to acquire the evaluation accuracy of power generation evaluation and determine the time granularity according to the evaluation 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 plant site environmental data, the plant site meteorological data and the project design data and at the time granularity.

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

[0036] 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;

[0037] Determining the unusable loss of photovoltaic modules of the photovoltaic power station according to the plant site environmental data and the plant site meteorological data;

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

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

[0040] A division module, used for dividing the plant site meteorological data according to the time granularity to obtain the plant site meteorological data of the photovoltaic power station in different time segments;

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

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

[0043] An analysis module, used to perform data analysis on the project design data to determine the attenuation curve of the photovoltaic components of the photovoltaic power station;

[0044] A second determination module is used to calculate and determine the power generation attenuation of the photovoltaic power station according to the attenuation curve and the operation time of the photovoltaic power station;

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

[0047] The preprocessing unit is used to obtain the plant site environment data, plant site meteorological data and project design data of the photovoltaic power station, and perform data preprocessing on the plant site environment data and the plant site meteorological data.

[0048] According to a third aspect of the present disclosure, there is provided an electronic device, including:

[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, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method described in the first aspect.

[0052] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute the method described in the first aspect.

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

[0054] The present disclosure provides a method and device, electronic device and storage medium for evaluating online electricity. According to the site environment data, site meteorological data and project design data of the photovoltaic power station, the average system efficiency of the photovoltaic power station is calculated; the site meteorological data is analyzed to determine the peak sunshine time of the photovoltaic power station; the project design data is analyzed to determine the power generation attenuation of the photovoltaic power station, and the peak power generation power of the photovoltaic power station is calculated by using the power generation attenuation; based on the average system efficiency, the peak sunshine time and the peak power generation power, the power generation of the photovoltaic power station within a preset time period is calculated. Compared with the related art, the embodiment of the present disclosure can more comprehensively reflect the actual operation status of the photovoltaic power station by comprehensively considering the site environment data, site meteorological data and project design data; the peak sunshine time of the photovoltaic power station can be determined by analyzing the site meteorological data; the power generation attenuation of the photovoltaic power station can be predicted by analyzing the project design data; the power generation performance of the photovoltaic power station can be evaluated based on the calculation results of the average system efficiency and the peak power generation power; based on the above calculation results, the power generation of the photovoltaic power station within the preset time period can be accurately estimated, which provides an important basis for the power sales, revenue forecast and economic benefit evaluation of the power station.

[0055] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0057] Figure 1 A flowchart of a method for evaluating online power consumption provided by an embodiment of the present disclosure;

[0058] Figure 2 A flowchart of another method for evaluating online power consumption provided by an embodiment of the present disclosure;

[0059] Figure 3 A schematic diagram of the structure of a device for evaluating online power consumption provided by an embodiment of the present disclosure;

[0060] Figure 4 A schematic diagram of the structure of another device for evaluating online power consumption provided by an embodiment of the present disclosure;

[0061] Figure 5 A schematic block diagram of an exemplary electronic device provided for an embodiment of the present disclosure. DETAILED DESCRIPTION

[0062] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

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

[0064] Figure 1 A flowchart of a method for evaluating online power consumption provided by an embodiment of the present disclosure is provided.

[0065] like Figure 1 As shown, the method comprises the following steps:

[0066] Step 101 : calculating the average system efficiency of the photovoltaic power station according to the site environment data, site meteorological data and project design data of the photovoltaic power station.

[0067] In the embodiments of the present disclosure, the site environmental data of the photovoltaic power station include but are not limited to the geographical location, topography, soil type, vegetation coverage, and the distribution of surrounding buildings or obstructions; the site meteorological data of the photovoltaic power station include but are not limited to key climate indicators such as 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 station may include but are not limited to the type and specifications of photovoltaic modules, array layout, inverter efficiency, cable line design and loss, configuration and efficiency of the energy storage system, type and performance parameters of the tracking system (if any), and the operation and maintenance strategy and maintenance cycle of the entire power station.

[0068] The average system efficiency of the photovoltaic power plant is comprehensively calculated and evaluated by using mathematical models and algorithms. This calculation process not only takes into account the direct impact of natural environmental factors on photovoltaic power generation efficiency, but also deeply analyzes the contribution and limitation of power plant design details and technical configuration to the overall efficiency, aiming to provide a comprehensive indicator reflecting the long-term operation performance of the power plant.

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

[0070] In the embodiments of the present disclosure, the peak sunshine time of the photovoltaic power station is determined by collating the meteorological data of the plant site in the area where the photovoltaic power plant is located, and by evaluating various meteorological indicators closely related to the operation of the photovoltaic power station. Specifically, by analyzing and studying the historical meteorological records at the plant site, the historical meteorological records include but are not limited to key data such as the total amount of solar radiation per day, sunshine duration, cloud coverage, atmospheric transparency, temperature change trends, and wind speed and direction. By calling a meteorological analysis algorithm or tool, these data are combined with the operating characteristics of the photovoltaic power station to determine the time period when the solar radiation intensity reaches or approaches the maximum value, that is, the "peak sunshine time".

[0071] During the analysis, the impact of seasonal changes, geographical differences, and long-term climate trends on peak sunshine hours should also be considered. For example, some areas may have longer sunshine hours and higher solar radiation intensity in the summer, while the opposite is true in the winter. In addition, the potential interference of specific weather events (such as sudden increase in cloud cover, sandstorms, etc.) on peak sunshine hours should be evaluated, and how these factors affect the actual power generation capacity of photovoltaic power plants. In this way, the sunshine hours of photovoltaic power plants can be calculated based on historical meteorological data by assigning the factors that affect sunshine hours at different weather times.

[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 power of the photovoltaic power station.

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

[0074] According to the array layout and orientation of the photovoltaic power station, and whether there is an automatic tracking system to maximize the absorption of solar radiation. Whether the array layout is reasonable, whether the spacing between components is appropriate, the accuracy and response speed of the tracking system and other factors will affect the actual power generation efficiency of the photovoltaic power station and its attenuation trend. In addition, the inverter efficiency, cable line design and loss, energy storage system configuration and charging and discharging efficiency are also links that cannot be ignored. As a key device for converting direct current into alternating current, the efficiency of the inverter directly affects the output power of the entire power station; the design of the cable line is related to the loss of electric energy during transmission; the energy storage system can provide power support when the light is insufficient, but its own charging and discharging efficiency will also affect the overall power generation efficiency.

[0075] Based on comprehensive consideration of all the above factors, the power generation attenuation curve of the photovoltaic power station is estimated by calling the simulation analysis algorithm, combining historical data and forecast analysis. This curve is used to describe the trend of power generation efficiency of the power station over time and is an important basis for evaluating the long-term performance and economic benefits of the power station.

[0076] The actual peak power generation of the photovoltaic power station is calculated 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 time.

[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 time, and the peak power generation power.

[0078] In the embodiment of the present disclosure, based on the previously determined average system efficiency, peak sunshine time and calculated peak power generation, the power generation of the photovoltaic power station within the preset time period can be further derived.

[0079] The average system efficiency reflects the overall efficiency of a photovoltaic power plant in capturing energy from solar radiation and converting it into usable electrical energy. It takes into account the influence of multiple factors such as photovoltaic module efficiency, inverter efficiency, line loss, dust accumulation, temperature effects, and system maintenance status, and is a key indicator for evaluating the long-term performance of a power plant. Peak sunshine time defines the time range when the intensity of solar radiation reaches or approaches the maximum value in a specific season or time period at the geographical location of the power plant. This data is crucial for estimating the maximum power generation potential of a power plant because it directly reflects how much solar energy the power plant can capture. Peak power generation represents the instantaneous output power of a photovoltaic power plant under ideal conditions (i.e., when the intensity of solar radiation is the highest and the system efficiency is the highest). It is calculated based on factors such as the design specifications of the power plant, the type of components, the array layout, and the performance of the tracking system, and provides a benchmark for predicting the power generation capacity of the power plant.

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

[0081] The present disclosure provides a method for evaluating the amount of electricity connected to the grid. According to the site environment data, site meteorological data and project design data of the photovoltaic power station, the average system efficiency of the photovoltaic power station is calculated; the site meteorological data is analyzed to determine the peak sunshine time of the photovoltaic power station; the project design data is analyzed to determine the power generation attenuation of the photovoltaic power station, and the peak power generation power of the photovoltaic power station is calculated by using the power generation attenuation; based on the average system efficiency, the peak sunshine time and the peak power generation power, the power generation of the photovoltaic power station within a preset time period is calculated. Compared with the related art, the embodiment of the present disclosure can more comprehensively reflect the actual operation status of the photovoltaic power station by comprehensively considering the site environment data, site meteorological data and project design data; the peak sunshine time of the photovoltaic power station can be determined by analyzing the site meteorological data; the power generation attenuation of the photovoltaic power station can be predicted by analyzing the project design data; the power generation performance of the photovoltaic power station can be evaluated based on the calculation results of the average system efficiency and the peak power generation power; based on the above calculation results, the power generation of the photovoltaic power station within the preset time period can be accurately estimated, which provides an important basis for the power sales, revenue forecast and economic benefit evaluation of the power station.

[0082] In order to clearly illustrate the embodiment of the present disclosure, this embodiment provides a flowchart of another method for evaluating online power consumption.

[0083] like Figure 2 As shown, the method comprises the following steps:

[0084] Step 201, obtaining the plant site environment data, plant site meteorological data and project design data of the photovoltaic power station, and performing data preprocessing on the plant site environment data and the plant site meteorological data.

[0085] Specifically, in step 201, in order to comprehensively evaluate the potential power generation capacity and long-term operating performance of the photovoltaic power station, it is necessary to first systematically obtain the environmental data, meteorological data and project design data of the photovoltaic power station 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.

[0086] When obtaining site environmental data, it is necessary to focus on analyzing factors such as topography, soil type, vegetation cover, hydrological conditions, and surrounding obstacles. These data are usually collected through a variety of technical means such as on-site surveys, satellite remote sensing, drone aerial photography, and geographic information systems (GIS). The acquisition of meteorological data mainly relies on meteorological observation stations, satellite cloud maps, historical meteorological records, and professional meteorological forecast models. These parameters are crucial for evaluating the power generation efficiency, component temperature effects, heat dissipation requirements, and system stability of power stations. The frequency and accuracy of meteorological data collection directly affect the accuracy of subsequent power generation forecasts and system design. Project design data covers detailed information such as component selection, array layout, inverter configuration, energy storage system design, cable line planning, and monitoring system settings of photovoltaic power stations.

[0087] After obtaining the above data, the purpose of data preprocessing is to improve data quality, eliminate noise and deviation, and lay a solid foundation for subsequent data analysis and modeling.

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

[0089] Step 202: Obtain the evaluation accuracy of the power generation evaluation, and determine the time granularity according to the evaluation accuracy.

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

[0091] The determination of evaluation accuracy is the premise for calculating the average system efficiency. It determines the accuracy level that needs to be achieved in subsequent calculations based on the operational needs of the power plant, the purpose of data analysis, and the acceptable error range. High accuracy requirements mean that we need more detailed time segmentation and more complex data processing, while low accuracy requirements allow for coarser time division and simplified data analysis methods.

[0092] After determining the evaluation accuracy, the time granularity can be set according to this requirement. Time granularity refers to the degree of refinement of time division, such as hours, days, months or years. It directly affects the data sampling frequency and calculation complexity when calculating the average system efficiency. Smaller time granularity can provide higher resolution power generation efficiency data, but it will also increase the difficulty of data processing and calculation cost; while larger time granularity simplifies the calculation process, it may sacrifice some accuracy and details.

[0093] Step 203 , based on the plant site environment data, the plant 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.

[0094] Step 2031, analyzing 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, determining the unusable loss of photovoltaic modules of the photovoltaic power station according to the plant site environmental data and the plant site meteorological data;

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

[0097] Specifically, in step 203, after the time granularity is determined, the average system efficiency of the photovoltaic power station in different time segments can be calculated according to the site environment 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 shadow loss, temperature effect, line loss, etc.) of the power station. Meteorological data is used to simulate the impact of key environmental factors such as solar radiation intensity and temperature on power generation efficiency, and environmental data and project design data are combined to evaluate the component efficiency, inverter efficiency and overall system performance of the power station.

[0098] Through detailed analysis of the project design data, the core performance parameters of the photovoltaic power station are 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 converting DC power into AC power; line loss energy loss rate, which describes the loss of electrical energy during line transmission; 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] The site environmental data and site meteorological data are used to determine the unusable losses of photovoltaic modules in photovoltaic power plants. These losses mainly include energy losses caused by factors such as shadowing, 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 photovoltaic modules can be more accurately evaluated.

[0100] After mastering the above key parameters and losses, I can start to calculate the average system efficiency of the photovoltaic power station in different time segments. This process will divide the operation time of the power station into several time segments according to the requirements of time granularity, and calculate the values ​​of the above parameters in each time segment. Then, we substitute these parameter values ​​into the calculation model and solve the average system efficiency in the time segment through iteration and optimization algorithm. This process will be repeated until we get the average system efficiency data in all time segments.

[0101] Step 204 , dividing the plant site meteorological data according to the time granularity to obtain the plant site meteorological data of the photovoltaic power station in different time segments.

[0102] Specifically, in step 204, the meteorological data of the plant site is divided according to the determined time granularity to ensure that the meteorological condition information of the photovoltaic power station in different time segments can be accurately obtained. The specific requirements of the time granularity are clarified, which determines how many time segments we divide the meteorological data into and the length of each time segment. The selection of time granularity is usually based on the operational needs of the power station, the accuracy requirements of data analysis, and the resolution of the meteorological data that can be obtained. For example, the time granularity may be divided into hours, days, weeks, or months, depending on the actual situation of the power station and the purpose of analysis.

[0103] Screening, sorting and reorganizing the original meteorological data. Allocate the meteorological data to the corresponding time segments according to the time tags to ensure that the data in each time segment is complete and continuous.

[0104] In the process of data division, special attention should be paid to some meteorological parameters that have an important impact on the power generation efficiency of photovoltaic power stations, 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 have an important impact on the overall performance of the power station by affecting the heat dissipation conditions of the power station, module temperature, and system stability. Finally, the meteorological data of the photovoltaic power station site in different time segments are obtained.

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

[0106] Specifically, in step 205, in order to deeply analyze 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 plant site meteorological data in different time segments, and determine the peak sunshine time of the photovoltaic power station in each time segment based on this.

[0107] First, the sunshine data is accurately screened from the time-segmented meteorological data. These sunshine data usually include key information such as solar radiation intensity, sunshine duration, and sunshine period, which together constitute a complete data set describing the sunshine conditions in a region. In the process of extracting sunshine data, high-precision timestamp technology is used to ensure that each sunshine data can accurately correspond to the time segment in which it is located. This not only improves the accuracy of the data. The extracted sunshine data is deeply statistically analyzed and processed. By statistically analyzing the solar radiation intensity, key indicators such as the average radiation intensity, maximum radiation intensity, and the change trend of radiation intensity in each time segment are obtained. At the same time, the sunshine conditions of the photovoltaic power station are comprehensively evaluated by combining the data of sunshine duration and sunshine period.

[0108] After determining the sunshine conditions in each time segment, the correlation between the power generation efficiency of the photovoltaic power station and the sunshine conditions is further analyzed. In the time segment with good sunshine conditions, the power generation efficiency of the photovoltaic power station is generally high, and the peak sunshine time and the peak period of power generation efficiency show an obvious positive correlation.

[0109] Based on these analysis results, the peak sunshine time of the photovoltaic power station in different time segments is determined. These data not only provide an important reference for the daily operation and maintenance of the power station, but also provide a scientific basis for the performance evaluation, optimization design and long-term planning of the power station. By deeply understanding the relationship between the sunshine conditions and power generation performance of the power station, we can more accurately predict the power generation of the power station, optimize the operation strategy of the power station, and thus improve the overall economic and social benefits of the power station.

[0110] Step 206: performing data analysis on the project design data to determine the attenuation curve of the photovoltaic components of the photovoltaic power station.

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

[0112] During the data analysis phase, we focus on extracting data directly related to the performance of PV modules, especially those that can reflect the performance changes of modules over time. This includes but is not limited to the initial power output of the module, power test results over the years or a specific time period, records of environmental factors (such as temperature, humidity, irradiance), and possible failure or maintenance records.

[0113] Statistical methods and professional software tools are used to process and analyze these data. By comparing the power output of components at different time points, the power attenuation rate of the components can be calculated, which is a key indicator for evaluating the degree of component attenuation. Furthermore, by combining environmental factors and the physical properties of components (such as materials and manufacturing processes), a mathematical model or curve of component attenuation can be constructed to more intuitively show the trend of component performance over time.

[0114] The degradation curve of a photovoltaic module is usually presented as a gradually decreasing curve, which describes the gradual reduction of the power output of the module from the initial installation to the end of its expected life. This curve is crucial for predicting the long-term power generation capacity of the power station, planning maintenance strategies, and evaluating the return on investment.

[0115] Step 207: Calculate and determine the power generation attenuation of the photovoltaic power station according to the attenuation curve and the operation time of the photovoltaic power station.

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

[0117] Select reference points: Determine one or more reference points, which usually represent the power output level at the beginning of the power plant operation or at a specific point in time. These reference points will serve as the basis for calculating the attenuation.

[0118] Calculate current power output: Based on the decay curve and the actual operation time of the power plant, estimate the power output level at the current time. This usually involves mathematical processing of the decay curve, such as interpolation or extrapolation.

[0119] Calculate the attenuation: Compare the difference between the current power output and the power output at the reference point to obtain the power generation attenuation of the power plant. This attenuation is usually expressed as a percentage or absolute power value to quantify the degree of change in power plant performance over time.

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

[0121] Step 208 : calculating 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.

[0122] Specifically, in step 208, it is clarified that the "initial peak power generation" is the benchmark for evaluating the performance of the photovoltaic power station. This data represents the maximum power generation that can be achieved when the power station is in a brand new state, all components are in the best working state, and the external environmental conditions (such as solar irradiance, temperature, etc.) are the most ideal. This value is usually obtained through professional testing during the commissioning phase after the completion of the power station construction. It is an important basis for evaluating the performance of the power station, formulating operation and maintenance strategies, and predicting future power generation.

[0123] Power generation attenuation refers to the performance degradation of a photovoltaic power station during actual operation due to component aging, environmental factors (such as ultraviolet radiation, temperature fluctuations, humidity changes, etc.), and possible mechanical stress. This attenuation will gradually accumulate over time, thus affecting the power generation efficiency of the power station. In order to quantify this attenuation, it is necessary to collect and analyze the actual power generation data of the power station in different time periods, and calculate the power generation attenuation rate of each time period by comparing the difference between the initial peak power generation and the current power generation.

[0124] Based on the physical mechanism and actual data of power generation attenuation, a mathematical model is constructed to reflect the change of power generation over time. This model may need to consider multiple factors, such as the aging effect of components, changes in environmental factors, and adjustments to operation and maintenance strategies. Using the constructed mathematical model, combined with the initial peak power generation and the power generation attenuation rate in different time periods, the peak power generation in each time segment is calculated. This calculation result can provide intuitive information about the change of power station performance over time, which is helpful to formulate more scientific operation and maintenance strategies and optimize the operation efficiency of the power station.

[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 time and the peak power generation power.

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

[0127] The average system efficiency reflects the overall efficiency of a photovoltaic power plant in capturing energy from solar radiation and converting it into usable electrical energy. It takes into account the influence of multiple factors such as photovoltaic module efficiency, inverter efficiency, line loss, dust accumulation, temperature effects, and system maintenance status, and is a key indicator for evaluating the long-term performance of a power plant. Peak sunshine time defines the time range when the intensity of solar radiation reaches or approaches the maximum value in a specific season or time period at the geographical location of the power plant. This data is crucial for estimating the maximum power generation potential of a power plant because it directly reflects how much solar energy the power plant can capture. Peak power generation represents the instantaneous output power of a photovoltaic power plant under ideal conditions (i.e., when the intensity of solar radiation is the highest and the system efficiency is the highest). It is calculated based on factors such as the design specifications of the power plant, the type of components, the array layout, and the performance of the tracking system, and provides a benchmark for predicting the power generation capacity of the power plant.

[0128] After mastering the above three key parameters, the power generation calculation model is used to combine the average system efficiency, peak sunshine time and peak power generation power, while considering dynamic factors in actual operation such as weather changes, cloud cover, temperature fluctuations, etc., to accurately estimate the power generation of the photovoltaic power station in a preset time period (such as day, week, month, year, etc.). E=PR×P×h PR=(1-η1)×(1-η2)×(…)×(1-η n )

[0129] Where: E is the power generation of the photovoltaic power station in a certain period of time, unit: kW-h; P is the sum of the peak power values ​​of all components of the photovoltaic power station, unit: kW; PR is the average system efficiency of the photovoltaic power station in this period of time; H is the peak sunshine hours on the array surface in this period of time.

[0130] It should be noted that the embodiments of the present disclosure may include multiple steps. For the convenience of description, these steps are numbered, but these numbers do not limit the execution time slots or execution order between the steps; these steps can be implemented in any order, and the embodiments of the present disclosure do not limit this.

[0131] Corresponding to the above-mentioned method for evaluating online power consumption, the present invention also provides a device for evaluating online power consumption. Since the device embodiment of the present invention corresponds to the above-mentioned method embodiment, details not disclosed in the device embodiment can be referred to the above-mentioned method embodiment, and will not be described in detail in the present invention.

[0132] Figure 3 A schematic diagram of a device for evaluating online power consumption provided by an embodiment of the present disclosure is shown in FIG. Figure 3 As shown, including:

[0133] A first calculation unit 31 is used to calculate the average system efficiency of the photovoltaic power station according to the site environment data, site meteorological data and project design data of the photovoltaic power station;

[0134] A determination unit 32, configured to analyze the meteorological data of the plant site to determine the peak sunshine time of the photovoltaic power station;

[0135] An analysis unit 33, configured to analyze the project design data to determine the power generation attenuation of the photovoltaic power station, and calculate the peak power generation power of the photovoltaic power station using the power generation attenuation;

[0136] 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 time and the peak power generation power.

[0137] The present disclosure provides a device for evaluating the amount of electricity on the grid, which calculates the average system efficiency of the photovoltaic power station according to the site environment data, site meteorological data and project design data of the photovoltaic power station; analyzes the site meteorological data to determine the peak sunshine time 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 power 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 time and the peak power generation power. Compared with the related art, the embodiment of the present disclosure can more comprehensively reflect the actual operation status of the photovoltaic power station by comprehensively considering the site environment data, site meteorological data and project design data; by analyzing the site meteorological data, the peak sunshine time 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 the peak power generation power, the power generation performance of the photovoltaic power station can be evaluated; based on the above calculation results, the power generation of the photovoltaic power station within the preset time period can be accurately estimated, providing an important basis for the power sales, revenue forecast and economic benefit evaluation of the power station.

[0138] Furthermore, in a possible implementation of this embodiment, Figure 4As shown, the first calculation unit 31 includes:

[0139] An acquisition module 311 is used to acquire the evaluation accuracy of the power generation evaluation and determine the time granularity according to the evaluation accuracy;

[0140] 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 plant site environment data, the plant site meteorological data and the project design data and at the time granularity.

[0141] Furthermore, in a possible implementation of this embodiment, Figure 4 As shown, the first calculation module 312 is also used for:

[0142] 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;

[0143] Determining the unusable loss of photovoltaic modules of the photovoltaic power station according to the plant site environmental data and the plant site meteorological data;

[0144] Based on the photovoltaic array efficiency, the conversion efficiency of the inverter, the line loss energy loss rate, the AC grid-connected efficiency, the other power regulation losses and the unusable losses of the photovoltaic components, the average system efficiency of the photovoltaic power station in different time segments is generated.

[0145] Furthermore, in a possible implementation of this embodiment, Figure 4 As shown, the determining unit 32 includes:

[0146] A division module 321 is used to divide the plant site meteorological data according to the time granularity to obtain the plant site meteorological data of the photovoltaic power station in different time segments;

[0147] The first determination module 322 is used to extract sunshine data from the plant site meteorological data in different time segments, and determine the peak sunshine time of the photovoltaic power station in different time segments.

[0148] Furthermore, in a possible implementation of this embodiment, Figure 4 As shown, the analysis unit 33 includes:

[0149] An analysis module 331 is used to perform data analysis on the project design data to determine the attenuation curve of the photovoltaic components of the photovoltaic power station;

[0150] A second determination module 332 is used to calculate and determine the power generation attenuation of the photovoltaic power station according to the attenuation curve and the operation time of the photovoltaic power station;

[0151] The second calculation module 333 is used to calculate the peak power generation power of the photovoltaic power station in different time segments based on the initial peak power generation power of the photovoltaic power station and the power generation attenuation of the photovoltaic power station in different time segments.

[0152] Furthermore, in a possible implementation of this embodiment, Figure 4 As shown, the device also includes:

[0153] The preprocessing unit 35 is used to obtain the plant site environment data, plant site meteorological data and project design data of the photovoltaic power station, and perform data preprocessing on the plant site environment data and the plant site meteorological data.

[0154] It should be noted that the above explanation of the method embodiment is also applicable to the device of this embodiment, and the principle is the same, which is not limited in this embodiment.

[0155] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0156] Figure 5 A schematic block diagram of an example electronic device 400 that can be used to implement an embodiment of the present disclosure is shown. 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 can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0157] like Figure 5 As shown, the device 400 includes a computing unit 401, which can perform various appropriate actions and processes according to a computer program stored in a ROM (Read-Only Memory) 402 or a computer program loaded from a storage unit 408 to a RAM (Random Access Memory) 403. In the RAM 403, various programs and data required for the operation of the device 400 can also be stored. The computing unit 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An I / O (Input / Output) interface 405 is also connected to the bus 404.

[0158] A number of components in the device 400 are connected to the I / O interface 405, including: an input unit 406, such as a keyboard, a mouse, etc.; an output unit 407, such as various types of displays, speakers, etc.; a storage unit 408, such as a disk, an optical disk, etc.; and a communication unit 409, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 409 allows the device 400 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0159] The computing unit 401 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a CPU (Central Processing Unit), a GPU (Graphic Processing Units), various dedicated AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, a DSP (Digital Signal Processor), and any appropriate processor, controller, microcontroller, etc. The computing unit 401 performs the various methods and processes described above, such as the method for evaluating the amount of electricity online. For example, in some embodiments, the method for evaluating the amount of electricity online may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 408. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 400 via the ROM 402 and / or the communication unit 409. When the computer program is loaded into the RAM 403 and executed by the computing unit 401, one or more steps of the method described above may be performed. Alternatively, in other embodiments, the computing unit 401 may be configured to execute the aforementioned online power consumption evaluation method in any other appropriate manner (for example, by means of firmware).

[0160] Various embodiments of the systems and techniques described above herein may 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 embodiments may include: being implemented in one or more computer programs that may be executed and / or interpreted on a programmable system including at least one programmable processor that may be a dedicated or general-purpose programmable processor that may receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0161] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0162] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. More specific examples of machine-readable storage media may include electrical connections based on one or more lines, 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.

[0163] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (Cathode-Ray Tube) or LCD (Liquid Crystal Display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the 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 acoustic input, voice input, or tactile input).

[0164] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: LAN (Local Area Network), WAN (Wide Area Network), the Internet, and blockchain networks.

[0165] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services ("Virtual Private Server", or "VPS" for short). The server may also be a server of a distributed system, or a server combined with a blockchain.

[0166] It should be noted that artificial intelligence is a discipline that studies how computers can simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.), and includes both hardware-level and software-level technologies. Artificial intelligence hardware technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, and big data processing; artificial intelligence software technologies mainly include computer vision technology, speech recognition technology, natural language processing technology, as well as machine learning / deep learning, big data processing technology, knowledge graph technology, and other major directions.

[0167] The various numerical numbers such as first and second involved in the present disclosure are only for the convenience of description and are not used to limit the scope of the embodiments of the present disclosure, but also indicate the order of precedence.

[0168] At least one in the present disclosure may also be described as one or more, and a plurality may be two, three, four or more, which is not limited in the present disclosure. In the embodiments of the present disclosure, for a technical feature, the technical features in the technical feature are distinguished by "first", "second", "third", "A", "B", "C" and "D", etc., and there is no order of precedence or size between the technical features described by the "first", "second", "third", "A", "B", "C" and "D".

[0169] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0170] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for evaluating online power consumption, characterized in that: include: Calculate the average system efficiency of the photovoltaic power station based on the site environment data, site meteorological data and project design data of the photovoltaic power station; Analyzing the meteorological data of the plant site to determine the peak sunshine time of the photovoltaic power station; Analyzing the project design data to determine power generation attenuation of the photovoltaic power station, and using the power generation attenuation to calculate the peak power generation power of the photovoltaic power station; The power generation of the photovoltaic power station within a preset time period is calculated based on the average system efficiency, the peak sunshine time and the peak power generation power.

2. The method according to claim 1, characterized in that The calculating the average system efficiency of the photovoltaic power station according to the site environment data, site meteorological data and project design data of the photovoltaic power station comprises: Obtain the assessment accuracy of power generation assessment and determine the time granularity based on the assessment accuracy; According to the plant site environmental data, the plant 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 calculating the average system efficiency of the photovoltaic power station in different time segments according to the plant site environment data, the plant site meteorological data and the project design data and the time granularity respectively includes: 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; Determining the unusable loss of photovoltaic modules of the photovoltaic power station according to the plant site environmental data and the plant site meteorological data; Based on the photovoltaic array efficiency, the conversion efficiency of the inverter, the line loss energy loss rate, the AC grid-connected efficiency, the other power regulation losses and the unusable losses of the photovoltaic components, 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 analyzing the meteorological data of the plant site to determine the peak sunshine time of the photovoltaic power station in different time segments includes: According to the time granularity, the plant site meteorological data is divided to obtain the plant site meteorological data of the photovoltaic power station in different time segments; The sunshine data in the meteorological data of the plant site in different time segments are extracted 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 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 power of the photovoltaic power station, includes: Performing data analysis on the project design data to determine the attenuation curve of the photovoltaic components of the photovoltaic power station; Calculating and determining power generation attenuation of the photovoltaic power station according to the attenuation curve and the operation time of the photovoltaic power station; Based on the initial peak power generation power of the photovoltaic power station and the power generation attenuation of the photovoltaic power station in different time segments, the peak power generation power of the photovoltaic power station in different time segments is calculated.

6. The method according to claim 1, characterized in that The method further comprises: The plant site environmental data, plant site meteorological data and project design data of the photovoltaic power station are obtained, and data preprocessing is performed on the plant site environmental data and the plant site meteorological data.

7. A device for evaluating online power consumption, characterized in that: include: A first calculation unit, configured to calculate an average system efficiency of the photovoltaic power station according to plant site environmental data, plant site meteorological data and project design data of the photovoltaic power station; A determination unit, configured to analyze the meteorological data of the plant site to determine the peak sunshine time of the photovoltaic power station; An analysis unit, configured to analyze the project design data to determine power generation attenuation of the photovoltaic power station, and calculate the peak power generation power 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 time and the peak power generation power.

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, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 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 execute the method according to any one of claims 1-6.

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

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