A method and device for evaluating light resources of a photovoltaic power station

Abstract

This application relates to the field of photovoltaic power plant solar resource assessment technology, and discloses a method and apparatus for assessing photovoltaic power plant solar resources. The method includes: acquiring first-cycle solar radiation data and first-cycle environmental parameters of the proposed photovoltaic power plant site within a first preset assessment period; identifying attenuation characteristics based on the first-cycle solar radiation data and corresponding theoretical radiation data; establishing a mapping prediction rule for predicting radiation attenuation characteristics caused by local micro-meteorological phenomena based on environmental parameters; acquiring second-cycle solar radiation data and corresponding second-cycle environmental parameters of the proposed photovoltaic power plant site within a second preset assessment period; predicting radiation attenuation characteristics caused by local micro-meteorological phenomena within the second preset assessment period based on the mapping prediction rule and second-cycle environmental parameters; and correcting the second-cycle solar radiation data based on the predicted radiation attenuation characteristics. This solves the problem that traditional assessment methods cannot capture the influence of local micro-meteorological phenomena.

Description

Technical Field

[0001] This application relates to the field of photovoltaic power plant solar resource assessment technology, and more specifically, to a method and apparatus for assessing the solar resources of a photovoltaic power plant. Background Technology

[0002] In the planning and operation of photovoltaic power plants, accurately assessing the solar resources of a site is fundamental to ensuring the project's economic benefits and the accuracy of power generation predictions. Traditional assessment methods typically rely on long-term historical meteorological data, which may originate from satellite remote sensing or distant weather stations. However, this type of macroscopic data often fails to capture the local micro-meteorological phenomena arising from the unique geographical environment of a specific site, such as fog occurring at specific times. These phenomena can significantly impact the actual solar radiation reaching the ground, leading to discrepancies between the assessed solar resources and the actual situation.

[0003] In the early development stage of a photovoltaic power plant, solar resource assessment is a crucial step in determining the investment value of the project. Project development teams typically acquire long-term historical meteorological data for the target site area. This data primarily comes from publicly available satellite remote sensing databases or national meteorological stations located tens of kilometers away from the site. Based on this meteorological data spanning ten to twenty years, technicians use specialized power generation simulation software to calculate core indicators such as the theoretical power generation and system efficiency throughout the power plant's entire lifecycle. This assessment report, based on long-term historical data, serves as the primary basis for economic analysis and financing decisions regarding the project.

[0004] However, when the project team deployed temporary ground weather stations at the proposed site for short-term on-site observations, they found that almost every day, from sunrise to around 10:00 AM, the measured total solar radiation was systematically lower than the long-term average of satellite data for the corresponding period, with the difference sometimes exceeding 15%. After 10:00 AM, the data from both sources showed a high degree of agreement. This persistent morning bias resulted in the actual accumulated solar radiation being significantly lower than initially expected.

[0005] How can we use short-term field measurement data to correct historical meteorological data spanning up to twenty years, thereby deriving a long-term power generation forecast that more closely reflects future realities and can be used for project decision-making? Simply adjusting historical data for the entire day with a fixed discount factor is impractical, as this effect is primarily concentrated in the morning, and the concentration and dissipation time of fog vary daily, potentially influenced by seasonality, humidity, wind speed, and other meteorological conditions. Directly discarding historical data and relying solely on measurements from the past few months is too short-term to reflect long-term climate fluctuations, lacks statistical representativeness, and carries significant risks. Therefore, the project urgently needs a new assessment method specifically designed to effectively correct for assessment biases caused by localized, instantaneous micrometeorological phenomena.

[0006] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this application provides a method and apparatus for assessing the solar resources of a photovoltaic power plant. The aim is to solve the technical problem that traditional solar resource assessment methods are unable to capture the impact of local micro-meteorological phenomena on solar radiation, leading to discrepancies between the assessment results and the actual situation.

[0008] Firstly, this application provides a method for assessing the solar resources of a photovoltaic power plant, including: Obtain the first-cycle solar radiation data and first-cycle environmental parameters of the proposed photovoltaic power plant site within the first preset assessment period; Based on the solar radiation data of the first cycle and the theoretical radiation data of the corresponding time, the attenuation characteristics of the radiation attenuation period caused by local micrometeorological phenomena are identified. Analyze the correlation between the decay characteristics during the radiation decay period and the environmental parameters of the first cycle, and extract the correlation information; Based on the correlation information, a mapping prediction rule is established to predict the radiation attenuation characteristics caused by local micrometeorological phenomena according to environmental parameters. The solar radiation data and corresponding environmental parameters of the proposed photovoltaic power plant site within the second preset evaluation period are obtained. The second preset evaluation period is longer than the first preset evaluation period. Based on the mapping prediction rule and the environmental parameters of the second period, the radiation attenuation characteristics caused by local micro-meteorological phenomena within the second preset evaluation period are predicted. The solar radiation data of the second period are corrected based on the predicted radiation attenuation characteristics. Based on the corrected second-cycle solar radiation data, typical meteorological year data reflecting the site's illumination conditions are generated to predict the power generation of the proposed photovoltaic power plant site.

[0009] Furthermore, in some preferred embodiments, the local micrometeorological phenomenon is low-altitude fog that occurs in the early morning due to the topography and reservoir environment of the proposed photovoltaic power station site.

[0010] Furthermore, the steps for identifying the attenuation characteristics of radiation attenuation periods caused by local micrometeorological phenomena include: For each preset morning period within the first preset evaluation cycle, based on the geographical location and time information of the proposed photovoltaic power station site, the theoretical radiation value at each time point within each preset morning period is calculated, and the relative attenuation between the first cycle solar radiation value and the theoretical radiation value at each time point within each preset morning period is calculated. Continuous time points where the relative attenuation level is higher than a first preset threshold are identified as radiation attenuation periods, where the first preset threshold is a preset relative radiation attenuation threshold caused by local micrometeorological phenomena. Record the attenuation characteristics of the radiation attenuation period. These attenuation characteristics include at least one or more of the following: the start and end times of the radiation attenuation period, the maximum value of the relative attenuation degree, and the curve shape of the relative attenuation degree changing over time.

[0011] Furthermore, the steps for analyzing the correlation between the attenuation characteristics during the radiation attenuation period and the environmental parameters of the first cycle, and extracting the correlation information, include: Local environmental parameters are extracted from the environmental parameters of the first cycle based on the radiation decay period; The extracted local environmental parameters are correlated with the attenuation characteristics during the radiation attenuation period to form a correlation data pair that reflects the correspondence between the local environmental parameters and the attenuation characteristics, which serves as the correlation information.

[0012] Furthermore, based on the associated information, the steps for establishing a mapping prediction rule for predicting the radiation attenuation characteristics caused by local micrometeorological phenomena according to environmental parameters include: Based on the local environmental parameters in the associated data pairs, a threshold for judging local environmental parameters used to predict radiation attenuation characteristics is determined. Based on local environmental parameters and their corresponding attenuation characteristics, a mapping relationship between the local environmental parameter judgment threshold and the radiation attenuation characteristics is established as a mapping prediction rule. The radiation attenuation characteristics include the start and end times of the radiation attenuation period in which the radiation attenuation characteristics are located, the maximum value of the relative attenuation degree corresponding to the radiation attenuation characteristics, and the curve shape of the relative attenuation degree changing with time.

[0013] Furthermore, the steps for predicting the radiation attenuation characteristics caused by local micrometeorological phenomena within the second preset assessment period based on mapping prediction rules and second-period environmental parameters include: The environmental parameters of the second cycle are compared with the local environmental parameter judgment threshold in the mapping prediction rule; If the environmental parameters of the second period reach the local environmental parameter judgment threshold, the radiation attenuation characteristics of the environmental parameters of the second period within the second preset evaluation period are determined according to the mapping prediction rule.

[0014] Furthermore, the steps for correcting the second-cycle solar radiation data based on the predicted radiation attenuation characteristics include: Based on the start and end times of the radiation attenuation period in which the radiation attenuation feature is located, the maximum value of the relative attenuation degree corresponding to the radiation attenuation feature, and the curve shape of the radiation attenuation degree changing with time, the function shape, maximum attenuation amplitude, and time interval of the attenuation factor function that dynamically changes with the attenuation process are determined. The time interval of the function is determined by the start and end times of the radiation attenuation period in which the radiation attenuation feature is located, the function shape is determined by the curve shape of the relative attenuation degree changing with time, and the maximum attenuation amplitude is determined by the maximum value of the relative attenuation degree corresponding to the radiation attenuation feature. The solar radiation data of the second period during the radiation decay period is corrected using the attenuation factor function to obtain the corrected solar radiation data of the second period.

[0015] Furthermore, the steps for generating typical meteorological year data reflecting site illumination conditions based on the corrected second-cycle solar radiation data include: Based on the corrected second-cycle solar radiation data, a representative month's data is selected for each month within the second preset evaluation cycle; Data from twelve representative months were combined in chronological order to generate typical meteorological year data.

[0016] Furthermore, when the proposed photovoltaic power plant site is located in a basin environment with different micro-topographic features, after obtaining the second-cycle solar radiation data and corresponding second-cycle environmental parameters of the proposed photovoltaic power plant site within the second preset evaluation period, the method further includes: The area where the proposed photovoltaic power station site is located is divided into multiple sub-regions with different micro-topographic features. The climate parameter deviation characteristics between each sub-region and the on-site meteorological station under the preset meteorological conditions are preset. For each sub-region, the environmental parameters of the second cycle are corrected based on the characteristics of the microclimate parameter deviation, thus obtaining the sub-regional environmental parameters for each sub-region. Based on the mapping prediction rules and the prediction of sub-regional environmental parameters, the radiation attenuation characteristics caused by local micro-meteorological phenomena are predicted within the second preset evaluation period. The sub-regional solar radiation data are corrected based on the predicted radiation attenuation characteristics to obtain the corrected sub-regional solar radiation data. Based on the spatial distribution of each sub-region within the proposed photovoltaic power plant site, the corrected solar radiation data of the sub-regions are integrated to generate a radiation dataset for the proposed photovoltaic power plant site. Based on the radiation dataset, typical meteorological year data are generated.

[0017] Secondly, this application also discloses a photovoltaic power plant solar resource assessment device for performing the above-mentioned photovoltaic power plant solar resource assessment method, the device comprising: The data acquisition module is used to acquire the first-cycle solar radiation data and first-cycle environmental parameters of the proposed photovoltaic power plant site within the first preset assessment period. The radiation attenuation identification module is used to identify the attenuation characteristics of the radiation attenuation period caused by local micrometeorological phenomena, based on the solar radiation data of the first cycle and the theoretical radiation data of the corresponding time. The correlation information extraction module is used to analyze the correlation between the attenuation characteristics and the environmental parameters of the first cycle during the radiation attenuation period and extract the correlation information. The mapping prediction rule establishment module is used to establish mapping prediction rules based on correlation information to predict the radiation attenuation characteristics caused by local micrometeorological phenomena according to environmental parameters. The data correction module is used to obtain the second-cycle solar radiation data and corresponding second-cycle environmental parameters of the proposed photovoltaic power plant site within the second preset evaluation period. The second preset evaluation period is longer than the first preset evaluation period. Based on the mapping prediction rule and the second-cycle environmental parameters, the module predicts the radiation attenuation characteristics caused by local micro-meteorological phenomena within the second preset evaluation period. Based on the predicted radiation attenuation characteristics, the module corrects the second-cycle solar radiation data. The typical meteorological year data generation module is used to generate typical meteorological year data reflecting the site illumination conditions based on the corrected second-cycle solar radiation data, in order to predict the power generation of the proposed photovoltaic power plant site.

[0018] The photovoltaic power plant solar resource assessment method disclosed in this application identifies and quantifies the attenuation effect of local micrometeorological phenomena, such as morning fog, on solar radiation, and establishes a mapping relationship between environmental parameters and attenuation characteristics. This application can predict and correct deviations in historical solar radiation data caused by local micrometeorological phenomena based on long-term environmental parameters. This correction is not a simple adjustment of fixed coefficients, but a dynamic correction based on actual observation and correlation analysis, which can more accurately reflect the true illumination conditions of the site. This application can integrate short-term high-precision measured data and long-term satellite historical data, specifically targeting and effectively correcting assessment deviations caused by local, instantaneous micrometeorological phenomena. This makes the final typical meteorological year data more accurate, thereby significantly improving the accuracy of photovoltaic power plant power generation prediction, providing a more reliable basis for project investment decisions and operation management, overcoming the shortcomings of existing technologies where assessment results deviate significantly from actual conditions, and has significant practical value and economic benefits. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating a photovoltaic power plant solar resource assessment method provided in an embodiment of this application.

[0020] Figure 2 This is a schematic diagram of the structure of a photovoltaic power plant light resource assessment device provided in an embodiment of this application.

[0021] Labeling Explanation: 210, Data Acquisition Module; 220, Radiation Attenuation Identification Module; 230, Correlation Information Extraction Module; 240, Mapping Prediction Rule Establishment Module; 250, Data Correction Module; 260, Typical Meteorological Year Data Generation Module. Detailed Implementation

[0022] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0023] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0024] Traditional methods for assessing solar resource resources for photovoltaic (PV) power plants typically rely on macroscopic historical meteorological data, such as satellite remote sensing data or data from distant weather stations. However, this type of data often fails to capture localized micrometeorological phenomena arising from the unique geographical environment of a specific site, such as low-altitude fog at certain times. These phenomena significantly impact the actual solar radiation reaching the ground, leading to discrepancies between the assessed solar resource resources and the actual situation. Failure to address these issues will result in inaccurate predictions of PV power plant power generation, affecting project economic benefit assessments and investment decisions.

[0025] Regarding this, firstly, see... Figure 1 This application proposes a method for assessing the solar resources of a photovoltaic power plant, including: S1. Obtain the first-cycle solar radiation data and first-cycle environmental parameters of the proposed photovoltaic power plant site within the first preset assessment period; S2. Based on the solar radiation data of the first cycle and the theoretical radiation data of the corresponding time, identify the attenuation characteristics of the radiation attenuation period caused by local micrometeorological phenomena. S3. Analyze the correlation between the attenuation characteristics during the radiation attenuation period and the environmental parameters of the first cycle, and extract the correlation information; S4. Based on the correlation information, establish a mapping prediction rule for predicting the radiation attenuation characteristics caused by local micrometeorological phenomena according to environmental parameters. S5. Obtain the second-cycle solar radiation data and corresponding second-cycle environmental parameters of the proposed photovoltaic power station site within the second preset evaluation period. The second preset evaluation period is longer than the first preset evaluation period. Based on the mapping prediction rule and the second-cycle environmental parameters, predict the radiation attenuation characteristics caused by local micro-meteorological phenomena within the second preset evaluation period. Correct the second-cycle solar radiation data based on the predicted radiation attenuation characteristics. S6. Generate typical meteorological year data reflecting the site illumination conditions based on the corrected second-cycle solar radiation data to predict the power generation of the proposed photovoltaic power plant site.

[0026] The first pre-set assessment period refers to the time frame for short-term on-site data collection, typically several months to a year, aimed at capturing short-term patterns of local micrometeorological phenomena. The second pre-set assessment period refers to the time frame for long-term solar resource assessment, typically several years or even decades, aimed at reflecting long-term climate fluctuations and trends. Local micrometeorological phenomena refer to meteorological phenomena with a small impact range and short duration caused by the unique geographical environment of a proposed photovoltaic power station site, such as low-altitude fog in the early morning. Theoretical radiation data refers to the solar radiation value calculated based on the site's geographical location and time under ideal atmospheric conditions such as cloudless and fog-free conditions. Radiation attenuation period refers to the continuous time period during which the actual solar radiation value is significantly lower than the theoretical radiation value due to local micrometeorological phenomena. Attenuation characteristics refer to parameters describing the change in radiation intensity within the radiation attenuation period, such as the start and end times of the attenuation period, the maximum value of the relative attenuation, and the curve shape of the relative attenuation over time. Environmental parameters refer to meteorological elements related to the occurrence and development of local micrometeorological phenomena, such as temperature, humidity, wind speed, and air pressure. Mapping prediction rules refer to mathematical or logical relationships established between environmental parameters and radiation attenuation characteristics, used to predict radiation attenuation characteristics based on environmental parameters. Typical meteorological year data refers to representative meteorological data for one year generated through statistical analysis and screening of long-term meteorological data, used for predicting power generation of photovoltaic power plants.

[0027] This application first obtains the first-cycle solar radiation data and first-cycle environmental parameters of the proposed photovoltaic power plant site within a first preset evaluation period. The first-cycle solar radiation data can be obtained through short-term on-site measurements using a high-precision solar radiation sensor deployed at the proposed site; for example, a solar intensity meter can be deployed to record the total radiation value at a frequency of once per minute. The first-cycle environmental parameters can be obtained through synchronous measurements using a weather station deployed at the proposed site; for example, a small weather station can be deployed to synchronously record data such as temperature, humidity, wind speed, and air pressure. This data provides a foundation for subsequent identification of local micrometeorological phenomena.

[0028] Next, based on the first-cycle solar radiation data and the corresponding theoretical radiation data, the attenuation characteristics of radiation attenuation periods caused by local micrometeorological phenomena are identified. Theoretical radiation data can be calculated using standard solar position algorithms and atmospheric models. For example, the solar altitude angle and azimuth angle at each moment can be calculated based on the site's latitude, longitude, altitude, and date and time, and combined with a standard atmospheric transmittance model, the solar radiation value under ideal conditions can be calculated. By comparing the measured first-cycle solar radiation data with the theoretical radiation data, deviations between the two can be identified. When the measured value is significantly lower than the theoretical value, it indicates that radiation attenuation may exist. For example, the relative attenuation degree between the measured and theoretical values ​​can be calculated; when the relative attenuation degree exceeds a certain preset threshold, it can be identified as a radiation attenuation period. Attenuation characteristics can include the start and end times of the radiation attenuation period, the maximum value of the relative attenuation degree, and the curve shape of the relative attenuation degree changing over time. For example, it can be recorded when the fog begins to appear after sunrise, when it completely dissipates, the percentage of radiation attenuation when the fog is at its densest, and the curve shape of the radiation attenuation degree gradually decreasing until it disappears over time.

[0029] Then, the correlation between the attenuation characteristics during the radiation attenuation period and the environmental parameters of the first cycle is analyzed to extract the correlation information. For example, for each identified radiation attenuation period, environmental parameters such as average temperature, average humidity, and average wind speed can be extracted. Subsequently, these local environmental parameters are correlated with the corresponding attenuation characteristics to form correlation data pairs. These correlation data pairs constitute the correlation information, revealing the environmental conditions for the occurrence and development of local micrometeorological phenomena.

[0030] Based on correlated information, a mapping prediction rule is established to predict the radiation attenuation characteristics caused by local micrometeorological phenomena according to environmental parameters. This mapping prediction rule can take various forms, such as statistical regression models, machine learning models like decision trees, neural networks, or expert rule systems. For example, based on the local environmental parameters in the correlated data pairs, a local environmental parameter judgment threshold for predicting radiation attenuation characteristics can be determined. For instance, it can be set that when humidity is greater than a certain threshold and wind speed is less than a certain threshold, fog attenuation is predicted. Then, based on the local environmental parameters and their corresponding attenuation characteristics, a mapping relationship between the local environmental parameter judgment threshold and the radiation attenuation characteristics is established. For example, a rule table can be created that shows the start time, end time, maximum attenuation degree, and attenuation curve shape of the corresponding radiation attenuation period when humidity and wind speed are within a certain range.

[0031] Next, the second-cycle solar radiation data and corresponding second-cycle environmental parameters for the proposed photovoltaic power plant site are obtained within the second preset assessment period. The second preset assessment period is typically much longer than the first, for example, ten or twenty years. The second-cycle solar radiation data and second-cycle environmental parameters can be derived from long-term satellite remote sensing data or historical data from distant meteorological stations. Based on mapping prediction rules and the second-cycle environmental parameters, the radiation attenuation characteristics caused by local micrometeorological phenomena within the second preset assessment period are predicted. For example, for each day within the second preset assessment period, based on environmental parameters such as morning humidity and wind speed, the established mapping prediction rules are used to determine whether local micrometeorological phenomena will occur and predict their attenuation characteristics. The second-cycle solar radiation data is then corrected based on the predicted radiation attenuation characteristics. For example, if fog attenuation is predicted to occur on a certain morning, the second-cycle solar radiation data for that period is correspondingly reduced based on the predicted attenuation characteristic start time, end time, and attenuation curve, thus obtaining the corrected second-cycle solar radiation data.

[0032] Finally, based on the corrected second-cycle solar radiation data, typical meteorological year data reflecting the site's illumination conditions is generated to predict the power generation of the proposed photovoltaic power plant site. Typical meteorological year data can be obtained through statistical analysis and selection of corrected long-term solar radiation data. For example, from the corrected ten- or twenty-year data, the most representative month's data can be selected for each month—for example, the month whose average radiation and radiation distribution are closest to the long-term average. Combining the selected twelve representative months in chronological order generates typical meteorological year data. Using this typical meteorological year data, which includes the influence of local micrometeorological phenomena, the long-term power generation of the photovoltaic power plant can be predicted more accurately, providing a more reliable basis for project investment decisions.

[0033] This application's assessment method effectively addresses the problem of solar resource assessment bias caused by the inability of traditional methods to capture local microclimate phenomena by combining short-term, high-precision measured data with long-term historical data. Traditional methods rely solely on macroscopic data, ignoring the site-specific microclimate influences, leading to overly optimistic or pessimistic power generation predictions. This application establishes a mapping prediction rule between local environmental parameters and radiation attenuation characteristics, enabling the application of the influence patterns of short-term observed local microclimate phenomena to long-term historical meteorological data, thereby correcting the long-term data. This correction not only considers whether attenuation occurs but also finely considers the timing, magnitude, and curve shape of attenuation, making the corrected solar resource data closer to reality.

[0034] Compared to existing technologies, the core innovation of this application lies in its ability to identify, quantify, and predict radiation attenuation caused by local micrometeorological phenomena, and integrate this into long-term solar resource assessments. For example, in traditional assessments, if a site experiences morning fog, but this is not reflected in satellite data or distant weather station data, the typical meteorological year data generated based on this data will overestimate the actual solar resources at the site. This application identifies the attenuation characteristics of fog through short-term measured data and establishes its correlation with environmental parameters. It then uses long-term environmental parameter data to predict the occurrence and impact of fog, correcting long-term solar radiation data. As a result, the generated typical meteorological year data will more accurately reflect the true solar conditions at the site, making power generation predictions more precise. This method not only improves the accuracy of solar resource assessments but also provides more reliable data support for the planning, design, and operation of photovoltaic power plants, reducing project investment risks and improving economic benefits.

[0035] Furthermore, the local micro-meteorological phenomenon is low-altitude fog that appears in the early morning due to the topography and reservoir environment of the proposed photovoltaic power station site.

[0036] Low-altitude fog refers to the condensation of water vapor near the ground, characterized by reduced visibility, and typically occurs in the early morning. For proposed photovoltaic power plant sites, if they are located near reservoirs or in specific basin or valley terrain, the surface radiative cooling effect is significant at night, making it easy for water vapor to condense and form low-altitude fog in the early morning. This fog significantly blocks solar radiation, resulting in a substantial decrease in the amount of sunlight received by the photovoltaic modules.

[0037] This application clarifies the local micrometeorological phenomenon as early morning low-altitude fog caused by topography and reservoir environment, enabling more targeted identification of radiation attenuation, analysis of attenuation characteristics, and establishment of mapping prediction rules. Once the specific type of micrometeorological phenomenon is identified, environmental parameters related to it, such as humidity, temperature, wind speed, and reservoir water level, can be collected more precisely, and their correlation with radiation attenuation characteristics during the formation, development, and dissipation of the fog can be analyzed specifically. This clarification helps improve the accuracy of attenuation characteristic identification and allows the prediction model to better capture the patterns of this specific phenomenon.

[0038] Through the above technical solution, this application can specifically address the problem of solar radiation attenuation caused by low-altitude fog in the early morning at proposed photovoltaic power plant sites in specific geographical environments, such as those near reservoirs or basins. This clear definition of the phenomenon makes the solar resource assessment method more applicable and accurate, avoiding errors that may arise from generalizing various complex micrometeorological phenomena. This improves the accuracy of photovoltaic power plant power generation prediction and provides more reliable data support for project investment decisions.

[0039] Furthermore, based on the solar radiation data of the first cycle and the theoretical radiation data at the corresponding time, the steps to identify the attenuation characteristics of the radiation attenuation period caused by local micrometeorological phenomena include: For each preset morning period within the first preset evaluation cycle, based on the geographical location and time information of the proposed photovoltaic power station site, the theoretical radiation value at each time point within each preset morning period is calculated, and the relative attenuation between the first cycle solar radiation value and the theoretical radiation value at each time point within each preset morning period is calculated. Continuous time points where the relative attenuation level is higher than a first preset threshold are identified as radiation attenuation periods, where the first preset threshold is a preset relative radiation attenuation threshold caused by local micrometeorological phenomena. Record the attenuation characteristics during the radiation attenuation period. The attenuation characteristics include at least one or more of the following: the start and end times of the radiation attenuation period, the maximum value of the relative attenuation degree, and the curve shape of the relative attenuation degree changing over time.

[0040] The preset early morning period can be understood as the specific time of day when solar radiation typically begins to increase, such as from sunrise to 10:00 AM. During this period, local micrometeorological phenomena, such as low-altitude fog, are more likely to have a significant impact on solar radiation. Specifically, this time period is designed to focus on the period when micrometeorological phenomena are frequent and have a significant impact, thereby improving the targeting and accuracy of attenuation identification.

[0041] The theoretical radiation value refers to the amount of solar radiation reaching the Earth's surface under ideal clear-sky conditions, i.e., without clouds, aerosols, or local micrometeorological phenomena. This theoretical radiation value can be accurately calculated using established solar radiation models, combined with the site's geographical coordinates such as latitude, longitude, altitude, date, and specific time. In practical applications, physical or empirical models can be used to calculate this value, providing a benchmark to measure the degree of attenuation in actual radiation.

[0042] The calculation of relative attenuation aims to quantify the difference between observed solar radiation and theoretical radiation. Specifically, it can be calculated as the percentage difference between the theoretical radiation value and the observed solar radiation value in the first cycle, relative to the theoretical radiation value. This provides a standardized indicator for comparing attenuation at different time points and radiation levels. A first preset threshold is a key parameter used to determine whether significant radiation attenuation caused by local micrometeorological phenomena exists. This threshold can be set based on historical data analysis, expert experience, or the characteristics of a specific site. For example, when the relative attenuation exceeds 5% or 10%, radiation attenuation caused by local micrometeorological phenomena is considered to exist. This distinguishes between normal fluctuations and significant attenuation caused by micrometeorological phenomena. A radiation attenuation period refers to a series of consecutive time points where the relative attenuation remains above the first preset threshold. Identifying these consecutive time points helps to accurately define the duration of the influence of micrometeorological phenomena.

[0043] Attenuation characteristics provide a comprehensive description of the phenomena during the radiation attenuation period. These characteristics may include, but are not limited to, the start and end times of attenuation, which define the duration of the attenuation event; the maximum value of the relative attenuation degree, which reflects the severity of attenuation; and the curve shape of the relative attenuation degree over time, which describes the dynamic change pattern of the attenuation process, such as rapid attenuation followed by slow recovery, or continuous and stable attenuation. These characteristics provide multi-dimensional information to enable a more comprehensive understanding and prediction of radiation attenuation phenomena.

[0044] This application, by comparing the actually observed first-cycle solar radiation data with theoretical radiation data at the same geographical location and time, can accurately quantify the solar radiation attenuation caused by local micrometeorological phenomena. By calculating the relative attenuation degree and comparing it with a preset first threshold, the radiation attenuation periods affected by micrometeorological phenomena can be objectively identified. Furthermore, by recording the start and end times, maximum relative attenuation degree, and the curve shape of the relative attenuation degree over time—all attenuation characteristics—the impact of local micrometeorological phenomena on solar radiation can be comprehensively and meticulously characterized, providing a solid data foundation for subsequent analysis of its correlation with environmental parameters.

[0045] Furthermore, the steps for analyzing the correlation between the attenuation characteristics during the radiation attenuation period and the environmental parameters of the first cycle, and extracting the correlation information, include: Local environmental parameters are extracted from the environmental parameters of the first cycle based on the radiation decay period; The extracted local environmental parameters are correlated with the attenuation characteristics during the radiation attenuation period to form a correlation data pair that reflects the correspondence between the local environmental parameters and the attenuation characteristics, which serves as the correlation information.

[0046] Specifically, when extracting local environmental parameters from the first-cycle environmental parameters based on the radiation decay period, local environmental parameters refer to environmental data that are directly related to or may affect the radiation decay phenomenon. For example, if the radiation decay period mainly occurs in the early morning, environmental parameters such as temperature, humidity, wind speed, wind direction, air pressure, and dew point temperature can be extracted during the early morning. This extraction method aims to focus on environmental conditions corresponding to the occurrence period of local micrometeorological phenomena, thereby improving the relevance of the correlation analysis.

[0047] The process involves associating extracted local environmental parameters with attenuation characteristics within a specific radiation attenuation period to form correlated data pairs reflecting the relationship between these parameters and attenuation characteristics. These correlated records can be stored by mapping the attenuation characteristics of each radiation attenuation event (e.g., start time, end time, maximum relative attenuation, attenuation curve shape) to the corresponding local environmental parameters for that event's occurrence period. Each correlated data pair can be a data structure containing one or more local environmental parameter fields and one or more attenuation characteristic fields, clearly representing the observed radiation attenuation characteristics under specific local environmental conditions.

[0048] This application, in analyzing the correlation between attenuation characteristics during the radiation attenuation period and environmental parameters of the first cycle, first precisely extracts local environmental parameters from the first cycle environmental parameters based on the radiation attenuation period, ensuring a high temporal correlation between the analyzed environmental data and the actual radiation attenuation phenomenon. It is precisely this targeted extraction of local environmental parameters that enables the subsequent correlation and recording of these local environmental parameters with the attenuation characteristics during the radiation attenuation period, forming more accurate and meaningful data pairs. This effectively reveals the intrinsic connection between the unique environmental conditions of local micrometeorological phenomena and the resulting radiation attenuation characteristics, laying a solid foundation for establishing accurate mapping and prediction rules.

[0049] Furthermore, based on the associated information, the steps for establishing a mapping prediction rule for predicting the radiation attenuation characteristics caused by local micrometeorological phenomena according to environmental parameters include: Based on the local environmental parameters in the associated data pairs, a threshold for judging local environmental parameters used to predict radiation attenuation characteristics is determined. Based on local environmental parameters and their corresponding attenuation characteristics, a mapping relationship between the local environmental parameter judgment threshold and the radiation attenuation characteristics is established as a mapping prediction rule. The radiation attenuation characteristics include the start and end times of the radiation attenuation period in which the radiation attenuation characteristics are located, the maximum value of the relative attenuation degree corresponding to the radiation attenuation characteristics, and the curve shape of the relative attenuation degree changing with time.

[0050] Specifically, the local environmental parameter judgment threshold refers to a series of critical values ​​used to distinguish different attenuation scenarios, defined based on the distribution of local environmental parameters in historical correlation data and their correspondence with radiation attenuation characteristics. For example, when the local environmental parameter is humidity, different humidity thresholds can be set to determine the probability and intensity of fog formation. These thresholds can be determined based on statistical analysis, expert experience, or machine learning methods, discretizing or segmenting continuously changing local environmental parameters to facilitate the establishment of a clear mapping relationship. Establishing the mapping relationship between the local environmental parameter judgment threshold and radiation attenuation characteristics can be understood as constructing a decision model or lookup table. This model or lookup table can output the corresponding radiation attenuation characteristics based on whether the input local environmental parameter reaches a specific judgment threshold. The radiation attenuation characteristics specifically include the start and end times of the radiation attenuation period, the maximum value of the relative attenuation degree, and the curve shape of the relative attenuation degree changing over time. For example, when humidity is above a certain threshold and wind speed is below a certain threshold, it may correspond to morning fog. Its attenuation characteristics are manifested in the fact that, during a specific period such as 6:00-8:00, the relative attenuation reaches a maximum of 30%, and the attenuation curve shows a pattern of rapid decline followed by slow recovery. This provides a structured and quantifiable set of rules for subsequent radiation attenuation prediction.

[0051] This application first determines a series of local environmental parameter judgment thresholds based on historical correlation data pairs, thereby effectively classifying and quantifying complex environmental conditions. Subsequently, based on these local environmental parameters and their corresponding attenuation characteristics, a mapping relationship is established between the judgment thresholds and specific radiation attenuation characteristics. This structured approach ensures that the prediction rules are no longer simple statistical fitting, but rather directly correspond to specific, quantifiable radiation attenuation characteristics based on clear environmental condition judgment criteria, thus significantly improving the accuracy and interpretability of predictions. In this way, the influence patterns of local micrometeorological phenomena on solar radiation under different environmental conditions can be captured more precisely.

[0052] Furthermore, the steps for predicting the radiation attenuation characteristics caused by local micrometeorological phenomena within the second preset assessment period based on mapping prediction rules and second-period environmental parameters include: The environmental parameters of the second cycle are compared with the local environmental parameter judgment threshold in the mapping prediction rule; If the environmental parameters of the second period reach the local environmental parameter judgment threshold, the radiation attenuation characteristics of the environmental parameters of the second period within the second preset evaluation period are determined according to the mapping prediction rule.

[0053] The process involves comparing the second-cycle environmental parameters with local environmental parameter judgment thresholds in the mapping prediction rules to determine whether the environmental conditions at the proposed photovoltaic power plant site meet the conditions for the occurrence of local micro-meteorological phenomena within the second preset evaluation period. For example, the local environmental parameter judgment thresholds may include specific ranges or critical values ​​for meteorological parameters such as temperature, humidity, and wind speed. When second-cycle environmental parameters, such as humidity or temperature in the early morning, reach or exceed the preset judgment thresholds, it is considered that there are potential conditions for the occurrence of local micro-meteorological phenomena. If the second-cycle environmental parameters reach the local environmental parameter judgment thresholds, the radiation attenuation characteristics corresponding to the second-cycle environmental parameters within the second preset evaluation period are determined according to the mapping prediction rules. This means that once environmental conditions are determined to potentially lead to local micro-meteorological phenomena, the system will use pre-established mapping prediction rules to find or calculate the corresponding radiation attenuation characteristics based on the current environmental parameters. Radiation attenuation characteristics may include the start and end times of the radiation attenuation period, the maximum value of the relative attenuation degree, and the curve shape of the relative attenuation degree changing over time. These characteristics will be used to subsequently correct solar radiation data.

[0054] This application first performs conditional judgments on the environmental parameters of the second period, ensuring that radiation attenuation characteristics are predicted only when environmental conditions meet the criteria for the occurrence of local micrometeorological phenomena. This conditional prediction mechanism avoids unnecessary calculations and improves prediction accuracy. By comparing the actual environmental parameters with preset judgment thresholds, periods potentially affected by local micrometeorological phenomena can be effectively screened. Once the conditions are met, a mapping prediction rule based on historical data is used to directly obtain the radiation attenuation characteristics corresponding to the current environmental parameters, thereby achieving accurate prediction of radiation attenuation characteristics caused by local micrometeorological phenomena.

[0055] Furthermore, the steps for correcting the second-cycle solar radiation data based on the predicted radiation attenuation characteristics include: Based on the start and end times of the radiation attenuation period in which the radiation attenuation feature is located, the maximum value of the relative attenuation degree corresponding to the radiation attenuation feature, and the curve shape of the relative attenuation degree changing with time, the function shape, maximum attenuation amplitude, and time interval of the attenuation factor function that dynamically changes with the attenuation process are determined. The time interval of the function is determined by the start and end times of the radiation attenuation period in which the radiation attenuation feature is located, the function shape is determined by the curve shape of the relative attenuation degree changing with time, and the maximum attenuation amplitude is determined by the maximum value of the relative attenuation degree corresponding to the radiation attenuation feature. The solar radiation data of the second period during the radiation decay period is corrected using the attenuation factor function to obtain the corrected solar radiation data of the second period.

[0056] Specifically, when determining the attenuation factor function that dynamically changes over time during the attenuation process, firstly, the function's effective time interval is precisely set between the start and end times of the radiation attenuation period in which the radiation attenuation characteristics are located. For example, if the predicted attenuation period is from 6:00 AM to 8:00 AM, the function will only be effective during this time period. Secondly, the function's shape is determined based on the curve shape of the predicted relative attenuation degree changing over time. For example, if the predicted attenuation curve shows a trend of rapid decline followed by slow recovery, the attenuation factor function can be designed to simulate this asymmetric curve shape, such as using a piecewise function, Gaussian function, or polynomial function. This ensures that the attenuation factor function accurately reflects the dynamic process of radiation attenuation. Furthermore, the maximum attenuation amplitude is determined based on the maximum value of the predicted relative attenuation degree, ensuring that the correction strength of the attenuation factor function at the moment of most severe attenuation matches the actual prediction. When using the attenuation factor function to correct the second-cycle solar radiation data within the radiation attenuation period, it can be understood as adjusting the original second-cycle solar radiation data at each time point within the attenuation period based on the output value of the attenuation factor function at that time point. For example, corrected solar radiation data can be represented as the original solar radiation data multiplied by a correction factor, which is determined by the value of the attenuation factor function, typically `1 - attenuation factor function value`. The predicted dynamic attenuation effect is precisely superimposed onto the original solar radiation data, resulting in corrected data that more closely approximates reality.

[0057] This application addresses the issue of insufficient accuracy caused by static or coarse corrections in traditional correction methods by transforming the predicted radiation attenuation characteristics—including attenuation period, maximum attenuation degree, and curve shape—into a dynamically changing attenuation factor function over time. Specifically, the time interval of the attenuation factor function ensures that corrections are only performed during the actual attenuation period, avoiding erroneous corrections during non-attenuation periods. The defined function shape allows the correction process to accurately simulate the dynamic trend of radiation attenuation, such as the process from slight attenuation to severe attenuation and then to gradual recovery. The setting of the maximum attenuation amplitude ensures that the correction intensity at the moment of most severe attenuation is consistent with the prediction result. Therefore, by using this dynamic attenuation factor function to correct the second-cycle solar radiation data point-by-point or time-by-time, the actual impact of local micrometeorological phenomena on illumination conditions can be reflected more realistically and meticulously, thus providing more accurate input data for subsequent power generation prediction.

[0058] Furthermore, the steps for generating typical meteorological year data reflecting site illumination conditions based on the corrected second-cycle solar radiation data include: Based on the corrected second-cycle solar radiation data, a representative month's data is selected for each month within the second preset evaluation cycle; Data from twelve representative months were combined in chronological order to generate typical meteorological year data.

[0059] The selection of a representative month's data for each month within the second preset assessment period refers to choosing the dataset that best represents the climate characteristics of each specific month (e.g., January, February) from the long-term corrected second-period solar radiation data. Specifically, this can be done using statistical methods, such as calculating the average solar radiation, sunshine hours, and temperature for each month, and selecting the month's data that is closest to the multi-year average as the representative month's data. This ensures that the selected monthly data accurately reflects the typical sunshine conditions for that month on a long-term scale, avoiding assessment bias caused by abnormal weather conditions in individual years.

[0060] Generating typical meteorological year data by combining data from twelve representative months in chronological order involves piecing together the data from these twelve months according to their natural order within a year, such as January, February...December, to construct a complete annual data sequence. This results in a comprehensive typical meteorological year dataset representing long-term average meteorological characteristics. A standardized, statistically significant annual sunshine dataset is also provided for subsequent long-term forecasting of photovoltaic power plant generation.

[0061] This application first carefully selects the most representative monthly data from the corrected long-term solar radiation data, ensuring that the sunshine conditions for each month reflect its long-term average state while also including the true situation after correction for local micrometeorological phenomena. Then, these representative monthly data are seamlessly combined in chronological order to construct a complete and statistically significant typical meteorological year dataset. This construction method avoids the randomness and extremes that may exist in directly using actual data from a single year, making the generated meteorological year data more stable and accurate in representing the long-term sunshine resource characteristics of the proposed photovoltaic power plant site.

[0062] The typical meteorological year data generated through the above technical solution not only reflects the long-term average sunshine conditions of the proposed photovoltaic power plant site, but more importantly, because it is constructed based on second-cycle solar radiation data corrected for local micrometeorological phenomena, it can more accurately reflect the radiation attenuation that the site may encounter in actual operation. This makes subsequent power generation predictions based on this typical meteorological year data more accurate and reliable, thus providing more solid data support for investment decisions, design optimization, and operation management of photovoltaic power plants, and effectively reducing the risks caused by inaccurate solar resource assessment.

[0063] Furthermore, when the proposed photovoltaic power plant site is located in a basin environment with different micro-topographic features, after obtaining the second-cycle solar radiation data and corresponding second-cycle environmental parameters of the proposed photovoltaic power plant site within the second preset evaluation period, the method further includes: The area where the proposed photovoltaic power station site is located is divided into multiple sub-regions with different micro-topographic features. The climate parameter deviation characteristics between each sub-region and the on-site meteorological station under the preset meteorological conditions are preset. For each sub-region, the environmental parameters of the second cycle are corrected based on the characteristics of the microclimate parameter deviation, thus obtaining the sub-regional environmental parameters for each sub-region. Based on the mapping prediction rules and the prediction of sub-regional environmental parameters, the radiation attenuation characteristics caused by local micro-meteorological phenomena are predicted within the second preset evaluation period. The sub-regional solar radiation data are corrected based on the predicted radiation attenuation characteristics to obtain the corrected sub-regional solar radiation data. Based on the spatial distribution of each sub-region within the proposed photovoltaic power plant site, the corrected solar radiation data of the sub-regions are integrated to generate a radiation dataset for the proposed photovoltaic power plant site. Based on the radiation dataset, typical meteorological year data are generated.

[0064] Specifically, when a proposed photovoltaic power plant site is located in a basin environment with different micro-topographic features, such as valleys, slopes, and reservoir edges, these micro-topographic features significantly affect local airflow, humidity, and temperature distribution, thereby influencing the formation and dissipation of local micro-meteorological phenomena. To more precisely assess solar resources, the area where the proposed photovoltaic power plant site is located is divided into multiple sub-regions. Each sub-region is designed to have relatively uniform micro-topographic features and microclimate conditions, for example, based on geographical information such as slope, altitude, and distance from water bodies.

[0065] To compensate for the inadequacy of field weather station data in complex terrain, which cannot fully represent the microclimate conditions of all sub-regions, it is necessary to pre-determine the climate parameter deviation characteristics between each sub-region and the field weather station under pre-defined meteorological conditions. Climate parameter deviation characteristics can be understood as the systematic differences in environmental parameters such as temperature, humidity, and wind speed within the sub-region relative to the measurements from the field weather station under specific meteorological conditions, such as clear skies without wind, cloudy skies, or fog. These deviation characteristics can be obtained through short-term field measurements, numerical simulations such as computational fluid dynamics (CFD) simulations, or historical data analysis.

[0066] Based on this, for each sub-region, the second-cycle environmental parameters obtained from the field weather station are corrected according to its preset microclimate parameter deviation characteristics, thereby obtaining sub-region environmental parameters that better reflect the actual situation of that sub-region. For example, if a sub-region is located at the bottom of a valley, its temperature may be generally lower than the regional average temperature measured by the weather station, and its humidity may be higher. In this case, the temperature and humidity data of the weather station will be adjusted according to the preset deviation characteristics.

[0067] Subsequently, based on the previously established mapping prediction rules and the corrected sub-regional environmental parameters, the radiation attenuation characteristics caused by local microclimate phenomena in each sub-region are predicted within the second preset assessment period. The predicted radiation attenuation characteristics, such as the start and end times of the attenuation period, the maximum relative attenuation degree, and the shape of the attenuation curve, are used to correct the second-period solar radiation data for that sub-region, thus obtaining the corrected sub-regional solar radiation data. This process ensures that the radiation data for each sub-region fully considers its unique microclimate influences.

[0068] Finally, to obtain a comprehensive solar resource assessment of the entire proposed photovoltaic power plant site, it is necessary to integrate the corrected sub-regional solar radiation data based on the spatial distribution of each sub-region within the site. For example, a weighted average can be calculated based on the area weight of each sub-region or its importance within the site, or a radiation dataset covering the entire site can be generated using spatial interpolation. Based on this integrated radiation dataset, typical meteorological year data reflecting the overall site illumination conditions can be generated to support more accurate photovoltaic power plant power generation predictions.

[0069] This application addresses the problem of traditional methods failing to accurately capture local microclimate differences in complex terrain by dividing proposed photovoltaic power plant sites in basin environments with complex micro-topographic features into multiple sub-regions and introducing climate parameter deviation features into each sub-region. Specifically, sub-region division breaks down large-scale complex terrain into multiple relatively homogeneous small regions, making the microclimate characteristics of each sub-region easier to identify and quantify. The pre-defined climate parameter deviation features serve as a correction mechanism, effectively correcting the discrepancies between central meteorological station data and the actual microclimate of the sub-region, making the environmental parameters used for radiation attenuation prediction more representative. Based on this, applying mapping prediction rules to the environmental parameters of each corrected sub-region allows for targeted prediction and correction of radiation attenuation in each sub-region, avoiding a one-size-fits-all assessment approach. Finally, by integrating the corrected sub-region solar radiation data, a more comprehensive and refined site radiation dataset can be constructed, providing a more solid data foundation for the generation of typical meteorological year data and power generation prediction.

[0070] Secondly, see Figure 2This application proposes a photovoltaic power plant solar resource assessment device for performing the aforementioned photovoltaic power plant solar resource assessment method. The device includes: Data acquisition module 210 is used to acquire solar radiation data and environmental parameters for the first period within the first preset assessment period of the proposed photovoltaic power plant site. The radiation attenuation identification module 220 is used to identify the attenuation characteristics of the radiation attenuation period caused by local micrometeorological phenomena based on the solar radiation data of the first cycle and the theoretical radiation data of the corresponding time. The correlation information extraction module 230 is used to analyze the correlation between the attenuation characteristics and the environmental parameters of the first cycle during the radiation attenuation period and extract the correlation information. The mapping prediction rule establishment module 240 is used to establish mapping prediction rules based on correlation information to predict the radiation attenuation characteristics caused by local micrometeorological phenomena according to environmental parameters. The data correction module 250 is used to obtain the second-cycle solar radiation data and corresponding second-cycle environmental parameters of the proposed photovoltaic power plant site within the second preset evaluation period. The second preset evaluation period is longer than the first preset evaluation period. Based on the mapping prediction rule and the second-cycle environmental parameters, the radiation attenuation characteristics caused by local micro-meteorological phenomena within the second preset evaluation period are predicted. The second-cycle solar radiation data is corrected based on the predicted radiation attenuation characteristics. Typical meteorological year data generation module 260 is used to generate typical meteorological year data reflecting the site illumination conditions based on the corrected second-cycle solar radiation data, in order to predict the power generation of the proposed photovoltaic power plant site.

[0071] Through the above-described solution, this application provides a device capable of automating and systematically performing photovoltaic power plant solar resource assessments. By decomposing complex methods and steps into clear functional modules, it significantly improves the efficiency and accuracy of solar resource assessments, reduces the need for manual intervention, and ensures the consistency and reliability of assessment results. Especially when dealing with radiation attenuation caused by local micrometeorological phenomena, it can establish predictive models based on historical data and correct long-term solar irradiance data, thereby making the final power generation prediction closer to reality. This provides a more accurate basis for decision-making in the planning, design, and operation of photovoltaic power plants. Users can interact with the device through a web interface or client application to configure assessment parameters, view assessment results, and manage data.

[0072] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for assessing the solar resources of a photovoltaic power plant, characterized in that, include: Obtain the first-cycle solar radiation data and first-cycle environmental parameters of the proposed photovoltaic power plant site within the first preset assessment period; Based on the solar radiation data of the first cycle and the theoretical radiation data at the corresponding time, the attenuation characteristics of the radiation attenuation period caused by local micrometeorological phenomena are identified. Analyze the correlation between the attenuation characteristics during the radiation attenuation period and the environmental parameters of the first period, and extract the correlation information; Based on the aforementioned correlation information, a mapping prediction rule is established for predicting the radiation attenuation characteristics caused by the local micrometeorological phenomena according to environmental parameters. The second-cycle solar radiation data and corresponding second-cycle environmental parameters of the proposed photovoltaic power station site within a second preset evaluation period are obtained, wherein the second preset evaluation period is longer than the first preset evaluation period. Based on the mapping prediction rule and the second-cycle environmental parameters, the radiation attenuation characteristics caused by the local micro-meteorological phenomena within the second preset evaluation period are predicted, and the second-cycle solar radiation data are corrected based on the predicted radiation attenuation characteristics. Based on the corrected second-cycle solar radiation data, typical meteorological year data reflecting the site's illumination conditions are generated to predict the power generation of the proposed photovoltaic power plant site.

2. The photovoltaic power plant solar resource assessment method according to claim 1, characterized in that, The localized micrometeorological phenomenon is a low-altitude fog that occurs in the early morning due to the topography and reservoir environment of the proposed photovoltaic power station site.

3. The method for assessing solar resources in a photovoltaic power station according to claim 1, characterized in that, The step of identifying the attenuation characteristics of the radiation attenuation period caused by local micrometeorological phenomena based on the solar radiation data of the first cycle and the theoretical radiation data at the corresponding time includes: For each preset morning period within the first preset evaluation period, based on the geographical location and time information of the proposed photovoltaic power station site, the theoretical radiation value at each time point within each preset morning period is calculated, and the relative attenuation degree between the first-cycle solar radiation value and the theoretical radiation value at each time point within each preset morning period is calculated. The continuous time points in which the relative attenuation degree is higher than the first preset threshold are identified as the radiation attenuation period, wherein the first preset threshold is a preset relative radiation attenuation threshold caused by the local micrometeorological phenomenon. Record the attenuation characteristics of the radiation attenuation period, wherein the attenuation characteristics include at least one or more of the following: the start and end times of the radiation attenuation period, the maximum value of the relative attenuation degree, and the curve shape of the relative attenuation degree changing over time.

4. The method for assessing solar resources in a photovoltaic power plant according to claim 1, characterized in that, The step of analyzing the correlation between the attenuation characteristics during the radiation attenuation period and the environmental parameters of the first period, and extracting the correlation information, includes: Local environmental parameters are extracted from the environmental parameters of the first cycle based on the radiation attenuation period; The extracted local environmental parameters are associated with the attenuation characteristics during the radiation attenuation period to form a data pair reflecting the correspondence between the local environmental parameters and the attenuation characteristics, which serves as the association information.

5. The method for assessing the solar resources of a photovoltaic power station according to claim 4, characterized in that, The step of establishing a mapping prediction rule based on the correlation information to predict the radiation attenuation characteristics caused by the local micrometeorological phenomenon according to environmental parameters includes: Based on the local environmental parameters in the associated data pair, a local environmental parameter judgment threshold for predicting the radiation attenuation characteristics is determined. Based on the local environmental parameters and their corresponding attenuation features, a mapping relationship is established between the local environmental parameter judgment threshold and the radiation attenuation feature, which serves as the mapping prediction rule. The radiation attenuation feature includes the start and end times of the radiation attenuation period in which the radiation attenuation feature is located, the maximum value of the relative attenuation degree corresponding to the radiation attenuation feature, and the curve shape of the relative attenuation degree changing over time.

6. The method for assessing solar resources in a photovoltaic power plant according to claim 5, characterized in that, The step of predicting the radiation attenuation characteristics caused by the local micrometeorological phenomena within the second preset evaluation period based on the mapping prediction rule and the second periodic environmental parameters includes: The second periodic environmental parameters are compared with the local environmental parameter judgment threshold in the mapping prediction rule; If the second periodic environmental parameter reaches the local environmental parameter judgment threshold, the radiation attenuation characteristic corresponding to the second periodic environmental parameter within the second preset evaluation period is determined according to the mapping prediction rule.

7. The photovoltaic power plant solar resource assessment method according to claim 6, characterized in that, The step of correcting the second-period solar radiation data based on the predicted radiation attenuation characteristics includes: Based on the start and end times of the radiation attenuation period in which the radiation attenuation feature is located, the maximum value of the relative attenuation degree corresponding to the radiation attenuation feature, and the curve shape of the relative attenuation degree changing with time, the function shape, maximum attenuation amplitude, and time interval of the function that dynamically changes with the attenuation process are determined. The time interval of the function is determined by the start and end times of the radiation attenuation period in which the radiation attenuation feature is located, the function shape is determined based on the curve shape of the relative attenuation degree changing with time, and the maximum attenuation amplitude is determined based on the maximum value of the relative attenuation degree corresponding to the radiation attenuation feature. The second-cycle solar radiation data during the radiation decay period is corrected using the attenuation factor function to obtain the corrected second-cycle solar radiation data.

8. The method for assessing solar resources in a photovoltaic power plant according to claim 1, characterized in that, The steps for generating typical meteorological year data reflecting site illumination conditions based on the corrected second-period solar radiation data include: Based on the corrected second-cycle solar radiation data, a representative month's data is selected for each month within the second preset evaluation period; The data from the twelve selected representative months are combined in chronological order to generate the typical meteorological year data.

9. The method for assessing solar resources in a photovoltaic power station according to claim 1, characterized in that, When the proposed photovoltaic power station site is located in a basin environment with different micro-topographic features, after obtaining the second-cycle solar radiation data and corresponding second-cycle environmental parameters of the proposed photovoltaic power station site within a second preset evaluation period, the method further includes: The area where the proposed photovoltaic power station site is located is divided into multiple sub-regions with different micro-topographic features. For each sub-region, the climate parameter deviation characteristics between it and the on-site meteorological station under preset meteorological conditions are preset. For each sub-region, the second-period environmental parameters are corrected based on the microclimate parameter deviation characteristics to obtain the sub-region environmental parameters for each sub-region; Based on the mapping prediction rule and the environmental parameters of the sub-region, the radiation attenuation characteristics caused by the local micro-meteorological phenomena are predicted within the second preset evaluation period. Based on the predicted radiation attenuation characteristics, the solar radiation data of the sub-region is corrected to obtain the corrected solar radiation data of the sub-region. Based on the spatial distribution relationship of each sub-region within the proposed photovoltaic power plant site, the corrected solar radiation data of the sub-regions are integrated to generate a radiation dataset for the proposed photovoltaic power plant site. Based on the radiation dataset, the typical meteorological year data is generated.

10. A photovoltaic power plant solar resource assessment device, used to perform the photovoltaic power plant solar resource assessment method as described in any one of claims 1 to 9, characterized in that, The device includes: The data acquisition module is used to acquire the first-cycle solar radiation data and first-cycle environmental parameters of the proposed photovoltaic power plant site within the first preset assessment period. The radiation attenuation identification module is used to identify the attenuation characteristics of the radiation attenuation period caused by local micrometeorological phenomena based on the solar radiation data of the first cycle and the theoretical radiation data at the corresponding time. The correlation information extraction module is used to analyze the correlation between the attenuation characteristics during the radiation attenuation period and the environmental parameters of the first period, and extract the correlation information. The mapping prediction rule establishment module is used to establish mapping prediction rules based on the associated information to predict the radiation attenuation characteristics caused by the local micrometeorological phenomena according to environmental parameters. The data correction module is used to obtain the second-cycle solar radiation data and corresponding second-cycle environmental parameters of the proposed photovoltaic power station site within a second preset evaluation period, wherein the second preset evaluation period is longer than the first preset evaluation period. Based on the mapping prediction rule and the second-cycle environmental parameters, the module predicts the radiation attenuation characteristics caused by the local micro-meteorological phenomena within the second preset evaluation period, and corrects the second-cycle solar radiation data based on the predicted radiation attenuation characteristics. The typical meteorological year data generation module is used to generate typical meteorological year data reflecting the site illumination conditions based on the corrected second-cycle solar radiation data, so as to predict the power generation of the proposed photovoltaic power plant site.

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