Key indicator set-based typical meteorological year scenario generation method, device, equipment and medium

Abstract

The application discloses a typical meteorological year scene generation method and device based on a key index set, equipment and a medium, relates to the technical field of power systems, and can provide more adaptive and reliable meteorological boundary input for the planning and design of new energy power stations, energy storage configuration and operation optimization, and effectively alleviate the regulation and configuration risks caused by scene distortion. The method comprises the following steps: obtaining meteorological data of a plurality of historical years, calculating a wind energy comprehensive index corresponding to each historical year according to the meteorological data of the plurality of historical years; calculating a photovoltaic comprehensive index corresponding to each historical year according to the meteorological data of the plurality of historical years; according to the scene type, combining the wind energy comprehensive index and the photovoltaic comprehensive index corresponding to each historical year, selecting corresponding target historical month data for each calendar month in the 12 calendar months from the meteorological data of the plurality of historical years to obtain a typical meteorological year scene matched with the scene type.

Description

Technical Field

[0001] This application relates to the field of power system technology, and in particular to a method, apparatus, equipment and medium for generating typical meteorological year scenarios based on a set of key indicators. Background Technology

[0002] In recent years, building a new power system with new energy sources as the mainstay has become a core task of energy transformation. However, the output of new energy sources is highly volatile, intermittent, and uncertain, and its power generation is significantly affected by meteorological conditions, leading to challenges in power system operation such as supply and demand balance regulation and energy storage configuration optimization. Against this backdrop, how to accurately simulate the spatiotemporal distribution characteristics of new energy output through scientific meteorological scenario generation technology has become a key issue supporting the planning and operation of the new power system.

[0003] In related technologies, the typical meteorological year (TMY) method is mainly used to construct new energy power generation scenarios. Specifically, it is necessary to conduct statistical screening based on historical meteorological data such as wind speed, sunshine, and temperature, and generate typical meteorological sequences by selecting representative years or weighted synthesis, and then combine them with equipment power curves to simulate power generation output. Alternatively, statistical methods such as cluster analysis and principal component analysis can be introduced to optimize scenario screening, or multiple scenario sets can be generated through Monte Carlo simulation to reflect uncertainty.

[0004] However, the applicant recognizes that the relevant technology has at least the following technical problems in its implementation: Typical meteorological scenarios based on static synthesis of historical data are difficult to capture dynamic features such as the frequent occurrence of extreme weather events and changes in climate patterns. This leads to systematic deviations between the generated scenarios and actual meteorological conditions, making it impossible to accurately depict the boundary conditions for new energy output and to accurately assist in the planning, design, and operation optimization of new energy power plants. Summary of the Invention

[0005] In view of this, this application provides a method, device, equipment and medium for generating typical meteorological year scenarios based on a set of key indicators. The main purpose is to solve the problem that it is currently impossible to accurately characterize the boundary conditions of new energy output, which makes it difficult to accurately assist in the planning, design and operation optimization of new energy power plants.

[0006] According to the first aspect of this application, a method for generating typical meteorological year scenes based on a set of key indicators is provided, the method comprising: Obtain meteorological data from multiple historical years, and calculate the comprehensive wind energy index for each historical year based on the meteorological data from multiple historical years; Based on the meteorological data from the multiple historical years, calculate the comprehensive photovoltaic index corresponding to each historical year; The scenario type of the typical meteorological year scenario to be generated is determined. Based on the scenario type, combined with the wind energy comprehensive index and photovoltaic comprehensive index corresponding to each historical year, the corresponding target historical month data is selected for each of the 12 calendar months in the meteorological data of the multiple historical years. The 12 target historical month data corresponding to the 12 calendar months are organized into a complete year sequence to obtain a typical meteorological year scenario that matches the scenario type.

[0007] According to a second aspect of this application, a typical meteorological year scene generation device based on a key indicator set is provided, the device comprising: The wind energy index generation module is used to acquire meteorological data from multiple historical years and calculate the comprehensive wind energy index for each historical year based on the meteorological data from multiple historical years. The photovoltaic index generation module is used to calculate the comprehensive photovoltaic index corresponding to each historical year based on the meteorological data of the multiple historical years. The typical meteorological year scene generation module is used to determine the scene type of the typical meteorological year scene to be generated. Based on the scene type, combined with the wind energy comprehensive index and photovoltaic comprehensive index corresponding to each historical year, the module selects the corresponding target historical month data for each of the 12 calendar months from the meteorological data of the multiple historical years, and organizes the 12 target historical month data corresponding to the 12 calendar months into a complete year sequence to obtain a typical meteorological year scene that matches the scene type.

[0008] According to a third aspect of this application, an apparatus is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of the method described in any of the first aspects above.

[0009] According to a fourth aspect of this application, a medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any one of the first aspects above.

[0010] By employing the aforementioned technical solutions, this application provides a method, apparatus, equipment, and medium for generating typical meteorological annual scenarios based on a set of key indicators. This application constructs multi-dimensional comprehensive indicators for wind energy and photovoltaics respectively, and selects and splices together the best-matching historical data for each month based on different scenario types. This not only preserves the authenticity of historical data, but also strengthens the feature extraction of volatility and extreme events through an indicator-based screening mechanism. As a result, it generates typical meteorological scenarios that better meet the planning and operation needs of new power systems, substantially improving the accuracy and boundary coverage of meteorological scenarios. This provides more adaptable and reliable meteorological boundary inputs for the planning and design, energy storage configuration, and operation optimization of new energy power plants, effectively mitigating the control and configuration risks caused by scenario distortion.

[0011] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0012] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 The illustration shows a flowchart of a method for generating typical meteorological year scenarios based on a set of key indicators, provided in an embodiment of this application. Figure 2 A schematic diagram of the structure of a typical meteorological year scene generation device based on a key indicator set provided in an embodiment of this application is shown. Figure 3 A schematic diagram of the device structure of a computer device provided in an embodiment of this application is shown. Detailed Implementation

[0013] Exemplary embodiments of the present application will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this application will be thorough and complete, and will fully convey the scope of the present application to those skilled in the art.

[0014] This application provides a method for generating typical meteorological year scenes based on a set of key indicators, such as... Figure 1 As shown, the method includes: S10: Obtain meteorological data from multiple historical years, and calculate the comprehensive wind energy index for each historical year based on the meteorological data from multiple historical years.

[0015] In this embodiment, meteorological data from multiple historical years is first obtained. For the hourly wind speed and wind direction time series data for each historical year, five wind energy indicators are calculated: average wind speed, wind speed standard deviation, wind speed coefficient of variation, daily frequency of extreme wind speeds, and wind direction concentration. The average wind speed reflects the average level of wind resources; the wind speed standard deviation describes the dispersion of wind speed; the wind speed coefficient of variation is the ratio of the standard deviation to the average, used to measure relative volatility; the daily frequency of extreme wind speeds refers to the percentage of days when wind speeds exceed the turbine cutoff speed, characterizing the risk of downtime; and the wind direction concentration reflects the availability of wind energy by calculating the consistency of wind direction angles.

[0016] Subsequently, these five indicators, each with different physical meanings and dimensions, were normalized to eliminate the influence of dimensions. Each indicator was then assigned a weight based on its contribution to the uncertainty of wind energy output. Finally, a single comprehensive wind energy index was constructed using a weighted summation method to indicate the multidimensional uncertainty of wind energy. Specifically, a structured index system can integrate multi-dimensional characteristics such as wind speed intensity, fluctuations, extreme events, and directional distribution into a comprehensive evaluation value. This allows for a comprehensive and quantitative characterization of the uncertainty level inherent in wind energy resources within a complete year, providing an accurate and comparable data foundation for subsequent scenario selection.

[0017] For example, when calculating the comprehensive wind energy index of a certain region in 1985, based on 8760 hours of wind speed data for that year, specific values ​​such as the annual average wind speed of 6.2 m / s, the wind speed standard deviation of 3.1, and the daily frequency of extreme wind speed of 2.5% were first calculated. After normalization and weighting, a comprehensive index value between 0 and 1 was finally obtained. The higher the value, the higher the overall level of uncertainty of wind energy in that year.

[0018] S20: Calculate the comprehensive photovoltaic index for each historical year based on meteorological data from multiple historical years.

[0019] In this embodiment, based on meteorological data from multiple historical years, a comprehensive photovoltaic index is calculated for each historical year. Specifically, five photovoltaic indicators can be calculated in parallel for the total hourly horizontal irradiance data for each historical year: average annual radiation, annual radiation fluctuation, typical cloudy / rainy day continuity index, sunny day probability, and maximum radiation day value. Average annual radiation is the average annual GHI, representing the total resource amount; annual radiation fluctuation is the standard deviation of GHI, characterizing the range of radiation intensity variation; the typical cloudy / rainy day continuity index reflects the risk of continuous cloudy / rainy weather by statistically analyzing the maximum number of consecutive hours with GHI below the low radiation threshold; sunny day probability is the proportion of days in the year where the daily average GHI exceeds the sunny day threshold; and maximum radiation day value is the maximum cumulative radiation on a single day in the year, representing the peak potential of the resource. Similarly, these indicators are normalized, and weights are assigned based on their impact on photovoltaic output uncertainty, thus weighted to synthesize a comprehensive photovoltaic multidimensional uncertainty index.

[0020] In this way, through the above process, the characteristics and uncertainties of solar radiation resources are systematically quantified from multiple key dimensions, including total resource volume, temporal fluctuations, risk of persistent shortages, number of sunny days, and peak potential. In particular, the quantification of extreme adverse conditions such as continuous cloudy and rainy weather allows the generated meteorological scenarios to more realistically reflect the challenges faced by photovoltaic power generation. For example, when assessing photovoltaic resources in 1998, the average radiation for that year was calculated to be 1500 kWh / m², and the longest continuous low radiation event was identified, lasting for 5 days. This information is integrated into a comprehensive photovoltaic index, thereby effectively distinguishing between different year types: "stable high radiation" and "fluctuating with prolonged cloudy and rainy weather."

[0021] S30: Determine the scenario type of the typical meteorological year scenario to be generated. Based on the scenario type, and in combination with the comprehensive wind energy index and comprehensive photovoltaic index corresponding to each historical year, select the corresponding target historical month data for each of the 12 calendar months from the meteorological data of multiple historical years, and organize the 12 target historical month data corresponding to the 12 calendar months into a complete year sequence to obtain a typical meteorological year scenario that matches the scenario type.

[0022] In this embodiment, firstly, the typical meteorological year scenario to be generated is identified as belonging to three types: average meteorological year scenario, highly volatile meteorological year scenario, and extreme meteorological year scenario. Then, based on the selected scenario type, monthly data is selected using scoring and selection rules matching that type. For example, for an average meteorological year, the wind and solar power comprehensive indexes for each of the 12 calendar months are calculated across all historical years, and a monthly comprehensive score is further synthesized. Finally, the monthly data of the specific year whose monthly score is closest to the historical long-term average level of that month is selected as the target historical monthly data for that calendar month. For a highly volatile meteorological year, the historical month with the highest weighted score for the two volatility indicators, wind speed standard deviation and radiation volatility, can be selected. For an extreme meteorological year, the historical month with the highest combined score for extreme wind speed frequency, rain continuity, and volatility indicators can be selected.

[0023] Finally, the data from 12 target historical months (selected from 12 calendar months, possibly from different historical years) are spliced ​​together in their natural month order (January to December) to form a physically complete and temporally continuous 8760-hour typical meteorological year scenario sequence. This breaks through the limitations of traditional methods that directly select a complete year or simply synthesize data. By using a strategy of selecting the best data for each month and relying on a refined comprehensive index, the desired features in the generated scenario are actively and specifically enhanced, such as average conditions, high volatility, or extremes. This results in meteorological inputs with clearer boundaries, stronger representativeness, and better suited to specific analytical purposes. For example, when constructing an "extreme meteorological year" for a certain region, extreme characteristic months from different years, such as January 2005 (with a high frequency of extreme winds), July 2010 (with a long number of consecutive rainy days), and March 1999 (with severe fluctuations in wind speed and radiation), can be combined to form a composite year that concentrates extreme meteorological conditions over many years. This helps to test the resilience of the power system under the most unfavorable conditions.

[0024] Optionally, in this embodiment of the application, the comprehensive wind energy index corresponding to each historical year is calculated based on meteorological data from multiple historical years. This includes: for each historical year, calculating the historical year's meteorological data to obtain the uncertainty risk value for that historical year. Wind speed standard deviation Wind speed variation coefficient Extreme wind speed daily frequency and wind direction concentration The following formula is used to calculate the uncertainty risk value for historical years. Wind speed standard deviation Wind speed variation coefficient Extreme wind speed daily frequency and wind direction concentration Calculations were performed to obtain the comprehensive wind energy index for historical years. ,

[0025] in, Represents the coefficient of variation of wind speed The corresponding normalized weighting coefficients, Indicates the standard deviation of wind speed The corresponding normalized weighting coefficients, Indicates the daily frequency of extreme wind speeds The corresponding normalized weighting coefficients, Indicates wind direction concentration The corresponding normalized weighting coefficients, Indicates the value of uncertainty risk The corresponding normalized weighting coefficients.

[0026] In this embodiment, firstly, for a complete hourly wind speed and direction data sequence of a historical year, five sub-indicators with clear physical meaning are calculated sequentially. The first of these is the uncertainty risk value. The uncertainty risk value is a contrarian indicator; the higher the value, the lower the average wind speed, and the higher the potential risk or uncertainty of wind energy development. The second item is the standard deviation of wind speed. The first term measures the absolute magnitude of wind speed fluctuations around its mean; the third term is the wind speed variation coefficient. The first term eliminates the dimension and is used to reflect the relative degree of wind speed fluctuation, which is particularly important for comparing the fluctuation of different wind speed levels in different regions; the fourth term is the daily frequency of extreme wind speeds. This directly quantifies the frequency of wind turbine shutdowns due to extreme winds; the fifth item is wind direction concentration. The value is between 0 and 1. The larger the value, the more concentrated the wind direction, which is more conducive to the optimization of wind turbine layout and output stability.

[0027] After obtaining the raw values ​​of the above five sub-indicators, in order to eliminate the influence of their different dimensions and numerical ranges, each item can first be normalized across years, and then they can be combined into a comprehensive index by weighted summation as the comprehensive wind energy index for historical years. Specifically, the weighted sum can be calculated using the following formula 1: Formula 1:

[0028] In Formula 1, Represents the coefficient of variation of wind speed The corresponding normalized weighting coefficients, Indicates the standard deviation of wind speed The corresponding normalized weighting coefficients, Indicates the daily frequency of extreme wind speeds The corresponding normalized weighting coefficients, Indicates wind direction concentration The corresponding normalized weighting coefficients, Indicates the value of uncertainty risk The corresponding normalized weight coefficients can be assigned values ​​based on their actual importance.

[0029] In this way, through the above process, we can break through the limitations of traditional methods that only focus on average wind speed. By systematically integrating the average level of wind speed, absolute and relative fluctuations, extreme event risks, and wind direction distribution characteristics, we can construct a comprehensive evaluation system that can fully and evenly characterize the spatiotemporal uncertainty of wind energy resources. This will enable us to more accurately select historical monthly data that meet the characteristics of different scenarios such as "average", "high fluctuation" or "extreme" when constructing typical meteorological years.

[0030] For example, when implementing this method based on historical data from a hydropower station area, the meteorological data for 1998 was used to calculate the annual average wind speed as 5.8 m / s and the standard deviation as 2.9 m / s. It was also found that there were three days in that year with extreme winds exceeding the shear line speed. After normalization and assigning pre-defined weights, if the volatility index has a slightly higher weight, a specific comprehensive wind energy index is calculated using a weighted average. If the value is at a moderate level in a multi-year series, then that year may be selected as one of the candidate months for the "mean weather year"; if its value is high in a specific month, such as a winter month... and If all values ​​are high and the performance is outstanding, then that month is more likely to be selected as the corresponding month of a "high-fluctuation weather year" or "extreme weather year".

[0031] Optionally, in this embodiment of the application, the uncertainty risk value of the historical year is calculated by analyzing the meteorological data corresponding to the historical year. Wind speed standard deviation Wind speed variation coefficient Extreme wind speed daily frequency and wind direction concentration This includes: calculating the uncertainty risk value of historical years using the following formula based on the meteorological data of those historical years. ,

[0032] in, This represents the average wind speed over historical years. The standard deviation of wind speed for each historical year is calculated using the following formula based on the meteorological data for that specific historical year. ,

[0033] in, Indicates the number of hours in a year. Indicates the first wind speed per hour, This represents the average wind speed over historical years. The coefficient of variation of wind speed for each historical year is calculated using the following formula based on the meteorological data for that specific historical year. ,

[0034] in, The standard deviation of wind speed for historical years. This represents the average wind speed over historical years. The following formula is used to calculate the daily frequency of extreme wind speeds for each historical year based on the corresponding meteorological data. ,

[0035] in, This represents the total number of days in a historical year. Indicates the first wind speed per hour, This represents the wind speed cutoff from the wind turbine; the wind direction concentration for each historical year is calculated using the following formula based on the corresponding historical meteorological data. ,

[0036] in, Indicates the number of hours in a year. Indicates the first The wind direction at that hour.

[0037] In this embodiment, firstly, the uncertainty risk value is calculated. The calculation formula is shown in Formula 2 below: Formula 2:

[0038] In Formula 2, This represents the average wind speed over historical years, expressed in m / s. Uncertainty risk value. As a contrarian indicator, Formula 2 is used to calculate the uncertainty risk value. This is because in wind energy resource assessment, average wind speed is the core parameter that determines power generation potential. The lower the value, the less ideal the basic resource conditions for wind energy development, and the more significant the relative impact of its fluctuations and uncertainties. Therefore, the reciprocal of the average wind speed is used to characterize this basic risk.

[0039] Next, calculate the standard deviation of wind speed. The calculation formula is shown in Formula 3 below: Formula 3:

[0040] In formula 3, Indicates the number of hours in a year. Indicates the first wind speed per hour, This represents the average wind speed over historical years. (Standard deviation of wind speed) It measures wind speed at its average value. The greater the standard deviation of the fluctuations, the stronger the dispersion of wind speed, which means greater hourly or minute-level fluctuations in wind power output.

[0041] Based on the standard deviation and mean wind speed, the coefficient of variation of wind speed can be further calculated. The calculation formula is shown in Formula 4 below: Formula 4:

[0042] In formula 4, The standard deviation of wind speed for historical years. This represents the average wind speed over historical years. Wind speed variation coefficient. It is a dimensionless indicator that reflects the relative fluctuation level of wind speed, making the volatility between regions or years with different average wind speed levels comparable. For example, a region with a high average wind speed but also a large standard deviation may have a similar coefficient of variation to a region with a low average wind speed but gentle fluctuations, which provides a more detailed perspective for comprehensive assessment.

[0043] For extreme wind speed daily frequency The calculation formula is shown in Formula 5 below: Formula 5:

[0044] In formula 5, This represents the total number of days in a historical year. Indicates the first wind speed per hour, This indicates the fan cutoff speed, which is the threshold speed at which the fan stops operating to protect its own safety. This is an indicator function; it takes a value of 1 when the condition within the parentheses is true, and 0 otherwise. The calculation process shown in Formula 5 essentially involves checking daily whether the daily maximum wind speed exceeds the cutoff wind speed and calculating the proportion of such days to the total number of days in the year, as well as the daily frequency of extreme wind speeds. It directly quantifies the frequency of wind turbine shutdowns due to extreme winds, which is key to assessing the reliability and power output guarantee rate of wind farms.

[0045] Finally, calculate the wind direction concentration. The calculation formula is shown in Formula 6 below: Formula 6:

[0046] In formula 6, Indicates the number of hours in a year. Indicates the first Wind direction (which can be expressed in radians) Hourly wind direction concentration The value ranges from 0 to 1, and its physical meaning lies in characterizing the stability of wind direction throughout the year; wind direction concentration. The closer the value is to 1, the more concentrated the wind direction is in the dominant direction, which is of positive significance for optimizing the arrangement of wind turbine units, reducing the wake effect, and improving the overall power generation efficiency.

[0047] In this way, by systematically calculating these indicators and integrating them into a comprehensive score through the above process, a panoramic and structured quantitative characterization of the spatiotemporal uncertainty of wind energy resources is achieved from five dimensions: average wind speed, standard deviation, coefficient of variation, cut-out frequency, and wind direction distribution. This surpasses the crude assessment that relies solely on average wind speed, enabling the generated meteorological scenarios to more realistically reflect the various complex situations that wind farms may face during operation. In particular, the quantification of extreme downtime risks and the impact of wind direction changes provides more accurate input boundaries for power plant micro-site selection, unit selection, and energy storage configuration.

[0048] For example, when constructing a typical meteorological year using meteorological data from a hydropower station area from 1980 to 2020, for 1995, the annual average wind speed was first calculated to be 6.1 m / s, and then the uncertainty risk value was calculated. Approximately 0.164; then, the standard deviation of wind speed was calculated based on the annual hourly wind speed series. The wind speed variation coefficient is calculated to be 3.0 m / s. Approximately 0.49; assuming there were 4 days in that year with winds exceeding the shear speed, such as 25 m / s, the daily frequency of extreme wind speeds can be calculated. Approximately equal to 0.011; wind direction concentration If the calculated value is 0.75, then these normalized index values, after being weighted according to the preset weights, jointly determine the position of "wind energy uncertainty" in the entire historical series in 1995. If the comprehensive index value is particularly high in a certain month, then the data of that month is more likely to be selected into the typical meteorological year used to characterize "high volatility" or "extreme" scenarios.

[0049] Optionally, in this embodiment of the application, the photovoltaic comprehensive index corresponding to each historical year is calculated based on meteorological data from multiple historical years, including: for each historical year, calculating the average annual radiation of the historical year based on the meteorological data corresponding to that historical year. Annual radiation fluctuation Typical Rainy Day Continuity Indicators Probability of sunny days and maximum daily radiation value The average annual radiation of historical years is calculated using the following formula. Annual radiation fluctuation Typical Rainy Day Continuity Indicators Probability of sunny days and maximum daily radiation value Calculations were performed to obtain the comprehensive photovoltaic index for historical years. ,

[0050] in, Indicates average annual radiation The corresponding dynamic weighting coefficients, Indicates the annual radiation fluctuation. The corresponding dynamic weighting coefficients, Indicators representing the continuity of typical rainy days The corresponding dynamic weighting coefficients, Indicates the probability of a sunny day. The corresponding dynamic weighting coefficients, Indicates the daily maximum radiation value The corresponding dynamic weighting coefficients, This represents the average daily radiation level throughout the year.

[0051] In this embodiment, it is necessary to first analyze the total hourly horizontal irradiance data for the entire year in historical years, and then calculate five key sub-indicators in sequence. The first of these is the average annual radiation. Average annual radiation The first characteristic represents the total amount of solar energy resources at a location and is fundamental for assessing the potential of photovoltaic power generation; the second characteristic is the annual radiative fluctuation. Annual radiation fluctuation The first term is the standard deviation of the annual GHI series, which directly characterizes the absolute fluctuation range of radiation intensity on an hourly scale. The greater the fluctuation, the more unstable the photovoltaic output. The third term is the continuity index of typical cloudy and rainy days. Typical continuous indicators of rainy days The fourth item is the probability of sunny days. This is used to quantify the duration of the most severe prolonged rainy weather, which is crucial for assessing the risk of prolonged power shortages for photovoltaic systems. The first indicator reflects the frequency of sunny, high-radiation days throughout the year; the fifth indicator is the maximum radiation day value. Maximum daily radiation value This represents the maximum potential peak value of local photovoltaic power generation under ideal conditions in a single day.

[0052] After obtaining these raw indicators, two key processes are required to integrate them into a unified comprehensive evaluation. First, all indicators are normalized to eliminate dimensions; second, based on the direction of each indicator's contribution to the uncertainty of photovoltaic output, a positive adjustment is made in the comprehensive formula, for example, the average annual radiation... It is a typical positive indicator, with higher values ​​being better. Therefore, it is used as a reciprocal in the comprehensive formula, transforming it into a negative indicator. A higher value indicates a relatively weaker resource base and higher uncertainty risk, which aligns with the overall risk assessment direction. Similarly, the probability of sunny days... This is also converted into the "probability of a non-sunny day"; maximum daily radiation value. Then by comparing it with the annual average daily radiation The ratio is used to characterize the prominence of the radiation peak; the larger the ratio, the more significant the extreme high-irradiance event. Finally, the comprehensive index of photovoltaic multidimensional uncertainty is synthesized by the weighted formula shown in Formula 7 below: Formula 7:

[0053] In Formula 7, Indicates average annual radiation The corresponding dynamic weighting coefficients, Indicates the annual radiation fluctuation. The corresponding dynamic weighting coefficients, Indicators representing the continuity of typical rainy days The corresponding dynamic weighting coefficients, Indicates the probability of a sunny day. The corresponding dynamic weighting coefficients, Indicates the daily maximum radiation value The corresponding dynamic weighting coefficients, This represents the average daily radiation level throughout the year. to The value can be determined through statistical methods such as principal component analysis or expert experience.

[0054] In this way, by systematically encompassing the five core dimensions affecting the stability and predictability of photovoltaic power generation—total resources, temporal fluctuations, continuous shortage risk, weather type frequency, and peak anomalies—the above process generates a comprehensive, balanced, and physically meaningful uncertainty assessment. This enables the accurate identification of representative historical months with dramatic radiation changes, continuous rain, or abnormal peak values ​​when constructing "high-fluctuation" or "extreme" weather scenarios.

[0055] For example, when performing a case study analysis on historical data of a hydropower station area, for the GHI data in 2002, the average annual radiation was first calculated. The value is 1480 kWh / m²; next, the annual radiation fluctuation is calculated. The value was 280 W / m². Scanning data revealed that the longest continuous low-radiation event of the year lasted 120 hours, or 5 days. Therefore, the typical continuous index for cloudy / rainy days... The probability is 5; the probability of sunny days in that year is calculated statistically. The value is 0.6; simultaneously, the daily maximum radiation value is calculated. The average daily radiation is 8.5 kWh / m². Approximately 4.05 kWh / m², then based on preset weights, such as a greater focus on volatility and continuity risks, it is assigned... and The higher weights are used for weighted summation to obtain the comprehensive photovoltaic index for 2002. value.

[0056] Optionally, in this embodiment of the application, the average annual radiation of the historical year is calculated from the meteorological data corresponding to the historical year. Annual radiation fluctuation Typical Rainy Day Continuity Indicators Probability of sunny days and maximum daily radiation value This includes: calculating the average annual radiation of historical years using the following formula based on the meteorological data of those historical years. ,

[0057] in, Indicates the number of hours in a year. Indicates the first The total horizontal irradiance per hour; the annual irradiance fluctuation of a historical year is calculated using the following formula based on the meteorological data of that historical year. ,

[0058] in, Indicates the number of hours in a year. Indicates the first Total horizontal irradiance per hour Indicates the first The total horizontal irradiance per hour; the typical cloudy and rainy day continuity index for historical years is obtained by calculating the meteorological data of corresponding historical years using the following formula. ,

[0059] in, Indicates an indicator function, Indicates the first The start time of a series of low-radiation events. Indicates the first The total number of hours of a continuous low-radiation event. Indicates from The number of hours to begin shifting backwards, Indicates at time Total irradiance on the horizontal plane, This represents the low radiation threshold; the probability of sunny days for a historical year is calculated using the following formula based on the meteorological data for that historical year. ,

[0060] in, This represents the total number of days in a historical year. Indicates the first The average daily total horizontal irradiance of the day The threshold for determining clear skies is indicated; the maximum daily radiation value for each historical year is calculated using the following formula based on the meteorological data for that specific historical year. ,

[0061] in, This represents the total number of days in a historical year. Indicates the first Total horizontal irradiance per hour It represents the time interval between two consecutive measurements of the total irradiance of the horizontal plane.

[0062] In this embodiment, the calculation of the comprehensive photovoltaic index is based on in-depth analysis of solar radiation time-series data. The calculation process and physical implications of its five basic sub-indicators together constitute a refined characterization of the uncertainty of photovoltaic resources. Among them, the average annual radiation... The calculation formula is shown in Formula 8 below: Formula 8:

[0063] In Formula 8, Indicates the number of hours in a year. Indicates the first Hourly total horizontal irradiance, measured in watts per square meter (W / m²). Average annual radiation. By calculating the arithmetic average of all hourly GHI values ​​throughout the year, the total amount of solar energy resources at a location is intuitively reflected, which is the basis for assessing the long-term expected power generation of a photovoltaic power station.

[0064] Next, the annual radiation fluctuation. The calculation formula is shown in Formula 9 below: Formula 9:

[0065] In Formula 9, Indicates the number of hours in a year. Indicates the first Total horizontal irradiance per hour Indicates the first Total horizontal irradiance per hour. Annual irradiance fluctuation. The value is used to quantify the short-term drastic changes in solar radiation. The larger the value, the more frequent and drastic the hourly radiation intensity changes. This will directly lead to large fluctuations in the output of photovoltaic power plants in a short period of time, and put forward higher requirements for the power balance regulation of the power grid.

[0066] Typical Rainy Day Continuity Index The calculation formula is shown in Formula 10 below: Formula 10:

[0067] In Formula 10, Indicates an indicator function, Indicates the first The start time of a series of low-radiation events. Indicates the first The total number of hours of a continuous low-radiation event. Indicates from The number of hours to begin shifting backwards, Indicates at time Total irradiance on the horizontal plane, Indicates a low radiation threshold. Typical indicators of continuous cloudy / rainy days. It captured the risk of prolonged periods of zero or low output for photovoltaic power generation due to continuous cloudy and rainy weather.

[0068] For the probability of sunny days Its calculations focus on the day-scale, and the formula is shown in Formula 11 below: Formula 11:

[0069] In Formula 11, This represents the total number of days in a historical year. Indicates the first The average daily total horizontal irradiance of the day This represents the threshold for determining whether a day is sunny. The probability of a sunny day. The statistics show the proportion of days with average daily radiation exceeding the threshold throughout the year, reflecting the frequency of days with high power generation efficiency.

[0070] Maximum daily radiation value The formula used to assess the peak potential of a resource is shown in Formula 12 below: Formula 12:

[0071] In formula 12, This represents the total number of days in a historical year. Indicates the first Total horizontal irradiance per hour This represents the time interval between two consecutive measurements of total irradiance on a horizontal surface. Maximum daily radiation value. This indicates the maximum energy that a photovoltaic system can receive in a single day under ideal weather conditions, which is crucial for assessing the equipment's peak power capacity and the grid's maximum daily absorption capacity.

[0072] In this way, by systematically calculating these five indicators through the above process and integrating them into the comprehensive photovoltaic score, a three-dimensional photovoltaic resource uncertainty assessment framework is constructed from five complementary and key dimensions: total resource base, short-term fluctuation intensity, risk of continuous shortage, frequency of high-efficiency days, and single-day peak potential. This framework not only surpasses the traditional method that only focuses on annual averages, but more importantly, it also considers the annual radiation fluctuation. and typical rainy day continuity index The study emphasizes two risk modes, "rapid fluctuations" and "long-term shortages," which have drastically different but equally important impacts on power system operation. This allows the generated typical meteorological scenarios to more realistically and comprehensively reflect the behavioral characteristics of photovoltaic power output under various boundary conditions.

[0073] For example, in the case study analysis of data from a hydropower station area in 2008, the average annual radiation for that year was first calculated. It is 1520 kWh / m²; then, its annual radiation fluctuation was found by calculating the difference between adjacent hours. The radiation intensity reached 295 W / m², indicating frequent changes in radiation intensity that year. Simultaneously, analysis identified the longest continuous low-radiation event of the year, lasting 96 hours (4 days), which is a typical indicator of the continuity of cloudy and rainy days. The value is 4; by setting a sunny day threshold, the probability of sunny days in that year is statistically obtained. It equals 58%; furthermore, calculations revealed that the cumulative radiation on a certain day of that year reached 8.8 kWh / m², which was the highest daily radiation value of the year. .

[0074] In this embodiment of the application, optionally, the scenario type of the typical meteorological year scenario to be generated is determined. Based on the scenario type, and combined with the comprehensive wind energy index and comprehensive photovoltaic index corresponding to each historical year, the corresponding target historical month data is selected for each of the 12 calendar months from the meteorological data of multiple historical years. This includes: normalizing the monthly meteorological data of each calendar month in each historical year in the corresponding historical year meteorological data using the following formula to obtain the normalized value of each calendar month in each historical year.

[0075] in, Indicates the first Year The month of The normalized value for each indicator type for the corresponding month Indicates the first Year The month of The original indicator values ​​corresponding to each indicator type in the meteorological data of the corresponding historical years. Indicates the first year in all years The month of The minimum indicator value for each indicator type Indicates the first year in all years The month of The maximum value of each indicator type; principal component analysis was performed on the comprehensive wind energy index and comprehensive photovoltaic index for each historical year to obtain the wind energy weight coefficient. and photovoltaic weighting coefficient Combined with wind energy weighting coefficient and photovoltaic weighting coefficient The following formula is used to calculate the corresponding monthly comprehensive score for each calendar month in each historical year. ,

[0076] in, Indicates the first Year Monthly overall score, Indicates the first Year The month of The normalized monthly value corresponding to each indicator type; obtain the target scoring formula matching the scenario type; and combine the target scoring formula with the monthly comprehensive score corresponding to each calendar month in each historical year. Each calendar month in each historical year is scored according to the corresponding dimension, so as to select the target historical month data that matches the scene type for each of the 12 calendar months from the meteorological data of multiple historical years.

[0077] In this embodiment, the process of selecting target historical month data for 12 calendar months is a process based on quantitative indicators and scenario-based scoring. Its core lies in decomposing the previously calculated annual wind and solar comprehensive indicators and applying them to a monthly scale for horizontal comparison and selection. First, all historical data needs to undergo standard normalization preprocessing. For each specific calendar month, such as January of all years, the following formula 13 is used to calculate the corresponding original indicator values. Formula 13:

[0078] In Formula 13, Indicates the first Year The month of The normalized value for each indicator type for the corresponding month Indicates the first Year The month of The original indicator values ​​corresponding to each indicator type in the meteorological data of the corresponding historical years. Indicates the first year in all years The month of The minimum indicator value for each indicator type Indicates the first year in all years The month of The maximum value of each indicator type. Formula 13 finds the maximum and minimum values ​​of a certain indicator in all historical years for the same month, and linearly scales the original indicator values ​​of that month in each year to the range of [0, 1] to obtain its normalized value, thereby eliminating the incomparability between different indicators due to differences in units and absolute value ranges, and ensuring the fairness and effectiveness of subsequent weighted summation.

[0079] After completing data normalization, it is necessary to determine the relative importance of various indicators for wind and solar power in the comprehensive score, i.e., the weighting coefficients. In this embodiment, principal component analysis is used to analyze a rich monthly indicator dataset spanning multiple years, objectively extracting the main components affecting the uncertainty of wind and solar power in a data-driven manner, and determining the wind power weighting coefficient accordingly. and photovoltaic weighting coefficient Wind energy weighting coefficient and photovoltaic weighting coefficient It reflects the contribution of different indicators to the overall fluctuation characteristics, and is more scientifically based than subjective weighting.

[0080] Subsequently, using the wind energy weighting coefficient and photovoltaic weighting coefficient Calculate a basic monthly comprehensive score for each "year-month" combination. The calculation formula is shown in Formula 14 below: Formula 14:

[0081] in, Indicates the first Year Monthly overall score, Indicates the first Year The month of Normalized monthly values ​​for each indicator type. Monthly overall score. It is a comprehensive evaluation that reflects the overall performance of the month compared to the same month in other years after taking into account all uncertainties in wind and solar power.

[0082] Considering that a single global score cannot meet the needs of constructing different targeted scenarios such as "average", "high volatility", and "extreme", this application introduces a target scoring formula that matches the scenario type. The target scoring formula is combined with the monthly comprehensive score corresponding to each calendar month in each historical year. Each calendar month in each historical year is scored according to the corresponding dimension, so as to select the target historical month data that matches the scene type for each of the 12 calendar months from the meteorological data of multiple historical years.

[0083] In this way, through the above process, it is possible to generate multiple scenarios on demand, ensure comparability through normalization, determine objective weights through principal component analysis, and then accurately screen through scenario-based target scoring formulas. This allows for the proactive extraction of the most representative meteorological sequences from massive historical data for specific analytical purposes such as planning, anti-fluctuation testing, and extreme risk stress testing.

[0084] Optionally, in this embodiment, a target scoring formula matching the scene type is obtained, and the target scoring formula is combined with the monthly comprehensive score corresponding to each calendar month in each historical year. The method involves scoring each calendar month within each historical year across various dimensions to select target historical month data matching the scene type for each of the 12 calendar months from multiple historical years' meteorological data. This includes: when a scene type indication is detected to generate an average meteorological year scene, the following formula is used as the target scoring formula, and each calendar month within each historical year is scored across various dimensions to select target historical month data matching the scene type for each of the 12 calendar months from multiple historical years' meteorological data.

[0085] in, Represented as the first The index of the historical year to which the target historical month data belongs. Indicates the first Year Monthly overall score, This represents the number of hours in a year. When a scenario type indicator is detected that generates a high-fluctuation meteorological year scenario, the following formula is used as the target scoring formula, and each calendar month in each historical year is scored according to the corresponding dimension. This is to select the target historical month data that matches the scenario type for each of the 12 calendar months from the meteorological data of multiple historical years.

[0086] in, The score represents the dimension of months with highly volatile weather. This represents the wind energy weighting coefficient. Indicates the photovoltaic weighting coefficient. Indicates the first Year Normalized value of the standard deviation of wind speed in a month Indicates the first Year The normalized value of monthly radiation fluctuation; when a scene type indication is detected to generate an extreme weather year scene, the following formula is used as the target scoring formula, and each calendar month in each historical year is scored according to the corresponding dimension, so as to select the target historical month data that matches the scene type for each of the 12 calendar months from the meteorological data of multiple historical years.

[0087] in, The score represents the monthly dimension of extreme weather. This represents the wind energy weighting coefficient. Indicates the photovoltaic weighting coefficient. Indicates the first Year Normalized value of the standard deviation of wind speed in a month Indicates the first Year Normalized value of the frequency of extreme wind speeds in a month Indicates the first Year The normalized value of the monthly radiation fluctuation. Indicates the first Year Normalized value of the continuity of cloudy and rainy weather in a month.

[0088] In this embodiment, different mathematical formulas are used to score and select the best candidate months based on preset scenario types, such as average meteorological year scenario, high-fluctuation meteorological year scenario, or extreme meteorological year scenario. Specifically, when it is detected that an average meteorological year scenario needs to be generated, the goal is to select the month that best represents the long-term climate norm. At this time, the target scoring and selection formula is shown in Formula 15 below: Formula 15:

[0089] In Formula 15, Represented as the first The index of the historical year to which the target historical month data belongs. Indicates the first Year Monthly overall score, It represents the number of hours in a year. This refers to the index of the year that minimizes the absolute deviation between its own score and the historical average score. Formula 15 then allows us to find the historical month that best represents the overall situation. For example, when constructing the January of a region's average meteorological year, we calculate the January scores for 41 Januarys from 1980 to 2020. And by calculating its historical average, it was finally discovered that January 1995... Since it is closest to this average, January 1995 was selected as the January of the average meteorological year.

[0090] When the scene type indicates the generation of a high-fluctuation meteorological year scene, the selection target changes to capturing the drastic fluctuation characteristics of meteorological elements. In this case, the target scoring formula is shown in Formula 16 below: Formula 16:

[0091] In Formula 16, The score represents the dimension of months with highly volatile weather. This represents the wind energy weighting coefficient. Indicates the photovoltaic weighting coefficient. Indicates the first Year Normalized value of the standard deviation of wind speed in a month Indicates the first Year The normalized value of the monthly radiative fluctuation. Formula 16 is used to calculate the radiative fluctuation of all candidate years for each calendar month. The month with the highest score was selected directly. For example, for March, calculations showed that in March 2008, not only did the normalized standard deviation of wind speed reach 0.92, but its normalized radiative fluctuation also reached 0.88. March was significantly longer than in other years, and was therefore chosen as March in a year with highly volatile weather.

[0092] For the construction of extreme weather year scenarios, the objective is further refined to identify the "worst" combination of months that simultaneously possess high volatility and high risk of extreme events. Therefore, the objective scoring formula is shown in Formula 17 below: Formula 17:

[0093] In Formula 17, The score represents the monthly dimension of extreme weather. This represents the wind energy weighting coefficient. Indicates the photovoltaic weighting coefficient. Indicates the first Year Normalized value of the standard deviation of wind speed in a month Indicates the first Year Normalized value of the frequency of extreme wind speeds in a month Indicates the first Year The normalized value of the monthly radiation fluctuation. Indicates the first Year The normalized value of the continuity of cloudy and rainy weather in a month. Formula 17 reflects an ideal extreme weather month, which should simultaneously exhibit high wind speed fluctuations and high outage risk in terms of wind energy, and high radiation fluctuations and prolonged cloudy and rainy weather risk in terms of photovoltaics. For example, when selecting July months for extreme weather years, July 2012 was likely the most extreme and representative of all July months due to its high wind speed fluctuations, relatively high number of extreme wind days, strong radiation fluctuations, and a continuous 5-day cloudy and rainy process.

[0094] In this way, through the target scoring and screening mechanism bound to the scene type, the purpose-oriented and feature-enhanced generation of meteorological scenes is realized, changing the limitation of traditional methods that only generate single, static, typical scenes. By designing dedicated mathematical filters for three different engineering analysis needs—average, high fluctuation, and extreme—it can proactively and accurately extract and aggregate the most relevant meteorological segments from historical long-sequence data. This not only significantly improves the fit between the generated meteorological scenes and specific analysis targets, but also makes the characterization of the boundary conditions of new energy output more precise, thus providing a more reliable, challenging, and scientific data foundation for the planning and design of new energy power plants and the operation optimization of power systems.

[0095] The method provided in this application constructs multi-dimensional comprehensive indicators for wind and photovoltaic power respectively, and selects the best matching historical data for each month based on different scenario types for splicing. This not only preserves the authenticity of historical data, but also strengthens the feature extraction of volatility and extreme events through an index-based screening mechanism. This generates typical meteorological scenarios that are more in line with the planning and operation needs of new power systems, substantially improving the accuracy of meteorological scenario characterization and boundary coverage. It can provide more adaptive and reliable meteorological boundary inputs for the planning and design, energy storage configuration and operation optimization of new energy power plants, and effectively alleviate the control and configuration risks caused by scenario distortion.

[0096] Furthermore, as Figure 1 In a specific implementation of the method, this application provides a typical meteorological year scene generation device based on a key indicator set, such as... Figure 2 As shown, the device includes: a wind energy index generation module 201, a photovoltaic index generation module 202, and a typical meteorological year scenario generation module 203.

[0097] The wind energy index generation module 201 is used to acquire meteorological data from multiple historical years and calculate the comprehensive wind energy index corresponding to each historical year based on the meteorological data from multiple historical years. The photovoltaic index generation module 202 is used to calculate the comprehensive photovoltaic index corresponding to each historical year based on the meteorological data of the multiple historical years. The typical meteorological year scene generation module 203 is used to determine the scene type of the typical meteorological year scene to be generated. Based on the scene type, combined with the wind energy comprehensive index and photovoltaic comprehensive index corresponding to each historical year, the module selects the corresponding target historical month data for each of the 12 calendar months in the meteorological data of the multiple historical years, and organizes the 12 target historical month data corresponding to the 12 calendar months into a complete year sequence to obtain a typical meteorological year scene that matches the scene type.

[0098] In specific application scenarios, the wind energy index generation module 201 is used to calculate the uncertainty risk value of each historical year based on the meteorological data corresponding to that historical year. Wind speed standard deviation Wind speed variation coefficient Extreme wind speed daily frequency and wind direction concentration The uncertainty risk value for the historical years is calculated using the following formula. Wind speed standard deviation Wind speed variation coefficient Extreme wind speed daily frequency and wind direction concentration Calculations were performed to obtain the comprehensive wind energy index for the historical years. ,

[0099] in, Represents the coefficient of variation of wind speed The corresponding normalized weighting coefficients, Indicates the standard deviation of wind speed The corresponding normalized weighting coefficients, Indicates the daily frequency of extreme wind speeds The corresponding normalized weighting coefficients, Indicates wind direction concentration The corresponding normalized weighting coefficients, Indicates the value of uncertainty risk The corresponding normalized weighting coefficients.

[0100] In a specific application scenario, the wind energy index generation module 201 is used to calculate the historical year's meteorological data using the following formula to obtain the uncertainty risk value of the historical year. ,

[0101] in, The average wind speed for the historical year is represented by the following formula, which is used to calculate the standard deviation of wind speed for the historical year based on the meteorological data for that historical year. ,

[0102] in, Indicates the number of hours in a year. Indicates the first wind speed per hour, The average wind speed for the historical year is represented by the following formula: The wind speed variation coefficient for the historical year is calculated using the meteorological data corresponding to that historical year. ,

[0103] in, This represents the standard deviation of wind speed for the stated historical years. The average wind speed for the historical year is represented by the formula below. The daily frequency of extreme wind speeds for the historical year is calculated using the meteorological data for that historical year. ,

[0104] in, This represents the total number of days in the historical year. Indicates the first wind speed per hour, This represents the wind speed cutoff from the wind turbine; the wind direction concentration for the historical year is calculated using the following formula based on the meteorological data corresponding to that historical year. ,

[0105] in, Indicates the number of hours in a year. Indicates the first The wind direction at that hour.

[0106] In a specific application scenario, the photovoltaic index generation module 202 is used to calculate the average annual radiation of each historical year based on the meteorological data of that historical year. Annual radiation fluctuation Typical Rainy Day Continuity Indicators Probability of sunny days and maximum daily radiation value The average annual radiation for the historical years is calculated using the following formula. Annual radiation fluctuation Typical Rainy Day Continuity Indicators Probability of sunny days and maximum daily radiation value Calculations were performed to obtain the comprehensive photovoltaic index for the historical years. ,

[0107] in, Indicates average annual radiation The corresponding dynamic weighting coefficients, Indicates the annual radiation fluctuation. The corresponding dynamic weighting coefficients, Indicators representing the continuity of typical rainy days The corresponding dynamic weighting coefficients, Indicates the probability of a sunny day. The corresponding dynamic weighting coefficients, Indicates the daily maximum radiation value The corresponding dynamic weighting coefficients, This represents the average daily radiation level throughout the year.

[0108] In a specific application scenario, the photovoltaic index generation module 202 is used to calculate the average annual radiation of the historical year based on the meteorological data corresponding to the historical year using the following formula. ,

[0109] in, Indicates the number of hours in a year. Indicates the first The total horizontal irradiance per hour; the annual irradiance fluctuation of the historical year is calculated using the meteorological data corresponding to the historical year using the following formula. ,

[0110] in, Indicates the number of hours in a year. Indicates the first Total horizontal irradiance per hour Indicates the first The total horizontal irradiance per hour; the typical rainy day continuity index for the historical year is obtained by calculating the meteorological data corresponding to the historical year using the following formula. ,

[0111] in, Indicates an indicator function, Indicates the first The start time of a series of low-radiation events. Indicates the first The total number of hours of a continuous low-radiation event. Indicates from The number of hours to begin shifting backwards, Indicates at time Total irradiance on the horizontal plane, The low radiation threshold is indicated; the probability of sunny days for the historical year is calculated using the following formula based on the meteorological data corresponding to the historical year. ,

[0112] in, This represents the total number of days in the historical year. Indicates the first The average daily total horizontal irradiance of the day The threshold for determining a clear day is indicated; the maximum daily radiation value for the historical year is calculated using the following formula based on the meteorological data for that historical year. ,

[0113] in, This represents the total number of days in the historical year. Indicates the first Total horizontal irradiance per hour It represents the time interval between two consecutive measurements of the total irradiance of the horizontal plane.

[0114] In specific application scenarios, the typical meteorological year scenario generation module 203 is used to normalize the monthly meteorological data of each calendar month in each historical year using the following formula, to obtain the normalized value of each calendar month in each historical year.

[0115] in, Indicates the first Year The month of The normalized value for each indicator type for the corresponding month Indicates the first Year The month of The original indicator values ​​corresponding to each indicator type in the meteorological data of the corresponding historical years. Indicates the first year in all years The month of The minimum indicator value for each indicator type Indicates the first year in all years The month of The maximum index value for each index type; principal component analysis is performed on the comprehensive wind energy index and comprehensive photovoltaic index corresponding to each historical year to obtain the wind energy weight coefficient. and photovoltaic weighting coefficient Combined with the aforementioned wind energy weighting coefficient and the photovoltaic weighting coefficient The following formula is used to calculate the corresponding monthly comprehensive score for each calendar month in each historical year. ,

[0116] in, Indicates the first Year Monthly overall score, Indicates the first Year The month of The normalized monthly value corresponding to each indicator type is obtained; the target scoring formula matching the scenario type is obtained; and the target scoring formula is used in conjunction with the monthly comprehensive score corresponding to each calendar month in each historical year. Each calendar month in each historical year is scored according to a corresponding dimension, so as to select the target historical month data that matches the scenario type for each of the 12 calendar months in the meteorological data of the multiple historical years.

[0117] In a specific application scenario, the typical meteorological year scenario generation module 203 is used to, when detecting the scenario type indication to generate an average meteorological year scenario, use the following formula as the target scoring formula, and score each calendar month in each historical year according to the corresponding dimension, so as to select the target historical month data that matches the scenario type for each of the 12 calendar months from the meteorological data of the multiple historical years.

[0118] in, Represented as the first The index of the historical year to which the target historical month data belongs. Indicates the first Year Monthly overall score, This represents the number of hours in a year. When the scenario type indicates the generation of a high-fluctuation meteorological year scenario, the following formula is used as the target scoring formula, and each calendar month in each historical year is scored according to the corresponding dimension. This is to select the target historical month data that matches the scenario type for each of the 12 calendar months from the meteorological data of the multiple historical years.

[0119] in, The score represents the dimension of months with highly volatile weather. This represents the wind energy weighting coefficient. Indicates the photovoltaic weighting coefficient. Indicates the first Year Normalized value of the standard deviation of wind speed in a month Indicates the first Year The normalized value of monthly radiation fluctuation; when the scenario type indicates the generation of an extreme weather year scenario, the following formula is used as the target scoring formula, and each calendar month in each historical year is scored according to the corresponding dimension, so as to select the target historical month data that matches the scenario type for each of the 12 calendar months in the meteorological data of the multiple historical years.

[0120] in, The score represents the monthly dimension of extreme weather. This represents the wind energy weighting coefficient. Indicates the photovoltaic weighting coefficient. Indicates the first Year Normalized value of the standard deviation of wind speed in a month Indicates the first Year Normalized value of the frequency of extreme wind speeds in a month Indicates the first Year The normalized value of the monthly radiation fluctuation. Indicates the first Year Normalized value of the continuity of cloudy and rainy weather in a month.

[0121] The device provided in this application constructs multi-dimensional comprehensive indicators for wind and photovoltaic power respectively, and selects the best matching historical data for each month based on different scenario types for splicing. This not only preserves the authenticity of historical data, but also strengthens the feature extraction of volatility and extreme events through an index-based screening mechanism. This generates typical meteorological scenarios that are more in line with the planning and operation needs of new power systems, substantially improving the accuracy of meteorological scenario characterization and boundary coverage. It can provide more adaptive and reliable meteorological boundary inputs for the planning and design, energy storage configuration and operation optimization of new energy power plants, and effectively alleviate the control and configuration risks caused by scenario distortion.

[0122] It should be noted that other corresponding descriptions of the functional units involved in the typical meteorological year scene generation device based on a key indicator set provided in this application embodiment can be found in the following references. Figure 1 The corresponding descriptions in [the document] will not be repeated here.

[0123] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0124] The above embodiments and the technical features in the embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0125] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

[0126] In an exemplary embodiment, see Figure 3 The invention also provides a computer device including a bus, a processor, a memory, and a communication interface. It may also include an input / output interface and a display device, wherein the various functional units can communicate with each other via the bus. The memory stores a computer program, and the processor executes the program stored in the memory to perform the typical meteorological year scene generation method based on key indicator sets described in the above embodiments.

[0127] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for generating typical meteorological year scenes based on a set of key indicators.

[0128] Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented in hardware or by using software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) and includes several instructions to cause a computer device (such as a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0129] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of a preferred embodiment, and the modules or processes shown in the drawings are not necessarily essential for implementing this application.

[0130] Those skilled in the art will understand that the modules in the apparatus of the implementation scenario can be distributed within the apparatus of the implementation scenario as described, or they can be located in one or more apparatuses different from this implementation scenario, with corresponding changes. The modules of the above-described implementation scenario can be combined into one module, or they can be further divided into multiple sub-modules.

[0131] The serial numbers in this application are for descriptive purposes only and do not represent the superiority or inferiority of the implementation scenario.

[0132] The above disclosures are only a few specific implementation scenarios of this application. However, this application is not limited to these. Any variations that can be conceived by those skilled in the art should fall within the protection scope of this application.

Claims

1. A method for generating typical meteorological year scenes based on a set of key indicators, characterized in that, include: Obtain meteorological data from multiple historical years, and calculate the comprehensive wind energy index for each historical year based on the meteorological data from multiple historical years; Based on the meteorological data from the multiple historical years, calculate the comprehensive photovoltaic index corresponding to each historical year; The scenario type of the typical meteorological year scenario to be generated is determined. Based on the scenario type, combined with the wind energy comprehensive index and photovoltaic comprehensive index corresponding to each historical year, the corresponding target historical month data is selected for each of the 12 calendar months in the meteorological data of the multiple historical years. The 12 target historical month data corresponding to the 12 calendar months are organized into a complete year sequence to obtain a typical meteorological year scenario that matches the scenario type.

2. The method according to claim 1, characterized in that, The calculation of the comprehensive wind energy index for each historical year based on meteorological data from multiple historical years includes: For each historical year, the corresponding historical meteorological data is used to calculate the uncertainty risk value for that historical year. Wind speed standard deviation Wind speed variation coefficient Extreme wind speed daily frequency and wind direction concentration ; The uncertainty risk value for the historical years is calculated using the following formula. Wind speed standard deviation Wind speed variation coefficient Extreme wind speed daily frequency and wind direction concentration Calculations were performed to obtain the comprehensive wind energy index for the historical years. , in, Represents the coefficient of variation of wind speed The corresponding normalized weighting coefficients, Indicates the standard deviation of wind speed The corresponding normalized weighting coefficients, Indicates the daily frequency of extreme wind speeds The corresponding normalized weighting coefficients, Indicates wind direction concentration The corresponding normalized weighting coefficients, Indicates the value of uncertainty risk The corresponding normalized weighting coefficients.

3. The method according to claim 2, characterized in that, The uncertainty risk value for the historical year is obtained by calculating the meteorological data corresponding to the historical year. Wind speed standard deviation Wind speed variation coefficient Extreme wind speed daily frequency and wind direction concentration ,include: The uncertainty risk value for the historical year is obtained by calculating the meteorological data corresponding to the historical year using the following formula. , in, This represents the average wind speed for the historical years mentioned; The standard deviation of wind speed for the historical year is obtained by calculating the meteorological data corresponding to the historical year using the following formula. , in, Indicates the number of hours in a year. Indicates the first wind speed per hour, This represents the average wind speed for the historical years mentioned; The following formula is used to calculate the wind speed variation coefficient for the corresponding historical year's meteorological data, to obtain the wind speed variation coefficient for that historical year. , in, This represents the standard deviation of wind speed for the stated historical years. This represents the average wind speed for the historical years mentioned; The following formula is used to calculate the daily frequency of extreme wind speeds for the corresponding historical years based on the meteorological data of those historical years. , in, This represents the total number of days in the historical year. Indicates the first wind speed per hour, Indicates the fan cut-out speed; The wind direction concentration for the historical year is obtained by calculating the meteorological data corresponding to the historical year using the following formula. , in, Indicates the number of hours in a year. Indicates the first The wind direction at that hour.

4. The method according to claim 1, characterized in that, The calculation of the comprehensive photovoltaic index for each historical year based on meteorological data from multiple historical years includes: For each historical year, the average annual radiation for that historical year is calculated using the corresponding meteorological data. Annual radiation fluctuation Typical Rainy Day Continuity Indicators Probability of sunny days and maximum daily radiation value ; The average annual radiation of the historical years is calculated using the following formula. Annual radiation fluctuation Typical Rainy Day Continuity Indicators Probability of sunny days and maximum daily radiation value Calculations were performed to obtain the comprehensive photovoltaic index for the historical years. , in, Indicates average annual radiation The corresponding dynamic weighting coefficients, Indicates the annual radiation fluctuation. The corresponding dynamic weighting coefficients, Indicators representing the continuity of typical rainy days The corresponding dynamic weighting coefficients, Indicates the probability of a sunny day. The corresponding dynamic weighting coefficients, Indicates the daily maximum radiation value The corresponding dynamic weighting coefficients, This represents the average daily radiation level throughout the year.

5. The method according to claim 4, characterized in that, The average annual radiation of the historical year is obtained by calculating the meteorological data corresponding to the historical year. Annual radiation fluctuation Typical Rainy Day Continuity Indicators Probability of sunny days and maximum daily radiation value ,include: The average annual radiation for the historical year is obtained by calculating the meteorological data corresponding to the historical year using the following formula. , in, Indicates the number of hours in a year. Indicates the first Total horizontal irradiance per hour; The annual radiation fluctuation of the historical year is obtained by calculating the meteorological data corresponding to the historical year using the following formula. , in, Indicates the number of hours in a year. Indicates the first Total horizontal irradiance per hour Indicates the first Total horizontal irradiance per hour; The following formula is used to calculate the typical rainy day continuity index for the historical year based on the meteorological data corresponding to the historical year. , in, Indicates an indicator function, Indicates the first The start time of a series of low-radiation events. Indicates the first The total number of hours of a continuous low-radiation event. Indicates from The number of hours to begin shifting backwards, Indicates at time Total irradiance on the horizontal plane, Indicates a low radiation threshold; The probability of sunny days for the historical year is obtained by calculating the meteorological data corresponding to the historical year using the following formula. , in, This represents the total number of days in the historical year. Indicates the first The average daily total horizontal irradiance of the day This indicates the threshold for determining sunny weather. The maximum daily radiation value for the historical year is obtained by calculating the meteorological data corresponding to the historical year using the following formula. , in, This represents the total number of days in the historical year. Indicates the first Total horizontal irradiance per hour It represents the time interval between two consecutive measurements of the total irradiance of the horizontal plane.

6. The method according to claim 1, characterized in that, The process involves determining the scenario type of the typical meteorological year scenario to be generated, and based on the scenario type, combined with the comprehensive wind energy index and comprehensive photovoltaic index corresponding to each historical year, selecting the corresponding target historical month data for each of the 12 calendar months from the meteorological data of the multiple historical years, including: The following formula is used to normalize the monthly meteorological data of each calendar month in each historical year within the corresponding historical year's meteorological data, yielding the normalized monthly value for each calendar month in each historical year. in, Indicates the first Year The month of The normalized value for each indicator type for the corresponding month Indicates the first Year The month of The original indicator values ​​corresponding to each indicator type in the meteorological data of the corresponding historical years. Indicates the first year in all years The month of The minimum indicator value for each indicator type Indicates the first year in all years The month of The maximum value of each indicator type; Principal component analysis was performed on the wind energy composite index and photovoltaic composite index for each historical year to obtain the wind energy weighting coefficient. and photovoltaic weighting coefficient ; Combined with the aforementioned wind energy weighting coefficient and the photovoltaic weighting coefficient The following formula is used to calculate the corresponding monthly comprehensive score for each calendar month in each historical year. , in, Indicates the first Year Monthly overall score, Indicates the first Year The month of Normalized monthly values ​​corresponding to each indicator type; Obtain the target scoring formula that matches the scenario type, and then apply the target scoring formula in conjunction with the monthly comprehensive score corresponding to each calendar month in each historical year. Each calendar month in each historical year is scored according to a corresponding dimension, so as to select the target historical month data that matches the scenario type for each of the 12 calendar months in the meteorological data of the multiple historical years.

7. The method according to claim 6, characterized in that, The process involves obtaining a target scoring formula that matches the scenario type, and then combining this target scoring formula with the monthly comprehensive score corresponding to each calendar month in each historical year. Each calendar month in each historical year is scored according to a corresponding dimension, so as to select the target historical month data that matches the scene type for each of the 12 calendar months from the meteorological data of the multiple historical years, including: When the scene type indication is detected to generate an average meteorological year scene, the following formula is used as the target scoring formula, and each calendar month in each historical year is scored according to the corresponding dimension, so as to select the target historical month data that matches the scene type for each of the 12 calendar months from the meteorological data of the multiple historical years. in, Represented as the first The index of the historical year to which the target historical month data belongs. Indicates the first Year Monthly overall score, Indicates the number of hours in a year; When the scenario type indicates the generation of a high-fluctuation meteorological year scenario, the following formula is used as the target scoring formula, and each calendar month in each historical year is scored according to the corresponding dimension, so as to select the target historical month data that matches the scenario type for each of the 12 calendar months from the meteorological data of the multiple historical years. in, The score represents the dimension of months with highly volatile weather. This represents the wind energy weighting coefficient. Indicates the photovoltaic weighting coefficient. Indicates the first Year Normalized value of the standard deviation of wind speed in a month Indicates the first Year The normalized value of the monthly radiation fluctuation; When the scenario type indication is detected to generate an extreme weather year scenario, the following formula is used as the target scoring formula, and each calendar month in each historical year is scored according to the corresponding dimension, so as to select the target historical month data that matches the scenario type for each of the 12 calendar months from the meteorological data of the multiple historical years. in, The score represents the monthly dimension of extreme weather. This represents the wind energy weighting coefficient. Indicates the photovoltaic weighting coefficient. Indicates the first Year Normalized value of the standard deviation of wind speed in a month Indicates the first Year Normalized value of the frequency of extreme wind speeds in a month Indicates the first Year The normalized value of the monthly radiation fluctuation. Indicates the first Year Normalized value of the continuity of cloudy and rainy weather in a month.

8. A device for generating typical meteorological year scenes based on a set of key indicators, characterized in that, include: The wind energy index generation module is used to acquire meteorological data from multiple historical years and calculate the comprehensive wind energy index for each historical year based on the meteorological data from multiple historical years. The photovoltaic index generation module is used to calculate the comprehensive photovoltaic index corresponding to each historical year based on the meteorological data of the multiple historical years. The typical meteorological year scene generation module is used to determine the scene type of the typical meteorological year scene to be generated. Based on the scene type, combined with the wind energy comprehensive index and photovoltaic comprehensive index corresponding to each historical year, the module selects the corresponding target historical month data for each of the 12 calendar months from the meteorological data of the multiple historical years, and organizes the 12 target historical month data corresponding to the 12 calendar months into a complete year sequence to obtain a typical meteorological year scene that matches the scene type.

9. A device comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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