New energy extreme characteristic analysis method and system based on statistical analysis and extreme value theory

Abstract

The invention discloses a new energy extreme characteristic analysis method and system based on statistical analysis and an extreme value theory, and the method comprises the steps: collecting and sorting data, collecting new energy ideal output data, and carrying out the preprocessing of the data; three types of new energy extreme events are obtained from the three aspects of electric power, electric quantity and climbing and based on the preprocessed new energy ideal output data; analyzing statistical characteristics of the three types of new energy extreme events, namely analyzing occurrence frequencies, interannual characteristics, seasonal characteristics and spatial characteristics of the three types of new energy extreme events; and further evaluating the three types of new energy extreme events by applying an extreme value theory, and obtaining the recurrence levels of the three types of new energy extreme events in different recurrence periods and corresponding confidence intervals by adopting a maximum value sequence method. According to the invention, the influence of extreme events on the power system under high-proportion new energy can be further studied.

Description

Technical Field

[0001] This invention belongs to the field of power system new energy characteristic analysis, and relates to a method and system for analyzing the extreme characteristics of new energy based on statistical analysis and extreme value theory. Background Technology

[0002] The increasing proportion of renewable energy sources has amplified the randomness and uncertainty of power supply, leading to a situation where rising power loads coexist with difficulties in absorbing them. Extreme weather events exacerbate the temporal and spatial mismatch and peak-valley mismatch between power supply and demand, severely impacting the power balance of the power system.

[0003] In recent years, with the intensification of climate change, such as the greenhouse effect, extreme weather events have become increasingly frequent globally. Correspondingly, extreme renewable energy generation events have also significantly impacted the reliable operation of power systems, attracting increasing attention. While extreme renewable energy generation events are infrequent, when they do occur, they have a profound impact on the supply and demand balance of the power system.

[0004] In summary, extreme renewable energy power generation events are becoming increasingly frequent in power systems. In order to accelerate the construction of power systems with a high proportion of renewable energy, it is urgent to carry out comprehensive research on the extreme characteristics of renewable energy. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing research on extreme characteristics, based on the current situation of high proportion of new energy sources. It defines various extreme events, conducts statistical analysis on these events, and establishes a method and system for analyzing the extreme characteristics of new energy sources based on extreme value theory. This is to further study the impact of extreme events on the power system under high proportion of new energy sources.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: Methods for analyzing the extreme characteristics of new energy sources based on statistical analysis and extreme value theory include: Step 1: Collect and organize data, collect ideal power output data for new energy sources, and preprocess the data; Step 2: From the perspectives of power, electricity, and ramp-up, and based on the pre-processed ideal output data of new energy sources, three types of extreme events of new energy sources are obtained; Step 3: Analyze the statistical characteristics of the three types of new energy extreme events, namely, analyze the frequency, interannual characteristics, seasonal characteristics, and spatial characteristics of the three types of new energy extreme events; Step 4: Further evaluate the three types of new energy extreme events using extreme value theory. Employ the maximum value sequence method to obtain the recurrence levels and corresponding confidence intervals of the three types of new energy extreme events at different recurrence periods.

[0007] A further improvement of this invention lies in step two: from the perspectives of electricity, power output, and ramp-up, and based on the preprocessed ideal output data of new energy sources, three types of extreme events for new energy sources are obtained, including: Define power events: Select different time windows to obtain the maximum output of new energy sources in the corresponding time windows. Minimal output of new energy sources ; Obtained through the following method

[0008] in, , T It is the total duration of the data. d It is the corresponding time window; Define extreme power generation events: First, define prolonged low power output events: Definition 1: For a given duration, the power output of renewable energy sources is consistently below a given power output threshold; Definition 2: For a given duration, the total power generation of renewable energy sources is below a given power output threshold. Second, define prolonged high power output events: Definition 1: For a given duration, the power output of renewable energy sources is consistently above a given power output threshold; Definition 2: For a given duration, the total power generation of renewable energy sources is above a given power output threshold. Define a ramp event: Select time windows of different durations. Ramp is defined as the difference between the maximum and minimum output values ​​within that time window.

[0009] in, , T It is the total duration of the data. d It refers to the corresponding time window.

[0010] A further improvement of this invention lies in step four: applying extreme value theory to further evaluate the three types of new energy extreme events, and using the maximum value sequence method to obtain the recurrence levels and corresponding confidence intervals for different recurrence periods of the three types of new energy extreme events, including: When performing extreme value analysis, the following approach is taken for extreme power events: for the maximum value sequence, the annual or monthly minimum value is taken; for the minimum value sequence, the annual or monthly maximum value is taken. The first theorem of extreme value theory states that a large number of random variables, after proper standardization, will converge to one of three possible extreme value distributions: the Gumbel distribution, the Fréchet distribution, and the Weibull distribution. The parameters of the extreme value distribution are estimated using maximum likelihood estimation, including: (4) (5) In the formula: These are the estimated parameters; It is the likelihood function; Is a given parameter The probability density function under; It is the log-likelihood function.

[0011] A further improvement of this invention is that the Gumbel probability density function is expressed as: (1) in, It is a location parameter, representing the median of the distribution, i.e., the location of the distribution; It is a scale parameter that affects the scale of the distribution and is related to the extent of the distribution's expansion; It is a shape parameter that controls the shape of the distribution.

[0012] A further improvement of this invention is that the probability density function of the Fréchet distribution is expressed as: (2) when At that time, the distribution exhibits a heavy-tailed property, that is, as... As the probability density of the tail increases, the probability density decreases more slowly, resulting in a higher probability of extreme events occurring; when When the Fréchet distribution degenerates into an exponential distribution, its tails are light-tailed, similar to the tails of an exponential distribution; when At that time, the distribution is bounded.

[0013] A further improvement of this invention is that the probability density function of the Weibull distribution is expressed as: (3) in, It is a position parameter; It is a scale parameter; It is a shape parameter.

[0014] A new energy extreme characteristic analysis system based on statistical analysis and extreme value theory includes: The data collection and preprocessing unit collects and organizes data, including ideal power output data for new energy sources, and performs preprocessing on the data. The data analysis unit, from the perspectives of power, electricity, and ramp-up, and based on preprocessed ideal power output data of new energy sources, derives three types of extreme events of new energy sources; The statistical characteristic analysis unit analyzes the statistical characteristics of three types of new energy extreme events, namely, the frequency, interannual characteristics, seasonal characteristics, and spatial characteristics of the three types of new energy extreme events. The evaluation unit uses extreme value theory to further evaluate three types of new energy extreme events. It adopts the maximum value sequence method to obtain the recurrence level and corresponding confidence interval of the three types of new energy extreme events at different recurrence periods.

[0015] A further improvement of this invention lies in that, in the data analysis unit, from the perspectives of power, electricity, and ramp-up, and based on preprocessed ideal power output data of new energy sources, three types of extreme events of new energy sources are obtained, including: Define power events: Select different time windows to obtain the maximum output of new energy sources in the corresponding time windows. Minimal output of new energy sources ; Obtained through the following method

[0016] in, , T It is the total duration of the data. d It is the corresponding time window; Define extreme power generation events: First, define prolonged low power output events: Definition 1: For a given duration, the power output of renewable energy sources is consistently below a given power output threshold; Definition 2: For a given duration, the total power generation of renewable energy sources is below a given power output threshold. Second, define prolonged high power output events: Definition 1: For a given duration, the power output of renewable energy sources is consistently above a given power output threshold; Definition 2: For a given duration, the total power generation of renewable energy sources is above a given power output threshold. Define a ramp event: Select time windows of different durations. Ramp is defined as the difference between the maximum and minimum output values ​​within that time window.

[0017] in, , T It is the total duration of the data. d It refers to the corresponding time window.

[0018] A further improvement of this invention lies in the following: In the evaluation unit, extreme value theory is applied to further evaluate the three types of new energy extreme events. The maximum value sequence method is used to obtain the recurrence levels and corresponding confidence intervals for different recurrence periods of the three types of new energy extreme events, including: When performing extreme value analysis, the following approach is taken for extreme power events: for the maximum value sequence, the annual or monthly minimum value is taken; for the minimum value sequence, the annual or monthly maximum value is taken. The first theorem of extreme value theory states that a large number of random variables, after proper standardization, will converge to one of three possible extreme value distributions: the Gumbel distribution, the Fréchet distribution, and the Weibull distribution. The parameters of the extreme value distribution are estimated using maximum likelihood estimation, including: (4) (5) In the formula: These are the estimated parameters; It is the likelihood function; Is a given parameter The probability density function under; It is the log-likelihood function.

[0019] A further improvement of this invention is that the Gumbel probability density function is expressed as: (1) in, It is a location parameter, representing the median of the distribution, i.e., the location of the distribution; It is a scale parameter that affects the scale of the distribution and is related to the extent of the distribution's expansion; It is a shape parameter that controls the shape of the distribution.

[0020] Compared with the prior art, the present invention has at least the following beneficial technical effects: This invention provides a method and system for analyzing the extreme characteristics of new energy sources based on statistical analysis and extreme value theory. Three types of extreme events in power systems are defined from the perspectives of power, energy consumption, and ramp-up. The invention comprehensively considers the extreme characteristics problems brought about by the high proportion of new energy sources integrated into new power systems. Statistical analysis and analysis based on extreme value theory are performed for each of the three types of new energy extreme events. This invention provides an analytical method for the extreme characteristics of power systems with a high proportion of new energy sources and has certain application prospects. Attached Figure Description

[0021] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0022] Figure 1 A PDF diagram illustrating the maximum wind power output over 24 hours; Figure 2 A schematic diagram showing the monthly minimum value of the maximum wind power output over 24 hours; Figure 3 A schematic diagram showing the once-in-a-decade minimum values ​​of the maximum wind speeds in each time window; Figure 4 This is a schematic diagram showing the monthly maximum values ​​during periods of low wind power. Figure 5 This is a schematic diagram illustrating the return period level during periods of low wind power. Figure 6 A PDF diagram illustrating the hill climb; Figure 7 This is a diagram illustrating the monthly maximum value of climbing events; Figure 8 A diagram illustrating the recurrence period of a hill-climbing event; Figure 9 This is a structural block diagram of the new energy extreme characteristic analysis system based on statistical analysis and extreme value theory of the present invention. Detailed Implementation

[0023] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the spirit or scope of the invention. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0024] In the description of this invention, it should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0025] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0026] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0027] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0028] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0029] Example 1 The present invention provides a method for analyzing the extreme characteristics of new energy sources based on statistical analysis and extreme value theory, comprising: Step 1: Data Collection and Organization. Collect ideal power output data for new energy sources and process abnormal and missing power output data.

[0030] Step Two: Define three types of extreme events related to renewable energy. Describe the extreme characteristics of renewable energy from three perspectives: electricity, power generation, and ramp-up.

[0031] Define power events. Select different time windows to obtain the maximum output of new energy sources for the corresponding time windows. Minimal output of new energy sources . Obtained through the following method

[0032] in, , T It is the total duration of the data. d It refers to the corresponding time window.

[0033] Define extreme power generation events. First, define prolonged low-output events: Definition 1: For a given duration, the output of renewable energy sources consistently falls below a given output threshold; Definition 2: For a given duration, the total power generation of renewable energy sources falls below a given power threshold. Second, define prolonged high-output events: Definition 1: For a given duration, the output of renewable energy sources consistently exceeds a given output threshold; Definition 2: For a given duration, the total power generation of renewable energy sources exceeds a given power threshold.

[0034] Define the ramp-up event. Select time windows of different durations; ramp-up is defined as the difference between the maximum and minimum output values ​​within that time window.

[0035] in, , T It is the total duration of the data. d It refers to the corresponding time window.

[0036] Step 3: Analyze the statistical characteristics of various types of new energy extreme events, namely, analyze the occurrence frequency, interannual characteristics, seasonal characteristics, and spatial characteristics of the three types of new energy extreme events.

[0037] Step 4: Further evaluate the three types of renewable energy extreme events using extreme value theory. Employing the maximum value sequence method, obtain the recurrence levels and corresponding confidence intervals for different return periods of the three types of renewable energy extreme events. The treatment of power extreme events during extreme value analysis is as follows: for the maximum value sequence, take the annual or monthly minimum value; for the minimum value sequence, take the annual or monthly maximum value.

[0038] Extremum theory includes two important theorems: the first theorem and the second theorem.

[0039] 1) First Theorem The first theorem describes the types of extreme value distributions. It states that for a large number of random variables, their maxima or minima, after appropriate standardization, will converge to one of three possible extreme value distributions, namely: (1) Gumbel distribution: The Gumbel distribution is a distribution with both light and heavy tails. It is mainly used to describe the maximum value of a random variable, and is therefore often used to model the probability distribution of maximum values ​​or extreme events.

[0040] The Gumbel distribution function and probability density function are as follows: (1) in, It is a location parameter, representing the median of the distribution, i.e., the location of the distribution; It is a scale parameter that affects the scale of the distribution and is related to the extent of the distribution's expansion; It is a shape parameter that controls the shape of the distribution.

[0041] (2) Fréchet distribution: The Fréchet distribution is a heavy-tailed distribution, typically used to describe the distribution of the maxima of random variables. It is suitable for extreme cases exhibiting heavy-tailed characteristics, i.e., cases where the tails decrease rapidly. When a sequence of random variables follows a certain "heavy-tailed distribution," its maxima or minima will converge to the Fréchet distribution under appropriate standardization. The Fréchet distribution is used to describe extreme cases with heavy-tailed characteristics.

[0042] The probability density function of the Fréchet distribution can be expressed as: (2) when At that time, the distribution exhibits a heavy-tailed property, that is, as... As the probability density of the tail increases, the probability density decreases more slowly, resulting in a higher probability of extreme events occurring; when When the Fréchet distribution degenerates into an exponential distribution, its tails are light-tailed, similar to the tails of an exponential distribution; when At that time, the distribution is bounded.

[0043] (3) Weibull distribution: The Weibull distribution is a light-tailed distribution, typically used to describe the distribution of the minimum values ​​of random variables. It is suitable for extreme value cases with a light-tailed characteristic, i.e., where the tail decreases slowly. When a sequence of random variables follows a certain "light-tailed distribution," its maxima or minima will converge to the Weibull distribution under appropriate standardization. The Weibull distribution is commonly used to describe extreme value cases with a light-tailed characteristic.

[0044] The probability density function of the Weibull distribution can be expressed as: (3) 2) Second Theorem The second theorem provides detailed information on parameter estimation and properties of extreme value distributions. It allows us to estimate the parameters of the extreme value distribution using sample data; through maximum likelihood estimation, parameters such as location, scale, and shape can be obtained. These estimates can be used to construct approximate models of the extreme value distribution for extreme value estimation.

[0045] The second theorem also includes methods for estimating sample properties of extreme value distributions, such as the mean, variance, and covariance. It also addresses the properties of extreme value distributions, such as the expressions for the distribution function, density function, and quantile function. These expressions can help analyze and understand the properties of extreme value distributions. Furthermore, it covers how to construct confidence intervals and assumptions for extreme value distributions to estimate parameter uncertainty.

[0046] Example 2 The following section will provide a more detailed explanation using new energy data from the Northwest region as an example.

[0047] The data used are historical wind power output data for Northwest China from 2020 to 2023. The historical data for each year were normalized, with the renewable energy output benchmark being the installed capacity of renewable energy on December 31st of each year. A selection of events is used to illustrate the practical application of the analytical methods.

[0048] 1) Extreme power events: By providing different time windows, the maximum wind power output of the corresponding window is obtained, and then the annual / monthly maximum is obtained. Extreme value fitting is performed to obtain the reproduction level.

[0049] The selected time windows are 1, 2, 10, 24, 36, 48, and 72 hours. A sliding window method is used to obtain the maximum wind power output sequence for each window. The monthly minimum value of the maximum wind power output sequence is taken to form a new sequence. This sequence is then fitted with a generalized extreme value distribution to obtain the once-in-a-decade minimum value of the maximum wind power output for that time window.

[0050] The time window is the maximum wind power output within 24 hours, such as Figure 1 As shown, the maximum wind power output during this time window is mostly concentrated between [0.15, 0.45].

[0051] A new sequence is formed by the monthly minimum values ​​of the maximum wind power output sequence for each time window. The time window is a 24-hour period, and the monthly minimum values ​​are as follows: Figure 2 As shown in Table 1, the monthly minimum values ​​for each time window are fitted using a generalized extreme value distribution, yielding the once-in-a-decade minimum values ​​for the corresponding time windows. Figure 3 As shown, the once-in-a-decade minimum increases with the increase of the time window.

[0052] Table 1. Maximum and minimum wind speed values ​​for each time window (once in ten years).

[0053] 2) Extreme power events: The definition of extreme power generation events adopts Definition 1 for prolonged low power output events. A threshold of 0.05 is selected. A low wind power period is defined as the time window during which wind power output consistently falls below the selected threshold. The longest low wind power period in each month is recorded, i.e., the monthly maximum value. The monthly maximum value is fitted using a generalized extreme value distribution to obtain the duration of a once-in-a-decade low wind power period. If there is no period in a month with output below 0.05, the monthly maximum value is set to 0.

[0054] Monthly maximum value, such as Figure 4 As shown, the duration is generally between 0 and 17 hours, but reached a maximum of 23.75 hours in December 2024. This yields a low wind power period of 22.777 hours (a once-in-a-decade event). The recurrence interval of the low wind power period is as follows: Figure 5 As shown, the horizontal curve gradually approaches a certain finite value.

[0055] 3) Extreme climbing events: A time window of 60 minutes was selected, and a sliding window was used to obtain the climbing sequence. Its probability distribution is shown in the figure below. Figure 6 As shown. A new sequence is constructed by taking the maximum value each month, as follows. Figure 7 As shown. The sequence is fitted with a generalized extreme value distribution to obtain the recurrence period of the climbing event within a 60-minute time window, as shown. Figure 8 As shown.

[0056] Example 3 like Figure 9 As shown, the new energy extreme characteristic analysis system based on statistical analysis and extreme value theory provided by this invention includes: The data collection and preprocessing unit collects and organizes data, including ideal power output data for new energy sources, and performs preprocessing on the data. The data analysis unit, from the perspectives of power, electricity, and ramp-up, and based on preprocessed ideal power output data of new energy sources, derives three types of extreme events of new energy sources; The statistical characteristic analysis unit analyzes the statistical characteristics of three types of new energy extreme events, namely, the frequency, interannual characteristics, seasonal characteristics, and spatial characteristics of the three types of new energy extreme events. The evaluation unit uses extreme value theory to further evaluate three types of new energy extreme events. It adopts the maximum value sequence method to obtain the recurrence level and corresponding confidence interval of the three types of new energy extreme events at different recurrence periods.

[0057] In the data analysis unit of this embodiment: from the perspectives of power, electricity, and ramp-up, and based on the preprocessed ideal output data of new energy sources, three types of extreme events of new energy sources are obtained, including: Define power events: Select different time windows to obtain the maximum output of new energy sources in the corresponding time windows. Minimal output of new energy sources ; Obtained through the following method

[0058] in, , T It is the total duration of the data. d It is the corresponding time window; Define extreme power generation events: First, define prolonged low power output events: Definition 1: For a given duration, the power output of renewable energy sources is consistently below a given power output threshold; Definition 2: For a given duration, the total power generation of renewable energy sources is below a given power output threshold. Second, define prolonged high power output events: Definition 1: For a given duration, the power output of renewable energy sources is consistently above a given power output threshold; Definition 2: For a given duration, the total power generation of renewable energy sources is above a given power output threshold. Define a ramp event: Select time windows of different durations. Ramp is defined as the difference between the maximum and minimum output values ​​within that time window.

[0059] in, , T It is the total duration of the data. d It refers to the corresponding time window.

[0060] In the evaluation unit of this embodiment: extreme value theory is used to further evaluate the three types of new energy extreme events. The maximum value sequence method is used to obtain the recurrence levels and corresponding confidence intervals of the three types of new energy extreme events at different return periods, including: When performing extreme value analysis, the following approach is taken for extreme power events: for the maximum value sequence, the annual or monthly minimum value is taken; for the minimum value sequence, the annual or monthly maximum value is taken. The first theorem of extreme value theory states that a large number of random variables, after proper standardization, will converge to one of three possible extreme value distributions: the Gumbel distribution, the Fréchet distribution, and the Weibull distribution. The parameters of the extreme value distribution are estimated using maximum likelihood estimation, including: (4) (5) In the formula: These are the estimated parameters; It is the likelihood function; Is a given parameter The probability density function under; It is the log-likelihood function.

[0061] In this embodiment, the Gumbel probability density function is expressed as: (1) in, It is a location parameter, representing the median of the distribution, i.e., the location of the distribution; It is a scale parameter that affects the scale of the distribution and is related to the extent of the distribution's expansion; It is a shape parameter that controls the shape of the distribution.

[0062] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the scope of the invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0063] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can be appropriately combined to form other embodiments that can be understood by those skilled in the art. The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A method for analyzing the extreme characteristics of new energy sources based on statistical analysis and extreme value theory, characterized in that: include: Step 1: Collect and organize data, collect ideal power output data for new energy sources, and preprocess the data; Step 2: From the perspectives of power, electricity, and ramp-up, and based on the pre-processed ideal output data of new energy sources, three types of extreme events of new energy sources are obtained; Step 3: Analyze the statistical characteristics of the three types of new energy extreme events, namely, analyze the frequency, interannual characteristics, seasonal characteristics, and spatial characteristics of the three types of new energy extreme events; Step 4: Further evaluate the three types of new energy extreme events using extreme value theory. Employ the maximum value sequence method to obtain the recurrence levels and corresponding confidence intervals of the three types of new energy extreme events at different recurrence periods.

2. The method for analyzing the extreme characteristics of new energy sources based on statistical analysis and extreme value theory according to claim 1, characterized in that, Step Two: From the perspectives of electricity, power generation, and ramp-up, and based on the preprocessed ideal output data of new energy sources, three types of extreme events for new energy sources are identified, including: Define power events: Select different time windows to obtain the maximum output of new energy sources in the corresponding time windows. Minimal output of new energy sources ; Obtained through the following method in, , T It is the total duration of the data. d It is the corresponding time window; Define extreme power generation events: First, define prolonged low power output events: Definition 1: For a given duration, the power output of renewable energy sources is consistently below a given power output threshold; Definition 2: For a given duration, the total power generation of renewable energy sources is below a given power output threshold. Second, define prolonged high power output events: Definition 1: For a given duration, the power output of renewable energy sources is consistently above a given power output threshold; Definition 2: For a given duration, the total power generation of renewable energy sources is above a given power output threshold. Define a ramp event: Select time windows of different durations. Ramp is defined as the difference between the maximum and minimum output values ​​within that time window. in, , T It is the total duration of the data. d It refers to the corresponding time window.

3. The method for analyzing the extreme characteristics of new energy sources based on statistical analysis and extreme value theory according to claim 1, characterized in that, Step 4: Further evaluate the three types of new energy extreme events using extreme value theory. Employing the maximum value sequence method, obtain the recurrence levels and corresponding confidence intervals for different return periods of the three types of new energy extreme events, including: When performing extreme value analysis, the following approach is taken for extreme power events: for the maximum value sequence, the annual or monthly minimum value is taken; for the minimum value sequence, the annual or monthly maximum value is taken. The first theorem of extreme value theory states that a large number of random variables, after proper standardization, will converge to one of three possible extreme value distributions: the Gumbel distribution, the Fréchet distribution, and the Weibull distribution. The parameters of the extreme value distribution are estimated using maximum likelihood estimation, including: (4) (5) In the formula: These are the estimated parameters; It is the likelihood function; Is a given parameter The probability density function under; It is the log-likelihood function.

4. The method for analyzing the extreme characteristics of new energy sources based on statistical analysis and extreme value theory according to claim 3, characterized in that, The Gumbel probability density function is expressed as: (1) in, It is a location parameter, representing the median of the distribution, i.e., the location of the distribution; It is a scale parameter that affects the scale of the distribution and is related to the extent of the distribution's expansion; It is a shape parameter that controls the shape of the distribution.

5. The method for analyzing the extreme characteristics of new energy sources based on statistical analysis and extreme value theory according to claim 3, characterized in that, The probability density function of the Fréchet distribution is expressed as: (2) when At that time, the distribution exhibits a heavy-tailed property, that is, as... As the probability density of the tail increases, the probability density decreases more slowly, resulting in a higher probability of extreme events occurring; when When the Fréchet distribution degenerates into an exponential distribution, its tails are light-tailed, similar to the tails of an exponential distribution; when At that time, the distribution is bounded.

6. The method for analyzing the extreme characteristics of new energy sources based on statistical analysis and extreme value theory according to claim 3, characterized in that, The probability density function of the Weibull distribution is expressed as: (3) in, It is a position parameter; It is a scale parameter; It is a shape parameter.

7. A new energy extreme characteristic analysis system based on statistical analysis and extreme value theory, characterized in that, include: The data collection and preprocessing unit collects and organizes data, including ideal power output data for new energy sources, and performs preprocessing on the data. The data analysis unit, from the perspectives of power, electricity, and ramp-up, and based on preprocessed ideal output data of new energy sources, derives three types of extreme events for new energy sources; The statistical characteristic analysis unit analyzes the statistical characteristics of three types of new energy extreme events, namely, the frequency, interannual characteristics, seasonal characteristics, and spatial characteristics of the three types of new energy extreme events. The evaluation unit uses extreme value theory to further evaluate three types of new energy extreme events. It adopts the maximum value sequence method to obtain the recurrence level and corresponding confidence interval of the three types of new energy extreme events at different recurrence periods.

8. The new energy extreme characteristic analysis system based on statistical analysis and extreme value theory according to claim 7, characterized in that, In the data analysis unit: from the perspectives of power, electricity, and ramp-up, and based on preprocessed ideal output data of new energy sources, three types of extreme events of new energy sources are obtained, including: Define power events: Select different time windows to obtain the maximum output of new energy sources in the corresponding time windows. Minimal output of new energy sources ; Obtained through the following method in, , T It is the total duration of the data. d It is the corresponding time window; Define extreme power generation events: First, define prolonged low power output events: Definition 1: For a given duration, the power output of renewable energy sources is consistently below a given power output threshold; Definition 2: For a given duration, the total power generation of renewable energy sources is below a given power output threshold. Second, define prolonged high power output events: Definition 1: For a given duration, the power output of renewable energy sources is consistently above a given power output threshold; Definition 2: For a given duration, the total power generation of renewable energy sources is above a given power output threshold. Define a ramp event: Select time windows of different durations. Ramp is defined as the difference between the maximum and minimum output values ​​within that time window. in, , T It is the total duration of the data. d It refers to the corresponding time window.

9. The new energy extreme characteristic analysis system based on statistical analysis and extreme value theory according to claim 8, characterized in that, In the evaluation unit: extreme value theory is used to further evaluate three types of new energy extreme events. The maximum value sequence method is employed to obtain the recurrence levels and corresponding confidence intervals for different return periods of the three types of new energy extreme events, including: When performing extreme value analysis, the following approach is taken for extreme power events: for the maximum value sequence, the annual or monthly minimum value is taken; for the minimum value sequence, the annual or monthly maximum value is taken. The first theorem of extreme value theory states that a large number of random variables, after proper standardization, will converge to one of three possible extreme value distributions: the Gumbel distribution, the Fréchet distribution, and the Weibull distribution. The parameters of the extreme value distribution are estimated using maximum likelihood estimation, including: (4) (5) In the formula: These are the estimated parameters; It is the likelihood function; Is a given parameter The probability density function under; It is the log-likelihood function.

10. The new energy extreme characteristic analysis system based on statistical analysis and extreme value theory according to claim 9, characterized in that, The Gumbel probability density function is expressed as: (1) in, It is a location parameter, representing the median of the distribution, i.e., the location of the distribution; It is a scale parameter that affects the scale of the distribution and is related to the extent of the distribution's expansion; It is a shape parameter that controls the shape of the distribution.

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