Meteorological data inversion-based wind chamber event distribution characteristic analysis method and system

Abstract

The invention discloses a wind chamber event distribution characteristic analysis method and system based on meteorological data inversion. The method comprises the following steps: converting historical wind speed data into cell wind speed data of fan hub height; converting the wind speed data of the cells into power data; performing weighted aggregation on each cell in combination with a wind energy region grading standard to obtain a wind power capacity coefficient sequence of the research region; a plurality of probability distribution models are adopted to fit the wind power capacity coefficient sequence, probability distribution with the optimal fitting effect is screened out, and recognition threshold values of wind chamber events of different degrees are determined through a percentile method; combining the recognition threshold values to recognize different degrees of wind chamber events, and calculating the duration time and intensity of each wind chamber event; and carrying out quantitative analysis on the duration time and the intensity of each wind chamber event to obtain wind power output distribution characteristics of seasons, interannual periods and extreme values of each wind chamber event. According to the invention, a decision basis is provided for coping with power supply insurance challenges caused by wind power output fluctuation.

Description

Technical Field

[0001] The present invention relates to the technical field of medium- and long-term meteorological simulation and renewable energy resource assessment, and in particular to a method and system for analyzing the distribution characteristics of wind shortage events based on meteorological data inversion. Background Art

[0002] Building a new power system with new energy as the main body is not only an important direction for the transformation and upgrading of my country's power system, but also a key path to achieving the goals of carbon peak and carbon neutrality. With the accelerated advancement of the green and low-carbon transformation of electricity, the penetration rate of wind energy in the power grid continues to rise; however, wind energy has the characteristics of intermittency, volatility and instability, and fluctuations in meteorological conditions have a significant impact on wind power generation efficiency and output. With the large-scale access of wind power to the power grid, extreme wind power events, especially long-term low wind power events (ie "wind shortage events"), have brought new challenges to the power supply adequacy and power balance of the power system.

[0003] After the power system is connected to a large number of photovoltaic and wind power sources distributed over a wide area, the primary energy characteristics for achieving power balance have changed; the traditional power system analyzes power balance through typical day combinations, and meteorological factors mainly affect the load side; in systems with a high proportion of new energy, various complex meteorological factors cause large fluctuations and uncertainties in the output of wind and solar power sources, and the power balance analysis needs to focus on evaluating the variability of its seasonal and inter-annual resources.

[0004] Current research focuses on the temporal and spatial distribution characteristics of wind energy resources, but lacks quantitative analysis and in-depth research on wind shortage events. This is because there is a lack of sufficiently large-scale and high-penetration wind and photovoltaic power output data at this stage, and it is difficult to use historical data to build grid operation scenario data under various meteorological conditions, which makes it difficult to form an in-depth understanding of wind shortage events. This has become an important reason for large-scale power rationing caused by wind shortage events in recent years. In the future, as the proportion of wind power installed capacity continues to increase, the power balance problem caused by wind shortage events will become more serious, which will bring greater difficulties to the dispatching management and regulation of the power system, and the power supply guarantee situation will become more severe. Summary of the invention

[0005] In order to address the deficiencies in the prior art, the present invention provides a method and system for analyzing the distribution characteristics of wind shortage events based on meteorological data inversion. The method inverts historical output data from historical meteorological data, quantifies the seasonal, annual and extreme value distribution characteristics of wind shortage events, and obtains the wind power output fluctuation characteristics, thereby providing a new perspective for addressing the challenges of power supply brought about by wind power output fluctuations, and providing a decision-making basis for formulating relevant power dispatching strategies and optimizing the energy structure.

[0006] The present invention adopts the following technical solution.

[0007] In a first aspect, the present invention provides a method for analyzing the distribution characteristics of wind shortage events based on meteorological data inversion, the method comprising:

[0008] Obtain historical wind speed data for each cell within the study area;

[0009] Preprocess the acquired historical wind speed data to obtain the unit cell wind speed data at the hub height of the wind turbine;

[0010] Converting the cell wind speed data into power data using a wind speed-power model;

[0011] Based on the power data, each cell is weighted and aggregated in combination with the wind energy regional classification standard to obtain the wind power capacity coefficient sequence of the study area;

[0012] Using multiple probability distribution models to fit the wind power capacity coefficient sequence respectively, selecting the probability distribution with the best fitting effect, and determining the identification thresholds of wind shortage events of different degrees by percentile method;

[0013] Identify wind shortage events of different degrees in the study area by combining the identification thresholds, and calculate the duration and intensity of each wind shortage event;

[0014] The duration and intensity of each wind shortage event are quantitatively analyzed to obtain the seasonal, annual and extreme wind power output distribution characteristics of each wind shortage event.

[0015] Optionally, the step of obtaining historical wind speed data of each cell within the study area includes:

[0016] The wind speed data of the east-west component u and the north-south component v at the base height of each cell in the study area were obtained; the spatial resolution of the historical wind speed data of each cell was 0.25° latitude * 0.25° longitude, and the temporal resolution was 1 hour.

[0017] Optionally, the step of preprocessing the acquired historical wind speed data includes:

[0018] The wind speed data of the east-west component u and the north-south component v of each cell are respectively synthesized into the synthetic wind speed V(100) of the corresponding cell base height;

[0019] The power exponential model is used to convert the synthetic wind speed V(100) of each cell into the cell wind speed data of its corresponding wind turbine hub height h.

[0020] Optionally, the expression of the power exponential model is as follows:

[0021]

[0022] Where V(h) is the wind speed at the reference height h of the wind turbine hub above the ground, and 1 / n is the wind speed profile index.

[0023] Optionally, the expression of the wind speed-power model is as follows:

[0024]

[0025] Where x is the wind speed at the fan head and y is the fan output power.

[0026] Optionally, the step of weighted aggregation of each cell in combination with the wind energy area classification standard to obtain the wind power capacity coefficient sequence of the study area includes:

[0027] Calculate the annual cumulative hours of wind speed data in each cell within the wind speed range of 3 to 20 m / s;

[0028] Based on the annual cumulative hours of each cell, each cell is divided into its corresponding regional level according to the wind energy regional level classification standard;

[0029] Different weights are set for different regional levels, and weighted aggregation is performed based on the power data of each unit to calculate the wind power capacity coefficient sequence of the study area.

[0030] Optionally, the calculation formula of the wind power capacity coefficient sequence is as follows:

[0031]

[0032] In the formula, cf t is the wind power capacity factor of the study area at hour t; p i,t is the total power of the cells within the i-th region level at the t-th hour; c i is the total installed capacity of cells within the i-th regional level; w i is the weight set for the i-th regional level; T max Represents the total number of hours of the study time scale.

[0033] Optionally, the regional levels are divided into four types, namely, wind energy-rich area, relatively wind energy-rich area, wind energy-utilizable area and wind energy-poor area; the weights set for the wind energy-rich area, relatively wind energy-rich area, wind energy-utilizable area and wind energy-poor area are 0.6, 0.3, 0.1 and 0 respectively.

[0034] Optionally, the step of using multiple probability distribution models to fit the wind power capacity coefficient sequence respectively and selecting the probability distribution with the best fitting effect includes:

[0035] The wind power capacity coefficient series is fitted using the GEV generalized extreme value distribution, Weibull distribution and Gumbel distribution probability distribution models, and the fitting results of each probability distribution model are obtained respectively;

[0036] Based on the above fitting results, the fitting result corresponding to the probability distribution model that passes the KS test and has the smallest AIC Akaike information criterion value is taken as the optimal probability distribution.

[0037] Optionally, the step of determining the identification thresholds of wind shortage events of different degrees by percentile method includes:

[0038] The wind power capacity factors corresponding to the 2%, 5%, and 10% quantiles are obtained from the selected optimal probability distribution;

[0039] The wind power capacity factors corresponding to the 2%, 5% and 10% quantiles are used as identification thresholds for severe wind shortage events, moderate wind shortage events and mild wind shortage events respectively.

[0040] Optionally, the steps of identifying wind shortage events of different degrees in the study area in combination with the identification thresholds, and calculating the duration and intensity of each wind shortage event include:

[0041] Based on the wind power capacity factor sequence obtained by weighted aggregation, calculate the sliding average capacity factor of no less than 5 hours;

[0042] Comparing each of the sliding average capacity factors with each identification threshold, respectively, and taking the number of hours calculated for the sliding average capacity factor that is smaller than each identification threshold as the duration of the corresponding degree of wind shortage event;

[0043] The coordinate area within the hours range enclosed by the wind power capacity factor and the corresponding identification threshold is taken as the intensity of the corresponding degree of wind shortage event.

[0044] Optionally, the step of quantitatively analyzing the duration and intensity of each wind shortage event includes:

[0045] The duration and intensity of wind shortage events of different degrees in each season are counted respectively, and the seasonal distribution characteristic diagram of the duration and intensity of wind shortage events of different degrees is generated;

[0046] The total duration and intensity of wind shortage events of different degrees in each year within the research time scale are counted respectively, and the interannual distribution characteristic diagram of the duration and intensity of wind shortage events of different degrees is generated;

[0047] The seasonal maximum sequence SMS model is used to extract the extreme value data of each degree of wind shortage event, and the seasonal extreme value sequence of each degree of wind shortage event in each season is obtained. The probability distribution of each seasonal extreme value sequence is fitted, and the seasonal recurrence level corresponding to each degree of wind shortage event is obtained by combining the extreme value theory evaluation;

[0048] The annual maximum value sequence AMS model is used to extract the extreme value data of each degree of wind shortage event, and the annual extreme value sequence of each degree of wind shortage event in each year is obtained. The probability distribution of each annual extreme value sequence is fitted, and the annual recurrence period level corresponding to each degree of wind shortage event is evaluated in combination with the extreme value theory.

[0049] In a second aspect, the present invention provides a system for analyzing the distribution characteristics of wind shortage events based on meteorological data inversion, and executes the steps of any method described in the first aspect of the present invention, the system comprising:

[0050] The data acquisition module is used to obtain the historical wind speed data of each cell within the study area;

[0051] A preprocessing module is used to preprocess the acquired historical wind speed data to obtain the unit cell wind speed data at the hub height of the wind turbine;

[0052] A power conversion module, used for converting the cell wind speed data into power data using a wind speed-power model;

[0053] A weighted aggregation module, used to perform weighted aggregation on each cell based on the power data and in combination with the wind energy area classification standard to obtain a wind power capacity coefficient sequence of the study area;

[0054] A coefficient sequence fitting module is used to fit the wind power capacity coefficient sequence using multiple probability distribution models, select the probability distribution with the best fitting effect, and determine the identification thresholds of wind shortage events of different degrees by percentile method;

[0055] A wind shortage event identification module, used to identify wind shortage events of different degrees in the study area in combination with each of the identification thresholds, and calculate the duration and intensity of each wind shortage event;

[0056] The quantitative analysis module is used to quantitatively analyze the duration and intensity of each wind shortage event to obtain the seasonal, annual and extreme wind power output distribution characteristics of each wind shortage event.

[0057] In a third aspect, the present invention provides a terminal, including a processor and a storage medium;

[0058] The storage medium is used to store instructions;

[0059] The processor is used to operate according to the instructions to execute the steps of any one of the methods described in the first aspect of the present invention.

[0060] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any one of the methods described in the first aspect of the present invention.

[0061] The beneficial effects of the present invention are as follows:

[0062] 1. The present invention inverts historical wind power output data based on historical meteorological data to identify historical wind shortage events, which overcomes the current problem of lack of wind power operation output data with a sufficiently large range and high penetration rate, and quantitatively analyzes its seasonal, interannual, extreme value and other distribution characteristics from multiple dimensions such as wind shortage intensity and duration, which helps to more comprehensively understand the regularity and change characteristics of wind shortage events, analyze the fluctuation characteristics of wind power output, and thus provide a new perspective and method for the challenge of power supply security, better cope with the challenges brought by wind power output fluctuations, and provide a decision-making basis for formulating relevant power dispatching strategies and optimizing energy structure.

[0063] 2. Most of the existing technologies simply arrange the wind power capacity coefficient from small to large, and classify it by percentage (such as the first 0-10%, 10%-20%), which is too dependent on the distribution of data. The present invention classifies each unit cell in the region according to my country's wind energy regional classification standards and selects corresponding weights, and weighted aggregates each unit grid to obtain a regional aggregate capacity coefficient sequence, which more accurately reflects the actual distribution of wind energy resources in each region compared to the existing technology. In addition, the present invention selects the probability distribution with the best fitting effect from multiple probability distribution models, which can more accurately divide the recognition thresholds of wind shortage events of various degrees, and further improves the accuracy of the distribution feature analysis of each wind shortage event.

[0064] 3. The present invention combines the ERA5 database with the wind speed-power model to calculate the power data of each cell to simulate the wind power output of each cell. According to my country's wind energy regional classification standard, each cell is weighted and aggregated to obtain a regional aggregated capacity coefficient sequence. Then, the MBT algorithm and probability distribution model are used to select the capacity factors of 2%, 5%, and 10% quantiles as the identification thresholds for severe, moderate, and mild wind shortage events, and the intensity and duration of wind shortage are quantitatively analyzed, which provides a scientific basis for identifying and evaluating wind shortage events of different degrees. Finally, the interannual and extreme value distribution characteristics are analyzed, and a "once in many years" recurrence period level indicator for wind shortage events is proposed, which can be used to predict and warn the frequency and severity of wind shortage events, and provide an important reference for the planning and scheduling of power systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1It is a flow chart of the method for analyzing the distribution characteristics of wind shortage events based on meteorological data inversion in the present invention;

[0066] Figure 2 This is a schematic diagram of MBT detection for identifying mild wind shortage events in the study area of ​​the present invention;

[0067] Figure 3 The seasonal distribution diagram of duration and intensity of mild wind shortage events in the study area of ​​the present invention;

[0068] Figure 4 This is a box plot of the interannual distribution of the duration and intensity of mild wind shortage events in the study area of ​​the present invention;

[0069] Figure 5 The duration and intensity recurrence period curves of mild wind shortage events in different seasons in the study area of ​​the present invention;

[0070] Figure 6 It is a structural principle block diagram of the wind shortage event distribution characteristic analysis system based on meteorological data inversion in the present invention. DETAILED DESCRIPTION

[0071] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in the present invention are only part of the embodiments of the present invention, not all of the embodiments. Based on the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without creative work are all within the scope of protection of the present invention.

[0072] Embodiment 1:

[0073] Reference Figure 1 The embodiment of the present invention provides a method for analyzing the distribution characteristics of wind shortage events based on meteorological data inversion, which specifically includes the following steps:

[0074] Step S1: Obtain historical wind speed data for each cell within the study area;

[0075] In this embodiment, the wind speed data of the east-west component u and the north-south component v of each cell in the study area at the reference height (i.e., 100m) are obtained from the European Center for Medium-Range Weather Forecasts ERA5 (ECMWF Reanalysis v5) database; wherein the spatial resolution of the historical wind speed data of each cell is 0.25° latitude * 0.25° (approximately 25km×25km) longitude, and the temporal resolution is 1 hour.

[0076] Step S2: pre-processing the acquired historical wind speed data to obtain the unit cell wind speed data at the hub height of the wind turbine;

[0077] Furthermore, in addition to performing missing value filling and outlier replacement processing on the acquired historical wind speed data, the preprocessing step of the embodiment of the present invention also includes:

[0078] S2.1. The wind speed data of the east-west component u and the north-south component v of each cell are respectively synthesized into the synthetic wind speed V(100) at the base height of the corresponding cell by the following formula:

[0079]

[0080] S2.2. Use the power exponential model to convert the synthetic wind speed V(100) of each cell into the cell wind speed data of the corresponding wind turbine hub height h; the expression of the power exponential model is as follows:

[0081]

[0082] Where V(h) is the wind speed at the reference height h of the wind turbine hub from the ground; 1 / n is the wind speed profile index, also known as the wind shear index, which is an indicator of how fast the wind speed changes with height. The wind shear index for different ground conditions is shown in Table 1:

[0083] Table 1 Wind shear index for different ground conditions

[0084]

[0085] Step S3: converting the cell wind speed data into power data using a wind speed-power model;

[0086] In a preferred but non-limiting embodiment, an Enercon E-101 wind turbine model with a rated capacity of 3.05 MW, a cut-in wind speed of 3 m / s, a cut-out wind speed of 25 m / s, and a hub height of 124 m is used to simulate the wind power output of each unit grid. The expression of the wind speed-power model is as follows:

[0087]

[0088] Where x is the wind speed at the fan head and y is the fan output power.

[0089] Step S4: Based on the power data, each cell is weighted and aggregated in combination with the wind energy area classification standard to obtain a wind power capacity coefficient sequence of the study area;

[0090] Furthermore, according to my country's wind energy regional classification standard, the unit cell wind resources are evaluated; as shown in Table 2, the wind energy regional classification standard and the weight of each regional level used in this embodiment are as follows:

[0091] Table 2 Wind energy regional classification standards

[0092]

[0093] Specifically, step S4 includes the following process:

[0094] S4.1. Calculate the annual cumulative hours of wind speed data in each cell within the wind speed range of 3 to 20 m / s.

[0095] S4.2. Based on the annual cumulative hours of each cell, classify each cell into its corresponding regional class according to the wind energy regional class classification standard;

[0096] S4.3. Set different weights for different regional levels, and perform weighted aggregation based on the power data of each unit to calculate the wind power capacity coefficient sequence of the study area. The calculation formula of the wind power capacity coefficient sequence is as follows:

[0097]

[0098] In the formula, cf t is the wind power capacity factor of the study area at hour t; p i,t is the total power of the cells within the i-th region level at the t-th hour; c i is the total installed capacity of cells within the i-th regional level; w i is the weight set for the i-th regional level; T max Represents the total number of hours of the study time scale.

[0099] Step S5: using multiple probability distribution models to fit the wind power capacity coefficient sequence respectively, selecting the probability distribution with the best fitting effect, and determining the identification thresholds of wind shortage events of different degrees by percentile method;

[0100] As an embodiment of the present invention, step S5 specifically includes:

[0101] S5.1. Use the GEV generalized extreme value distribution, Weibull distribution and Gumbel distribution probability distribution models to fit the wind power capacity coefficient series, and obtain the fitting results of each probability distribution model;

[0102] S5.2. Based on the above fitting results, the fitting result corresponding to the probability distribution model that passes the KS test and has the smallest AIC Akaike Information Criterion value is taken as the optimal probability distribution;

[0103] S5.3. Obtain the wind power capacity factors corresponding to the 2%, 5%, and 10% quantiles from the selected optimal probability distribution;

[0104] S5.4. The wind power capacity factors corresponding to the 2%, 5% and 10% quantiles are used as identification thresholds for severe wind shortage events, moderate wind shortage events and mild wind shortage events respectively.

[0105] It should be noted that the KS test is a non-parametric statistical test method used to test whether sample data comes from a specific distribution; its principle is to compare the difference between the sample cumulative distribution function (ECDF) and the theoretical cumulative distribution function (CDF); if the p-value is less than the significance level (the study set it to 0.05), the null hypothesis is rejected, that is, the sample distribution is significantly different from the assumed theoretical distribution. In addition, AIC is an information criterion used to compare the fitting effects of different models on a given data set; the smaller the AIC value, the better the model is in fitting the data.

[0106] The errors calculated under different fitting models in this embodiment are shown in Table 3:

[0107] Table 3 Errors under different fitting models

[0108]

[0109] Based on the statistical significance of the KS test and the minimization of the AIC value, it can be known that the Weibull distribution passes the KS test and has the smallest AIC value. Therefore, this embodiment uses the Weibull distribution to best represent the theoretical probability distribution model of the data set and fits the data.

[0110] Step S6: identifying wind shortage events of different degrees in the study area in combination with the identification thresholds, and calculating the duration and intensity of each wind shortage event;

[0111] It is further explained that this embodiment identifies wind shortage events of different degrees by using the MBT algorithm; the definition of the MBT (Mean Below Threshold) algorithm in wind shortage event identification is: when the sliding average capacity factor of 5 hours or more in the area is lower than a given threshold, it is defined as a wind shortage event; in this embodiment, step S6 specifically includes:

[0112] S6.1. Based on the wind power capacity factor sequence obtained by weighted aggregation, calculate the sliding average capacity factor of not less than 5 hours;

[0113] S6.2. Compare each of the sliding average capacity factors with each identification threshold, and use the number of hours calculated for the sliding average capacity factor that is smaller than each identification threshold as the duration of the corresponding degree of wind shortage event;

[0114] S6.3. The coordinate area within the range of hours enclosed by the wind power capacity coefficient and the corresponding identification threshold is taken as the intensity of the corresponding degree of wind shortage event.

[0115] Reference Figure 2 , showing the wind power capacity factor threshold θ CF = 0.1 to detect wind shortage events, Figure 2 T1 and T2 represent the duration of each mild wind shortage event, and the wind power capacity factor and threshold θ CF =0.1 represents the intensity of a mild wind shortage event.

[0116] Step S7: Quantitatively analyze the duration and intensity of each wind shortage event to obtain seasonal, annual and extreme wind power output distribution characteristics of each wind shortage event.

[0117] As an embodiment of the present invention, step S7 specifically includes:

[0118] S7.1. Count the duration and intensity of wind shortage events of different degrees in each season, and generate seasonal distribution characteristic diagrams of the duration and intensity of wind shortage events of different degrees;

[0119] S7.2. Count the total duration and intensity of wind shortage events of different degrees in each year within the research time scale, and generate the interannual distribution characteristic diagram of the duration and intensity of wind shortage events of different degrees;

[0120] S7.3. The extreme value data of each degree of wind shortage event are extracted by using the seasonal maximum series SMS model to obtain the seasonal extreme value series of each degree of wind shortage event in each season, and the probability distribution of each seasonal extreme value series is fitted respectively, and the seasonal recurrence level corresponding to each degree of wind shortage event is obtained by combining the extreme value theory evaluation;

[0121] S7.4. Utilize the Annual Maximum Series (AMS) model to extract the extreme value data of each degree of wind shortage event, obtain the annual extreme value sequence of each degree of wind shortage event in each year, perform probability distribution fitting on each of the annual extreme value sequences, and use the extreme value theory to evaluate and obtain the annual recurrence period level corresponding to each degree of wind shortage event.

[0122] The evaluation principles for the seasonal return period levels and annual return period levels in steps S7.3 and S7.4 are the same. The following only gives an example of the evaluation of the annual return period level, and the evaluation of the seasonal return period level will not be repeated.

[0123] In this embodiment, a probability distribution model is used to fit the duration and intensity data of the annual scale wind shortage event with probability distribution, and the extreme value theory is used to estimate the recurrence level of the sample data. Statistically speaking, it is to calculate the small probability events on both sides of the extreme value distribution adopted; based on the probability theory and statistical theory, assuming that X is a continuous random variable, X represents the duration or intensity extreme value sequence of the seasonal or annual scale wind shortage event, and x represents an extreme value in the annual extreme value sequence, then the probability F(x) obtained based on the probability distribution fitting that the extreme values ​​of the remaining years in the annual extreme value sequence are less than x is expressed as:

[0124]

[0125] Rewrite F(x) in the above formula into the probability on the right side as follows:

[0126]

[0127] Assume that: Then we have:

[0128] Among them, T is the recurrence period of the selected extreme value. According to the recurrence period to be evaluated, it is substituted into the above formula to reversely calculate the specific value of the extreme value x. When T=50 is set, it means that in any given year, the probability of a wind shortage event at least equal to the extreme value x is 2%. Such events are called "once in 50 years", that is, in long-term observations, this extreme event recurs on average once every 50 years.

[0129] In a specific example, the seasonal distribution and interannual distribution of the duration and intensity of mild wind shortage events in a province are respectively as follows: Figure 3 , Figure 4 As shown in Figure 2, the return period curves of duration and intensity of mild wind shortage events in different seasons are as follows: Figure 5 The duration and recurrence period of mild wind shortage events on seasonal and annual scales are shown in Table 4.

[0130] from Figure 3It can be seen that the distribution of wind shortage events varies significantly between seasons. Due to the rise in temperature in spring and frequent cyclone activities, the wind speed is generally high. Therefore, the intensity and duration of wind shortage events in spring are significantly lower than those in other seasons. On the contrary, the duration and intensity of wind shortage events in autumn exceed those in other seasons, and the extreme wind shortage in the year is also concentrated in autumn. From the perspective of power and electricity balance of the power system, summer and winter are peak load periods, while wind power generation in the region reaches its peak in spring. This seasonal mismatch indicates that when the load demand is large in summer and winter, wind power generation is relatively insufficient, which brings challenges to seasonal power balance. Current technology mainly focuses on intraday regulation demand, but as the proportion of wind power generation in the power system gradually increases, the problem of seasonal power balance will become more prominent. Therefore, it is necessary to fully consider the impact of the volatility of wind power output on the stable supply of electricity, and configure sufficient conventional power sources or flexible adjustment resources to ensure the reliability of power supply during peak summer and peak winter. It can be seen that the present invention has identified the distribution patterns of wind shortage events between seasons such as spring and autumn. This in-depth seasonal assessment helps to more accurately predict the wind power output level in each season, thereby better planning the allocation of power resources.

[0131] Figure 4 The upper middle part shows the duration distribution of mild wind shortage events, and the lower middle part shows the intensity distribution of mild wind shortage events. Figure 4 It can be seen that in the past few decades, the duration and intensity of wind shortage events have been relatively stable, with little fluctuation, but since around 2010, there has been a clear growth trend, especially reaching a significant peak in 2020. Although the intensity and duration of wind shortages have rebounded slightly in some years, the overall trend is increasing, which indicates that the duration and intensity of wind shortage events have increased in recent years. In future power system scheduling and planning, more attention needs to be paid to the impact of wind shortage events. The interannual characteristic changes in the intensity of wind shortage events provided by the present invention can guide power dispatching and planning departments to conduct long-term risk assessments, and predict the frequency and intensity of possible wind shortage events in the future based on historical data, and formulate response plans to respond quickly when wind shortage events occur to reduce the impact on power supply.

[0132] from Figure 5Table 4 shows the seasonal and interannual multi-year return period levels of mild wind shortage events obtained by the method of the present invention. It can be seen that the duration and intensity of wind shortage events vary significantly in different seasons. The duration and intensity of wind shortage events in autumn are higher than those in other seasons, indicating that autumn is the most serious period of wind shortage events; the duration of wind shortage events in summer is longer, but the intensity is relatively low; the duration and intensity of wind shortage events in spring and winter are relatively low, but the intensity of wind shortage events in winter is slightly higher than that in spring. In addition, it can be seen from the data in Table 4 that the duration and intensity of wind shortage events increase with the increase of the return period. This means that extreme wind shortage events (i.e., "once in a multi-year" event) may last longer and have greater intensity. For the return periods of 50 and 100 years, the duration and intensity of wind shortage events increase significantly, which is particularly evident in autumn, indicating that longer and stronger wind shortage events may occur in autumn. Therefore, power system scheduling and planning need to pay special attention to wind shortage events in autumn and summer, because these seasonal wind shortage events may last longer or reach higher intensity, thereby affecting the stability of power supply. For extreme events with a long recurrence period, the power system should take countermeasures in advance, such as reserving more regulation resources or increasing flexibility, to cope with possible long-term high-intensity wind shortage events. It can be seen that the present invention can provide a more accurate seasonal and interannual risk assessment tool for the dispatch and planning of the power system, greatly improving the power system's ability to cope with meteorological fluctuations, especially extreme wind shortage events, and ensuring the continuity and reliability of power supply.

[0133] Table 4 Duration and intensity return period of seasonal and annual mild wind shortage events

[0134]

[0135]

[0136] The beneficial effects of the present invention are as follows:

[0137] 1. The present invention inverts historical wind power output data based on historical meteorological data to identify historical wind shortage events, which overcomes the current problem of lack of wind power operation output data with a sufficiently large range and high penetration rate, and quantitatively analyzes its seasonal, interannual, extreme value and other distribution characteristics from multiple dimensions such as wind shortage intensity and duration, which helps to more comprehensively understand the regularity and change characteristics of wind shortage events, analyze the fluctuation characteristics of wind power output, and thus provide a new perspective and method for the challenge of power supply security, better cope with the challenges brought by wind power output fluctuations, and provide a decision-making basis for formulating relevant power dispatching strategies and optimizing energy structure.

[0138] 2. Most of the existing technologies simply arrange the wind power capacity coefficient from small to large, and classify it by percentage (such as the first 0-10%, 10%-20%), which is too dependent on the distribution of data. The present invention classifies each unit cell in the region according to my country's wind energy regional classification standards and selects corresponding weights, and weighted aggregates each unit grid to obtain a regional aggregate capacity coefficient sequence, which more accurately reflects the actual distribution of wind energy resources in each region compared to the existing technology. In addition, the present invention selects the probability distribution with the best fitting effect from multiple probability distribution models, which can more accurately divide the recognition thresholds of wind shortage events of various degrees, and further improves the accuracy of the distribution feature analysis of each wind shortage event.

[0139] 3. The present invention combines the ERA5 database with the wind speed-power model to calculate the power data of each cell to simulate the wind power output of each cell. According to my country's wind energy regional classification standard, each cell is weighted and aggregated to obtain a regional aggregated capacity coefficient sequence. Then, the MBT algorithm and probability distribution model are used to select the capacity factors of 2%, 5%, and 10% quantiles as the identification thresholds for severe, moderate, and mild wind shortage events, and the intensity and duration of wind shortage are quantitatively analyzed, which provides a scientific basis for identifying and evaluating wind shortage events of different degrees. Finally, the interannual and extreme value distribution characteristics are analyzed, and a "once in many years" recurrence period level indicator for wind shortage events is proposed, which can be used to predict and warn the frequency and severity of wind shortage events, and provide an important reference for the planning and scheduling of power systems.

[0140] Embodiment 2:

[0141] like Figure 6 As shown, the present invention provides a system for analyzing the distribution characteristics of wind shortage events based on meteorological data inversion, the system is used to implement the steps of the method in the above embodiment 1, and the system specifically includes:

[0142] The data acquisition module is used to obtain the historical wind speed data of each cell within the study area;

[0143] A preprocessing module is used to preprocess the acquired historical wind speed data to obtain the unit cell wind speed data at the hub height of the wind turbine;

[0144] A power conversion module, used for converting the cell wind speed data into power data using a wind speed-power model;

[0145] A weighted aggregation module, used to perform weighted aggregation on each cell based on the power data and in combination with the wind energy area classification standard to obtain a wind power capacity coefficient sequence of the study area;

[0146] A coefficient sequence fitting module is used to fit the wind power capacity coefficient sequence using multiple probability distribution models, select the probability distribution with the best fitting effect, and determine the identification thresholds of wind shortage events of different degrees by percentile method;

[0147] A wind shortage event identification module, used to identify wind shortage events of different degrees in the study area in combination with each of the identification thresholds, and calculate the duration and intensity of each wind shortage event;

[0148] The quantitative analysis module is used to quantitatively analyze the duration and intensity of each wind shortage event to obtain the seasonal, annual and extreme wind power output distribution characteristics of each wind shortage event.

[0149] The system for analyzing the distribution characteristics of wind shortage events based on meteorological data inversion provided in the embodiment of the present invention and the method for analyzing the distribution characteristics of wind shortage events based on meteorological data inversion provided in Example 1 are based on the same technical concept, and can produce the beneficial effects as described in Example 1. For the contents not described in detail in this embodiment, please refer to Example 1.

[0150] Embodiment three:

[0151] A terminal provided by an embodiment of the present invention includes a processor and a storage medium;

[0152] The storage medium is used to store instructions;

[0153] The processor is used to operate according to the instruction to execute the steps of the method according to any one of the first embodiments.

[0154] Embodiment 4:

[0155] An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the steps of any method described in Embodiment 1 are implemented.

[0156] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0157] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0158] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0159] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0160] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for analyzing the distribution characteristics of wind shortage events based on meteorological data inversion, characterized in that: Methods include: Obtain historical wind speed data for each cell within the study area; Preprocess the acquired historical wind speed data to obtain the unit cell wind speed data at the hub height of the wind turbine; Converting the cell wind speed data into power data using a wind speed-power model; Based on the power data, each cell is weighted and aggregated in combination with the wind energy regional classification standard to obtain the wind power capacity coefficient sequence of the study area; Using multiple probability distribution models to fit the wind power capacity coefficient sequence respectively, selecting the probability distribution with the best fitting effect, and determining the identification thresholds of wind shortage events of different degrees by percentile method; Identify wind shortage events of different degrees in the study area by combining the identification thresholds, and calculate the duration and intensity of each wind shortage event; The duration and intensity of each wind shortage event are quantitatively analyzed to obtain the seasonal, annual and extreme wind power output distribution characteristics of each wind shortage event.

2. The method for analyzing the distribution characteristics of wind shortage events based on meteorological data inversion according to claim 1 is characterized in that: The step of obtaining the historical wind speed data of each cell within the study area includes: The wind speed data of the east-west component u and the north-south component v at the base height of each cell in the study area were obtained; the spatial resolution of the historical wind speed data of each cell was 0.25° latitude * 0.25° longitude, and the temporal resolution was 1 hour.

3. The method for analyzing the distribution characteristics of wind shortage events based on meteorological data inversion according to claim 2 is characterized in that: The step of preprocessing the acquired historical wind speed data comprises: The wind speed data of the east-west component u and the north-south component v of each cell are respectively synthesized into the synthetic wind speed V(100) of the corresponding cell base height; The power exponential model is used to convert the synthetic wind speed V(100) of each cell into the cell wind speed data of its corresponding wind turbine hub height h.

4. The method for analyzing the distribution characteristics of wind shortage events based on meteorological data inversion according to claim 3 is characterized in that: The expression of the power exponential model is as follows: Where V(h) is the wind speed at the reference height h of the wind turbine hub above the ground, and 1 / n is the wind speed profile index.

5. The method for analyzing the distribution characteristics of wind shortage events based on meteorological data inversion according to claim 1 is characterized in that: The expression of the wind speed-power model is as follows: Where x is the wind speed at the fan head and y is the fan output power.

6. The method for analyzing the distribution characteristics of wind shortage events based on meteorological data inversion according to claim 1 is characterized in that: The step of weighting and aggregating each cell in combination with the wind energy area classification standard to obtain the wind power capacity coefficient sequence of the study area includes: Calculate the annual cumulative hours of wind speed data in each cell within the wind speed range of 3 to 20 m / s; Based on the annual cumulative hours of each cell, each cell is divided into its corresponding regional level according to the wind energy regional level classification standard; Different weights are set for different regional levels, and weighted aggregation is performed based on the power data of each unit to calculate the wind power capacity coefficient sequence of the study area.

7. The method for analyzing the distribution characteristics of wind shortage events based on meteorological data inversion according to claim 6 is characterized in that: The calculation formula of the wind power capacity coefficient sequence is as follows: In the formula, cf t is the wind power capacity factor of the study area at hour t; p i,t is the total power of the cells within the i-th region level at the t-th hour; c i is the total installed capacity of cells within the i-th regional level; w i is the weight set for the i-th regional level; T max Represents the total number of hours of the study time scale.

8. The method for analyzing the distribution characteristics of wind shortage events based on meteorological data inversion according to claim 7 is characterized in that: The regional levels are divided into four types, namely, wind energy-rich area, relatively wind energy-rich area, wind energy-utilizable area and wind energy-poor area; the weights set for the wind energy-rich area, relatively wind energy-rich area, wind energy-utilizable area and wind energy-poor area are 0.6, 0.3, 0.1 and 0 respectively.

9. The method for analyzing the distribution characteristics of wind shortage events based on meteorological data inversion according to claim 1 or 8, characterized in that: The steps of fitting the wind power capacity coefficient sequence respectively using multiple probability distribution models and selecting the probability distribution with the best fitting effect include: The wind power capacity coefficient series is fitted using the GEV generalized extreme value distribution, Weibull distribution and Gumbel distribution probability distribution models, and the fitting results of each probability distribution model are obtained respectively; Based on the above fitting results, the fitting result corresponding to the probability distribution model that passes the KS test and has the smallest AIC Akaike information criterion value is taken as the optimal probability distribution.

10. The method for analyzing the distribution characteristics of wind shortage events based on meteorological data inversion according to claim 9, characterized in that: The step of determining the identification thresholds of wind shortage events of different degrees by percentile method comprises: The wind power capacity factors corresponding to the 2%, 5%, and 10% quantiles are obtained from the selected optimal probability distribution; The wind power capacity factors corresponding to the 2%, 5% and 10% quantiles are used as identification thresholds for severe wind shortage events, moderate wind shortage events and mild wind shortage events respectively.

11. The method for analyzing the distribution characteristics of wind shortage events based on meteorological data inversion according to claim 1, characterized in that: The steps of identifying wind shortage events of different degrees in the study area in combination with the identification thresholds and calculating the duration and intensity of each wind shortage event include: Based on the wind power capacity factor sequence obtained by weighted aggregation, calculate the sliding average capacity factor of no less than 5 hours; Comparing each of the sliding average capacity factors with each identification threshold, respectively, and taking the number of hours calculated for the sliding average capacity factor that is smaller than each identification threshold as the duration of the corresponding degree of wind shortage event; The coordinate area within the hours range enclosed by the wind power capacity factor and the corresponding identification threshold is taken as the intensity of the corresponding degree of wind shortage event.

12. The method for analyzing the distribution characteristics of wind shortage events based on meteorological data inversion according to claim 1, characterized in that: The steps of quantitatively analyzing the duration and intensity of each of the wind shortage events include: The duration and intensity of wind shortage events of different degrees in each season are counted respectively, and the seasonal distribution characteristic diagram of the duration and intensity of wind shortage events of different degrees is generated; The total duration and intensity of wind shortage events of different degrees in each year within the research time scale are counted respectively, and the interannual distribution characteristic diagram of the duration and intensity of wind shortage events of different degrees is generated; The seasonal maximum sequence SMS model is used to extract the extreme value data of each degree of wind shortage event, and the seasonal extreme value sequence of each degree of wind shortage event in each season is obtained. The probability distribution of each seasonal extreme value sequence is fitted, and the seasonal recurrence level corresponding to each degree of wind shortage event is obtained by combining the extreme value theory evaluation; The annual maximum value sequence AMS model is used to extract the extreme value data of each degree of wind shortage event, and the annual extreme value sequence of each degree of wind shortage event in each year is obtained. The probability distribution of each annual extreme value sequence is fitted, and the annual recurrence period level corresponding to each degree of wind shortage event is evaluated in combination with the extreme value theory.

13. A system for analyzing the distribution characteristics of wind shortage events based on meteorological data inversion, running the method for analyzing the distribution characteristics of wind shortage events based on meteorological data inversion as claimed in any one of claims 1 to 12, characterized in that: System includes The data acquisition module is used to obtain the historical wind speed data of each cell within the study area; A preprocessing module is used to preprocess the acquired historical wind speed data to obtain the unit cell wind speed data at the hub height of the wind turbine; A power conversion module, used for converting the cell wind speed data into power data using a wind speed-power model; A weighted aggregation module, used to perform weighted aggregation on each cell based on the power data and in combination with the wind energy area classification standard to obtain a wind power capacity coefficient sequence of the study area; A coefficient sequence fitting module is used to fit the wind power capacity coefficient sequence using multiple probability distribution models, select the probability distribution with the best fitting effect, and determine the identification thresholds of wind shortage events of different degrees by percentile method; A wind shortage event identification module, used to identify wind shortage events of different degrees in the study area in combination with each of the identification thresholds, and calculate the duration and intensity of each wind shortage event; The quantitative analysis module is used to quantitatively analyze the duration and intensity of each wind shortage event to obtain the seasonal, annual and extreme wind power output distribution characteristics of each wind shortage event.

14. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1-12.

15. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 12 are implemented.

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