Maximum wind-solar power fluctuation interval estimation method and device based on wavelet decomposition, equipment and storage medium

Abstract

The invention provides a maximum wind-solar power fluctuation interval estimation method and device based on wavelet decomposition, equipment and a storage medium, and relates to the technical field of new energy power generation. The method comprises the following steps: acquiring historical wind power output power and historical photovoltaic output power of a power system; performing decomposition processing on the historical wind power output power and the historical photovoltaic output power based on a wavelet decomposition strategy, determining wind power output power fluctuation corresponding to the historical wind power output power, and determining photovoltaic output power fluctuation corresponding to the historical photovoltaic output power; constructing a power fluctuation interval estimation model based on wind power output power fluctuation and photovoltaic output power fluctuation; and based on the power fluctuation interval estimation model and the actual output data of the power system, determining a power fluctuation maximum estimation value. The embodiment provided by the invention is used for solving the defects of large error and low accuracy of maximum wind-solar power fluctuation interval estimation in the prior art, and the maximum wind-solar power fluctuation result can be simply and efficiently estimated.

Description

Technical Field

[0001] The present invention relates to the field of renewable energy power generation technology, and in particular to a method, device, equipment and storage medium for estimating a maximum wind / solar power fluctuation interval based on wavelet decomposition. Background Art

[0002] As the global energy structure transforms toward low-carbon and clean energy, the penetration rate of renewable energy represented by wind power and photovoltaics in the power system continues to rise. However, the natural volatility and randomness of wind and solar resources lead to significant spatiotemporal uncertainty in their output power, especially on a minute-level time scale. Violent power fluctuations may cause frequency over-limit, voltage instability and other problems, seriously threatening the safe operation of the power grid. In related technologies, quantitative analysis methods for wind and solar power fluctuations are mainly divided into two categories: statistical modeling and physical mechanism methods.

[0003] 1. Statistical modeling method uses historical data to construct probability distribution models (such as normal distribution and Weibull distribution) to describe the statistical characteristics of output fluctuations. However, this method has significant limitations: first, traditional single-scale statistical models are difficult to capture the dynamic behavior of short-term mutations at the minute level; second, the output characteristics of wind power and photovoltaic power are significantly different (wind power is affected by wind turbulence, and photovoltaic power is blocked by clouds). Existing methods mostly use independent analysis modes, and do not fully consider the "smoothing effect" of the superposition of the two fluctuations, resulting in a large deviation in the estimation of the joint fluctuations.

[0004] Second, the physical mechanism law is based on numerical meteorological forecasts and equipment physical models to simulate the law of output fluctuations. However, this method relies on high-precision meteorological data and complex parameter calibration, has low computational efficiency and is difficult to adapt to real-time scheduling needs, especially in extreme weather scenarios where the error is significant.

[0005] In summary, accurately estimating the maximum fluctuation range of wind and solar power is a key prerequisite for real-time dispatching, backup capacity configuration and stability control of the power system. Therefore, how to accurately estimate the maximum fluctuation range of wind and solar power is a technical problem that needs to be solved urgently. Summary of the invention

[0006] The present invention provides a method, device, equipment and storage medium for estimating the maximum wind and solar power fluctuation interval based on wavelet decomposition, which are used to solve the defects of large error and low accuracy in estimating the maximum wind and solar power fluctuation interval in the prior art, and to achieve simple and efficient estimation of the maximum wind and solar power fluctuation result.

[0007] In a first aspect, the present invention provides a method for estimating the maximum wind and solar power fluctuation range based on wavelet decomposition, comprising the following steps: Obtain the historical wind power output and historical photovoltaic power output of the power system; Based on the wavelet decomposition strategy, decompose the historical wind power output and the historical photovoltaic power output respectively, determine the wind power output fluctuation corresponding to the historical wind power output, and determine the photovoltaic power output fluctuation corresponding to the historical photovoltaic power output; Based on the wind power output fluctuation and the photovoltaic power output fluctuation, construct a power fluctuation interval estimation model; Based on the power fluctuation interval estimation model and the actual output data of the power system, determine the maximum estimated value of the power fluctuation.

[0008] Preferably, according to a method for estimating the maximum wind-solar power fluctuation interval based on wavelet decomposition provided by the present invention, the step of respectively decomposing the historical wind power output and the historical photovoltaic power output based on the wavelet decomposition strategy, determining the wind power output fluctuation corresponding to the historical wind power output, and determining the photovoltaic power output fluctuation corresponding to the historical photovoltaic power output includes: Based on the historical wind power output and the historical photovoltaic power output, determine the wavelet basis function for analyzing the new energy power signal, where the wavelet basis function is the function corresponding to the wavelet decomposition strategy; Determine the decomposition level for decomposing the historical wind power output and the historical photovoltaic power output, and based on the wavelet basis function, decompose the historical wind power output and the historical photovoltaic power output layer by layer according to the decomposition level to obtain the wind power decomposition result and the photovoltaic power decomposition result corresponding to each layer; Based on the wind power decomposition result and the photovoltaic power decomposition result, perform minute-level fluctuation component extraction processing respectively to obtain the corresponding wind power output fluctuation and the photovoltaic power output fluctuation.

[0009] Preferably, according to a method for estimating the maximum wind-solar power fluctuation interval based on wavelet decomposition provided by the present invention, the power fluctuation interval estimation model includes: a wind power interval estimation model and a photovoltaic power interval estimation model; The step of constructing a power fluctuation interval estimation model based on the wind power output fluctuation and the photovoltaic power output fluctuation includes: Based on the smoothing effect strategy, perform definition processing on the wind power output fluctuation and the photovoltaic power output fluctuation to determine the corresponding minute-level volatility of the wind power and the minute-level volatility of the photovoltaic power; Based on the minute-level volatility of the wind power and the per-unit value of the wind power output, construct a wind power scatter plot, and based on the minute-level volatility of the photovoltaic power and the per-unit value of the photovoltaic power output, construct a photovoltaic power scatter plot; The outer envelope of the wind power scatter plot is fitted with a double exponential function to determine a wind power interval estimation model, and the outer envelope of the photovoltaic scatter plot is fitted with a double exponential function to determine a photovoltaic interval estimation model.

[0010] Preferably, according to a method for estimating the maximum wind-solar power fluctuation interval based on wavelet decomposition provided by the present invention, the wind power interval estimation model characterizes the wind power mapping relationship between the minute-level volatility of the wind power and the historical wind power output; The photovoltaic interval estimation model characterizes the photovoltaic power mapping relationship between the minute-level volatility of the photovoltaic power and the historical photovoltaic power output.

[0011] Preferably, according to a method for estimating the maximum wind-solar power fluctuation interval based on wavelet decomposition provided by the present invention, the actual output data at least includes: the actual wind power output and the actual photovoltaic power output; The maximum estimated value of the power fluctuation at least includes: the maximum estimated value of the minute-level wind power fluctuation and the maximum estimated value of the minute-level photovoltaic power fluctuation; Determining the maximum estimated value of the power fluctuation based on the power fluctuation interval estimation model and the actual output data of the power system includes: Based on the wind power mapping function corresponding to the wind power mapping relationship and the actual wind power output of the power system, determining the maximum estimated value of the minute-level wind power fluctuation; Based on the photovoltaic power mapping function corresponding to the photovoltaic power mapping relationship and the actual photovoltaic power output of the power system, determining the maximum estimated value of the minute-level photovoltaic power fluctuation.

[0012] Preferably, according to a method for estimating the maximum wind-solar power fluctuation interval based on wavelet decomposition provided by the present invention, after the step of constructing a wind power scatter plot based on the minute-level volatility of the wind power and the per-unit value of the wind power output, the method includes: Calculating the wind power mean value and the wind power standard deviation of the minute-level volatility of the wind power; Calculating the wind power deviation between each data point in the wind power scatter plot and the wind power mean value, and comparing and processing the wind power deviation with 3 times the wind power standard deviation; Eliminating the data points whose wind power deviation is greater than or equal to 3 times the wind power standard deviation from the wind power scatter plot; After the step of constructing a photovoltaic scatter plot based on the minute-level volatility of the photovoltaic power and the per-unit value of the photovoltaic power output, the method includes: Calculating the photovoltaic power mean value and the photovoltaic power standard deviation of the minute-level volatility of the photovoltaic power; Calculate the photovoltaic deviation between each data point in the photovoltaic scatter plot and the photovoltaic mean value, and compare and process the photovoltaic deviation with 3 times the photovoltaic standard deviation; Exclude the data points with the photovoltaic deviation greater than or equal to 3 times the photovoltaic standard deviation from the photovoltaic scatter plot.

[0013] In a second aspect, the present invention also provides a device for estimating the maximum wind-solar power fluctuation interval based on wavelet decomposition, including: An acquisition module for acquiring the historical wind power output and the historical photovoltaic power output of the power system; An output power fluctuation determination module for respectively decomposing the historical wind power output and the historical photovoltaic power output based on a wavelet decomposition strategy to determine the wind power output fluctuation corresponding to the historical wind power output and the photovoltaic power output fluctuation corresponding to the historical photovoltaic power output; A construction module for constructing a power fluctuation interval estimation model based on the wind power output fluctuation and the photovoltaic power output fluctuation; A maximum estimation value determination module for determining the maximum estimated value of the power fluctuation based on the power fluctuation interval estimation model and the actual output data of the power system.

[0014] In a third aspect, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the program, it implements the method for estimating the maximum wind-solar power fluctuation interval based on wavelet decomposition as described in any one of the above.

[0015] In a fourth aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the method for estimating the maximum wind-solar power fluctuation interval based on wavelet decomposition as described in any one of the above.

[0016] In a fifth aspect, the present invention also provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the method for estimating the maximum wind-solar power fluctuation interval based on wavelet decomposition as described in any one of the above.

[0017] A method, device, equipment and storage medium for estimating the maximum wind-solar power fluctuation interval based on wavelet decomposition provided by the present invention obtain the historical wind power output and historical photovoltaic power output of a power system; respectively decompose the historical wind power output and the historical photovoltaic power output based on a wavelet decomposition strategy to determine the wind power output fluctuation corresponding to the historical wind power output and the photovoltaic power output fluctuation corresponding to the historical photovoltaic power output; construct a power fluctuation interval estimation model based on the wind power output fluctuation and the photovoltaic power output fluctuation; and determine the maximum estimated value of the power fluctuation based on the power fluctuation interval estimation model and the actual output data of the power system. It is used to solve the defect that the estimation error of the maximum wind-solar power fluctuation interval in the prior art is relatively large and the accuracy is low, and realizes a simple and efficient estimation of the maximum wind-solar power fluctuation result. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0019] Figure 1 FIG. 1 is one of the flow diagrams of a method for estimating the maximum wind-solar power fluctuation interval based on wavelet decomposition provided by the present invention.

[0020] Figure 2 FIG. 2 is another flow diagram of a method for estimating the maximum wind-solar power fluctuation interval based on wavelet decomposition provided by the present invention.

[0021] Figure 3 FIG. 3 is a structural diagram of a device for estimating the maximum wind-solar power fluctuation interval based on wavelet decomposition provided by the present invention.

[0022] Figure 4 FIG. 4 is a structural diagram of an electronic device provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0023] In order to make the objectives, technical solutions and advantages of the present invention clearer, the following will clearly and completely describe the technical solutions in the present invention with reference to the drawings in the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0024] The following will be combined with Figures 1 - 4A method, device, equipment and storage medium for estimating the maximum wind-solar power fluctuation interval based on wavelet decomposition of the present invention. By obtaining the historical wind power output and historical photovoltaic power output of the power system; based on the wavelet decomposition strategy, respectively decompose the historical wind power output and the historical photovoltaic power output, determine the wind power output fluctuation corresponding to the historical wind power output, and determine the photovoltaic power output fluctuation corresponding to the historical photovoltaic power output; based on the wind power output fluctuation and the photovoltaic power output fluctuation, construct a power fluctuation interval estimation model; based on the power fluctuation interval estimation model and the actual output data of the power system, determine the maximum estimated value of the power fluctuation. It is used to solve the defect that the estimation error of the maximum wind-solar power fluctuation interval in the prior art is relatively large and the accuracy is low, and realize the simple and efficient estimation of the maximum wind-solar power fluctuation result.

[0025] Figure 1 FIG. 4 is one of the flow diagrams of a method for estimating the maximum wind-solar power fluctuation interval based on wavelet decomposition provided by the present invention. As Figure 1 shown, the method may include but is not limited to steps S100 to S400: S100, obtain the historical wind power output and historical photovoltaic power output of the power system; S200, based on the wavelet decomposition strategy, respectively decompose the historical wind power output and the historical photovoltaic power output, determine the wind power output fluctuation corresponding to the historical wind power output, and determine the photovoltaic power output fluctuation corresponding to the historical photovoltaic power output; S300, based on the wind power output fluctuation and the photovoltaic power output fluctuation, construct a power fluctuation interval estimation model; S400, based on the power fluctuation interval estimation model and the actual output data of the power system, determine the maximum estimated value of the power fluctuation.

[0026] In step S100 of some embodiments, obtain the historical wind power output and historical photovoltaic power output of the power system.

[0027] It can be understood that the power monitoring system will collect and record the relevant operation data of each generating unit in the power system in real time, including the output power of wind farms and photovoltaic power stations. These data are collected from sensors on the generating equipment, such as the rotational speed sensor of the wind turbine, the power factor controller, and the output power monitoring device of the photovoltaic panel inverter, etc.

[0028] The collected data will be transmitted to the monitoring center and stored in the database for subsequent query and analysis.

[0029] In some embodiments of the present invention, first, the type and architecture of the power monitoring system are determined, and its data acquisition method and storage mechanism are understood. Common power monitoring systems include SCADA (Supervisory Control and Data Acquisition), etc.

[0030] Obtain the permission to access the power monitoring system database, and then query the wind power and photovoltaic output power data within a specific time period in the database according to the requirements. SQL (Structured Query Language) query statements can be written, or the data query tools provided by the monitoring system can be used.

[0031] In step S200 of some embodiments, the historical wind power output power and the historical photovoltaic output power are respectively decomposed based on the wavelet decomposition strategy to determine the wind power output power fluctuation corresponding to the historical wind power output power and the photovoltaic output power fluctuation corresponding to the historical photovoltaic output power.

[0032] First of all, it should be noted that wavelet decomposition is a signal processing technology that decomposes a signal into components with different time scales and frequency ranges. Compared with the traditional Fourier transform, wavelet decomposition can analyze the signal in both the time domain and the frequency domain simultaneously, and is particularly suitable for processing non-stationary signals. This characteristic makes wavelet decomposition have significant advantages in the decomposition of new energy power fluctuations. The method based on the double-exponential function fitting describes the relationship between the minute-level volatility of new energy power and the per-unit value of the actual output, and estimates the fluctuation range, which has the following significant advantages: it can accurately describe the non-linear relationship between the minute-level volatility of new energy power and the actual output, and each exponential term in the double-exponential function can also be given a clear physical meaning; it can adjust the fitting parameters to adapt to the data characteristics in different scenarios, and the interval estimation method based on the double-exponential function fitting is simple and efficient in calculation and suitable for large-scale real-time applications.

[0033] It can be understood that after the steps of step S100 are executed, the specific execution steps can be: Based on the historical wind power output power and the historical photovoltaic output power, determine the wavelet basis function for analyzing the new energy power signal, where the wavelet basis function is the function corresponding to the wavelet decomposition strategy; Determine the decomposition level for decomposing the historical wind power output power and the historical photovoltaic output power, and based on the wavelet basis function, decompose the historical wind power output power and the historical photovoltaic output power layer by layer according to the decomposition level to obtain the wind power decomposition result and the photovoltaic power decomposition result corresponding to each layer; Based on the wind power decomposition result and the photovoltaic power decomposition result, respectively perform minute-level fluctuation component extraction processing to obtain the corresponding wind power output power fluctuation and the photovoltaic output power fluctuation.

[0034] Further, first analyze the signals of historical wind power output and historical photovoltaic power output. Observe their characteristics such as periodicity, volatility, and abrupt changes. For example, wind power may have daily periodic fluctuations and random fluctuations affected by wind speed changes; photovoltaic power has obvious fluctuations during the day and is affected by factors such as weather and time.

[0035] Then select a suitable type of wavelet basis function: Based on the characteristics of the power signal, preliminarily screen from common types of wavelet basis functions. For example, for the part of the signal with smooth and strong periodicity, db4 or db5 in the Daubechies wavelet family can be selected; for the signal containing more abrupt points, Haar wavelet or Coiflet wavelet can be selected.

[0036] Conduct experiments and comparisons: Use different wavelet basis functions to perform trial decompositions on some sample data, and evaluate the adaptability of different wavelet basis functions to the new - energy power signal by calculating indicators such as the energy distribution and reconstruction error of each component after decomposition. Select the wavelet basis function that can accurately reflect the signal characteristics and make each component after decomposition have clear physical meanings as the final analysis wavelet basis function.

[0037] In some embodiments, input the historical wind power output and historical photovoltaic power output, and select a wavelet basis function suitable for analyzing the new - energy power signal; The formula of the wavelet basis function is: Among them, represents the wavelet basis function, which is the basic building block of wavelet decomposition, similar to the sine and cosine functions in Fourier transform, but with better localization characteristics.

[0038] represents the original input signal (historical wind power output and historical photovoltaic power output), represents the sub - wavelet function after scale stretching and translation operations.

[0039] Among them, a is the scale parameter, which controls the width of the wavelet (frequency resolution), and b is the translation parameter, which controls the position of the wavelet (time resolution).

[0040] The selection of the wavelet basis function directly affects the effect of subsequent wavelet decomposition. A suitable wavelet basis function can more accurately capture the characteristics of different frequency bands in the new - energy power signal, making each component after decomposition better reflect the actual fluctuations of wind power and photovoltaic power output, providing a good basis for subsequent analysis and processing.

[0041] In some embodiments, the method for determining the decomposition level: Multiple methods can be used to determine the decomposition level. One is based on an empirical formula to determine a rough range of the decomposition level according to the signal length and sampling frequency. For example, for a signal of length N, the decomposition level J can be initially estimated as J = log2(N). Then, within this range, the optimal level is determined by experimenting and comparing the decomposition effects at different levels. Another method is to determine the decomposition level according to the change in information entropy. As the decomposition level increases, calculate the information entropy of each component. When the change in information entropy tends to be flat, it can be considered that the appropriate decomposition level has been reached.

[0042] Decomposition based on the wavelet basis function: Using the selected wavelet basis function and the determined decomposition level, the historical wind power output and historical photovoltaic power output are decomposed layer by layer. The specific decomposition process is implemented through the wavelet transform algorithm. Taking the Mallat algorithm as an example, it decomposes the signal into a low-frequency approximation component (reflecting the general trend of the signal) and a high-frequency detail component (reflecting the local fluctuations of the signal). In each layer of decomposition, the low-frequency approximation component of the previous layer is further decomposed into the low-frequency approximation component and the high-frequency detail component of the next layer until the predetermined decomposition level is reached. In this way, the decomposition results of wind power (including low-frequency and high-frequency components) and photovoltaic power corresponding to each layer are obtained.

[0043] Determining a reasonable decomposition level can decompose the new energy power signal into components of different frequency scales, which respectively represent the fluctuation characteristics at different time scales. By decomposing the wind power and photovoltaic power output layer by layer, their fluctuation laws can be analyzed more comprehensively. For example, the low-frequency component can reflect the long-term trend and periodic changes of the power output, while the high-frequency component can reflect short-term fluctuations and mutations, providing conditions for accurately extracting the minute-level fluctuation component subsequently.

[0044] Furthermore, in order to extract the minute-level fluctuation component, usually a relatively shallow decomposition level (1 - 2 layers) is selected, and the historical wind power output and historical photovoltaic power output are decomposed layer by layer using the Discrete Wavelet Transform (DWT). The decomposition process is that the input historical wind power output and historical photovoltaic power output are divided into a low-frequency component (the approximation part ), and a high-frequency component (the detail part ). The result of each layer of decomposition can be expressed as: where, represents the original input signal (historical wind power output and historical photovoltaic power output), is the low-frequency component of the th layer, is the high-frequency component of the nth layer.

[0045] In some embodiments, the execution steps of extracting the minute-level fluctuation component based on the decomposition result may specifically be: Identify the frequency range where the minute-level fluctuation component is located: According to the correspondence between time and frequency and the characteristics of the new energy power signal, determine the frequency range corresponding to the minute-level fluctuation. For example, for minute-level fluctuations, their corresponding frequencies are usually relatively high and may be reflected in the high-frequency components within a certain range.

[0046] Extract the minute-level fluctuation component from the decomposition result: In the obtained wind power decomposition result and photovoltaic power decomposition result, find the components within the frequency range corresponding to the minute-level fluctuation. This may require further analysis and screening of each decomposed component. For example, by setting thresholds or frequency ranges to extract the relevant high-frequency detail components as the minute-level fluctuation components. Then reconstruct or combine these extracted components to obtain the complete wind power output power fluctuation and photovoltaic power output power fluctuation.

[0047] Furthermore, extract the minute-level fluctuation component from the first-layer decomposition result which reflects the minute-level power fluctuation of the new energy, that is, obtain the corresponding wind power output power fluctuation and the photovoltaic power output power fluctuation.

[0048] By extracting the minute-level fluctuation component, it is possible to focus on the rapid changes in the new energy output power within a short period of time. This is of great significance for studying the short-term power generation characteristics of wind power and photovoltaic power, evaluating their real-time impact on the power grid, and formulating corresponding power dispatching strategies. For example, the minute-level fluctuation component can help dispatchers more accurately predict the power changes in the short term, so as to timely adjust the output of other power sources or load arrangements in the power grid and ensure the stable operation of the power system.

[0049] In step S300 of some embodiments, based on the wind power output power fluctuation and the photovoltaic power output power fluctuation, construct a power fluctuation interval estimation model.

[0050] First of all, it should be noted that the power fluctuation interval estimation model includes: a wind power interval estimation model and a photovoltaic power interval estimation model.

[0051] The wind power interval estimation model represents the wind power mapping relationship between the minute-level volatility of the wind power and the historical wind power output power; The photovoltaic power interval estimation model represents the photovoltaic power mapping relationship between the minute-level volatility of the photovoltaic power and the historical photovoltaic power output power.

[0052] It can be understood that after the steps of step S200 are executed, the specific execution steps can be: based on the smoothing effect strategy, perform definition processing on the wind power output power fluctuation and the photovoltaic power output power fluctuation, and determine the corresponding minute-level wind power volatility and minute-level photovoltaic power volatility; Construct a wind power scatter plot based on the minute-level wind power volatility and the per-unit value of wind power output, and construct a photovoltaic scatter plot based on the minute-level photovoltaic power volatility and the per-unit value of photovoltaic power output; Use a double-exponential function to fit the outer envelope of the wind power scatter plot to determine a wind power interval estimation model, and use a double-exponential function to fit the outer envelope of the photovoltaic scatter plot to determine a photovoltaic interval estimation model.

[0053] It should be noted that processing the wind power output power fluctuation and the photovoltaic power output power fluctuation based on the smoothing effect strategy is mainly to eliminate random noise and abnormal fluctuations in the data, and highlight the main change trends and periodic laws of the data. This helps to more accurately define the minute-level wind power volatility and the minute-level photovoltaic power volatility, and provides a more reliable data basis for subsequent model construction.

[0054] The steps of processing the wind power output power fluctuation and the photovoltaic power output power fluctuation based on the smoothing effect strategy can specifically include: Moving average method: For example, for the wind power output power time series data, a time window of a certain length (such as 5 minutes) can be selected, the average value of the data within each window is calculated, and then these average values are used to form a new data series. In this way, a smoothed wind power output power curve can be obtained, making its fluctuation more stable and facilitating the analysis of its long-term trend and periodic changes. The same method can also be used to process the photovoltaic power output power data.

[0055] Exponential smoothing method: Give higher weights to recent data and lower weights to distant data. By adjusting the smoothing coefficient, the data can better reflect the latest change trend while retaining certain historical information. For example, for the wind power output power data, an appropriate smoothing coefficient can be selected according to practical experience to perform exponential smoothing processing on the data to obtain a smoother curve for defining the minute-level wind power volatility.

[0056] Furthermore, for the data preparation of constructing the wind power scatter plot: Use the minute-level wind power volatility data after smoothing processing as the abscissa, and the corresponding per-unit value of wind power output (the ratio of the actual output power to the rated power) as the ordinate to form a set of data points. For example, if the minute-level wind power volatility is calculated to be 0.05 (assuming the unit) within a certain minute, and the corresponding per-unit value of wind power output at this time is 0.8, then a point with coordinates (0.05, 0.8) can be found in the scatter plot.

[0057] Function of constructing wind power scatter plot: By constructing such a scatter plot, the relationship between the minute-level volatility of wind power and the per-unit value of wind power output can be visually observed. If there is an obvious correlation between the two, the data points may show a certain specific distribution pattern, such as linear, non-linear, etc., which provides a basis for subsequent selection of a model for fitting.

[0058] Data preparation for constructing photovoltaic scatter plot: Similar to wind power, the processed minute-level volatility data of photovoltaic power is used as the abscissa, and the per-unit value of photovoltaic power output is used as the ordinate to form the data points of the photovoltaic scatter plot. For example, when the minute-level volatility of photovoltaic power is 0.03 (assuming the unit) at a certain minute and the corresponding per-unit value of photovoltaic power output is 0.7, a point with coordinates (0.03, 0.7) will appear in the scatter plot.

[0059] Function of constructing photovoltaic scatter plot: Similarly, the photovoltaic scatter plot can help us analyze the mapping relationship between the minute-level volatility of photovoltaic power and the per-unit value of photovoltaic power output. By observing the distribution pattern of the scatter plot, the type of relationship between the two can be initially judged, such as whether there is a linear relationship, exponential relationship, etc., thus providing a reference for determining the form of the photovoltaic interval estimation model.

[0060] Furthermore, the steps for determining the wind power interval estimation model include: Select the double-exponential function as the fitting function, and its formula is: Where, is the minute-level volatility of wind power, , , , are the fitting parameters between the minute-level volatility of wind power and wind power respectively, is the per-unit value of wind power output and thermal power installed capacity. This function can better adapt to various data change trends and has strong fitting ability.

[0061] The specific fitting process can be: Using professional data analysis software or programming languages (such as the SciPy library in Python), input the data of the constructed wind power scatter plot into the algorithm, and adjust the parameters , , , The value of makes the fitting function as close to the data points in the scatter plot as possible. For example, by continuously adjusting the parameters, the sum of square errors between the fitting curve and the actual data points is minimized, thereby obtaining the best fitting effect. The final double exponential function is the wind power interval estimation model, which can characterize the mapping relationship between the minute-level volatility of wind power and the historical wind power output.

[0062] Furthermore, the steps of determining the photovoltaic interval estimation model include: The double exponential function is also used as the fitting function, and its formula is: in, is the minute-level fluctuation rate of photovoltaic power, , , , are the fitting parameters between the photovoltaic minute-level volatility and photovoltaic power, is the per unit value of photovoltaic output and thermal power installed capacity. This function can, to a certain extent, reflect the complex variation of photovoltaic output under the influence of various factors such as light intensity.

[0063] The specific fitting process can be: substitute the data of the photovoltaic scatter plot into the selected double exponential function, and use a suitable optimization algorithm (such as gradient descent method, etc.) to estimate the parameters. In the fitting process, it is necessary to consider the daily periodicity characteristics of photovoltaic data, that is, the daily light intensity and temperature and other factors show similar changes. Therefore, corresponding periodic constraints can be added in the fitting process to improve the fitting accuracy of the model. Through continuous iterative optimization, a photovoltaic interval estimation model that can accurately reflect the relationship between the minute-level volatility of photovoltaic power and the historical photovoltaic output power is obtained.

[0064] Further, in some embodiments of the present invention, after the step of constructing a wind power scatter plot based on the wind power minute-level fluctuation rate and the wind power output per unit value, the method includes: Calculate the wind power mean and wind power standard deviation of the wind power minute-level fluctuation rate; Calculating the wind power deviation between each data point in the wind power scatter plot and the wind power mean, and comparing the wind power deviation with three times the wind power standard deviation; Eliminate data points whose wind power deviation is greater than or equal to 3 times the wind power standard deviation from the wind power scatter plot; After the step of constructing a photovoltaic scatter plot based on the photovoltaic power minute-level fluctuation rate and the photovoltaic output per unit value, the method includes: Calculate the photovoltaic mean and photovoltaic standard deviation of the photovoltaic power minute-level fluctuation rate; Calculate the photovoltaic deviation between each data point in the photovoltaic scatter plot and the photovoltaic mean value, and compare and process the photovoltaic deviation with 3 times the photovoltaic standard deviation; Remove the data points with the photovoltaic deviation greater than or equal to 3 times the photovoltaic standard deviation from the photovoltaic scatter plot.

[0065] It can be understood that the steps for calculating the wind power minute-level volatility mean value and the wind power standard deviation are as follows: The wind power mean value is calculated by adding up all the data of the wind power minute-level volatility and then dividing by the total number of data points. For example, if there are 100 data points of the wind power minute-level volatility, after finding their total sum and dividing by 100, the wind power mean value is obtained.

[0066] The wind power standard deviation is used to measure the degree of dispersion of these data points relative to the mean value. When calculating, first find the square of the difference between each data point and the mean value, then add up these squared values, divide by the total number of data points (for sample data, in some statistical methods, it is divided by the total number minus 1), and finally take the square root of this result to obtain the wind power standard deviation.

[0067] It should be noted that the wind power mean value reflects the average level of the wind power minute-level volatility, providing a benchmark for subsequent judgment of the deviation of data points. The wind power standard deviation represents the degree of data fluctuation. The larger the standard deviation, the greater the degree of data dispersion. These two statistical quantities can help us understand the overall characteristics of the wind power minute-level volatility data and are the basis for subsequent deviation analysis and outlier removal.

[0068] The steps for calculating the wind power deviation between each data point in the wind power scatter plot and the wind power mean value and comparing the wind power deviation with 3 times the wind power standard deviation include: For each data point in the wind power scatter plot (assuming the corresponding wind power minute-level volatility is x), calculate the difference between it and the wind power mean value (assuming it is ), that is, the wind power deviation . Then compare this wind power deviation with 3 times the wind power standard deviation (assuming the wind power standard deviation is , then 3 times the wind power standard deviation is 3 ).

[0069] According to the comparison result, if the wind power deviation of a certain data point is greater than or equal to 3 times the wind power standard deviation, it is considered an outlier and removed from the wind power scatter plot.

[0070] By calculating the wind power deviation, the difference size between each data point and the mean value can be clarified. Comparing the wind power deviation with 3 times the wind power standard deviation is based on the "3 The "principle". In a normal distribution, the probability of data points exceeding 3 standard deviations from the mean is very low. This comparison helps identify data points that deviate far from the majority of the data, i.e., potential outliers.

[0071] Removing outliers is to improve the accuracy and reliability of the subsequent construction of the wind power interval estimation model. Outliers may cause significant interference to the fitting of the model, resulting in the model not accurately reflecting the true relationship between the minute-level volatility of wind power and the per-unit value of wind power output. By removing these outliers, the remaining data can better conform to the normal distribution law, making the constructed wind power interval estimation model better reflect the overall characteristics and trends of the data.

[0072] In some embodiments, the steps of calculating the photovoltaic mean and photovoltaic standard deviation of the minute-level volatility of photovoltaic power include: The calculation method of the photovoltaic mean is similar to that of the wind power mean, that is, adding up all the data of the minute-level volatility of photovoltaic power and then dividing by the total number of data points. For example, if there are 200 data points of the minute-level volatility of photovoltaic power, after finding their total sum and dividing by 200, the photovoltaic mean is obtained.

[0073] The calculation of the photovoltaic standard deviation is also to first find the square of the difference between each data point and the mean, then add up these squared values, divide by the total number of data points (for sample data, in some statistical methods, it is divided by the total number minus 1), and finally take the square root to obtain.

[0074] The photovoltaic mean reflects the average level of the minute-level volatility of photovoltaic power and provides a benchmark for subsequent judgment of the deviation of data points. The photovoltaic standard deviation represents the degree of data fluctuation. The larger the standard deviation, the greater the degree of data dispersion. These two statistical quantities can help understand the overall characteristics of the minute-level volatility data of photovoltaic power and are the basis for subsequent deviation analysis and outlier removal.

[0075] The steps of calculating the photovoltaic deviation between each data point in the photovoltaic scatter plot and the photovoltaic mean and comparing and processing the photovoltaic deviation with 3 times the photovoltaic standard deviation include: For each data point in the photovoltaic scatter plot (assuming its corresponding minute-level volatility of photovoltaic power is y), calculate the difference between it and the photovoltaic mean (assuming it is ), that is, the photovoltaic deviation .

[0076] Then compare this photovoltaic deviation with 3 times the photovoltaic standard deviation (assuming the photovoltaic standard deviation is , then 3 times the photovoltaic standard deviation is 3 ).

[0077] According to the comparison results of the above photovoltaic deviations, if the photovoltaic deviation of a certain data point is greater than or equal to 3 times the photovoltaic standard deviation, it is considered an outlier and removed from the photovoltaic scatter plot.

[0078] By calculating the photovoltaic deviation, the magnitude of the difference between each data point and the mean value can be determined. Comparing the photovoltaic deviation with 3 times the photovoltaic standard deviation is based on the "3σ principle" in statistics. In a normal distribution, the probability of a data point that is more than 3 times the standard deviation away from the mean is very low. This comparison helps to identify those data points that deviate far from most of the data, i.e., potential outliers.

[0079] Removing outliers is to improve the accuracy and reliability of the subsequent construction of the photovoltaic interval estimation model. Outliers may cause significant interference to the fitting of the model, resulting in the model not being able to accurately reflect the true relationship between the minute-level volatility of photovoltaic power and the per-unit value of photovoltaic output. By removing these outliers, the remaining data can better conform to the normal distribution law, making the constructed photovoltaic interval estimation model better reflect the overall characteristics and trends of the data.

[0080] In step S400 of some embodiments, based on the power fluctuation interval estimation model and the actual output data of the power system, the maximum estimated value of power fluctuation is determined.

[0081] It should be noted that the actual output data at least includes: the actual output power of wind power, the actual output power of photovoltaic power.

[0082] The maximum estimated value of power fluctuation at least includes: the maximum estimated value of minute-level power fluctuation of wind power, the maximum estimated value of minute-level power fluctuation of photovoltaic power.

[0083] It can be understood that after the execution of step S300, the specific execution steps can be: based on the wind power mapping function corresponding to the wind power mapping relationship and the actual output power of wind power in the power system, determine the maximum estimated value of minute-level power fluctuation of wind power; Based on the photovoltaic mapping function corresponding to the photovoltaic mapping relationship and the actual output power of photovoltaic power in the power system, determine the maximum estimated value of minute-level power fluctuation of photovoltaic power.

[0084] Furthermore, the actual output power of wind power is measured by devices such as sensors installed on wind turbines. These sensors can monitor information such as the rotational speed of the wind turbine, the blade angle, and the wind speed in real time, and then calculate the actual output power of wind power based on this information. The actual output power of electricity reflects the instantaneous value of the electric energy transmitted by the wind power generation system to the power grid during actual operation. It is one of the key input data for constructing the wind power interval estimation model and is used to determine the maximum estimated value of wind power fluctuation. By analyzing the variation of the actual output power of wind power over time, the fluctuation characteristics of wind power can be understood, providing basic data support for subsequent power fluctuation interval estimation.

[0085] The actual output power of photovoltaic power is mainly obtained by converting solar energy into electrical energy through photovoltaic panels in the photovoltaic module, and then converting direct current into alternating current through an inverter. At the same time, relevant electrical parameter information such as current and voltage can be obtained during the conversion process. Based on these electrical parameters and factors such as the area of the photovoltaic array, the light intensity, and the temperature correction coefficient, the power calculation formula is used to calculate the actual output power of photovoltaic power. For example, according to parameters such as the short-circuit current, open-circuit voltage, maximum power point current, and voltage of the photovoltaic panel, combined with the current light intensity and environmental temperature, the actual output power of photovoltaic power is calculated through a specific algorithm.

[0086] The actual output power of photovoltaic power reflects the actual power generation capacity of the photovoltaic power generation system under different environmental conditions. It is also an important input data for constructing the photovoltaic interval estimation model and is used to determine the maximum estimated value of photovoltaic power fluctuation. Analyzing the variation law of the actual output power of photovoltaic power can help to master the characteristics of photovoltaic output, thus providing a basis for accurately estimating the range of photovoltaic power fluctuation.

[0087] In some embodiments, the maximum estimated value of the wind power minute-level power fluctuation is determined based on the wind power mapping function corresponding to the wind power mapping relationship and the actual output power of wind power in the power system. First, a wind power mapping function needs to be established, which reflects the mapping relationship between the actual output power of wind power and the wind power fluctuation.

[0088] In some embodiments, it can also be obtained by analyzing and fitting a large amount of historical wind power output data and corresponding power fluctuation data. For example, methods such as linear regression and neural networks can be used to establish this mapping relationship. Then, the actual output power of wind power obtained by real-time monitoring is substituted into the wind power mapping function to calculate the maximum estimated value of the wind power minute-level power fluctuation.

[0089] The maximum estimated value of wind power minute - level power fluctuation provides a quantitative index for evaluating the uncertainty of wind power output. In the operation and dispatch of power systems, understanding the fluctuation range of wind power is crucial for ensuring the safe and stable operation of power systems. By accurately estimating the maximum value of wind power minute - level power fluctuation, it can help dispatchers formulate corresponding dispatch strategies in advance, such as adjusting the output of conventional energy units and arranging the charge - discharge plans of energy storage systems, to cope with the fluctuation of wind power output and ensure the power supply - demand balance and power quality of power systems.

[0090] In some embodiments, the maximum estimated value of photovoltaic minute - level power fluctuation is determined based on the photovoltaic mapping function corresponding to the photovoltaic mapping relationship and the actual photovoltaic output power of the power system. Similarly, it is necessary to first establish a photovoltaic mapping function, which describes the mapping relationship between the actual photovoltaic output power and the photovoltaic power fluctuation.

[0091] In some embodiments, this mapping function can also be obtained through the analysis and processing of historical photovoltaic output data and power fluctuation data. For example, considering the influence of factors such as light intensity and temperature on photovoltaic output, a non - linear mapping model is established using machine learning algorithms. Then, the currently measured actual photovoltaic output power is input into the photovoltaic mapping function to calculate the maximum estimated value of photovoltaic minute - level power fluctuation.

[0092] The maximum estimated value of photovoltaic minute - level power fluctuation helps to evaluate the reliability and stability of photovoltaic power generation. In power systems, the access ratio of photovoltaic power generation is gradually increasing, and the impact of its output volatility on the power grid is becoming increasingly significant. Accurately estimating the maximum value of photovoltaic minute - level power fluctuation can provide an important reference for power grid dispatch, enabling dispatchers to reasonably arrange photovoltaic output and optimize the operation mode of the power grid. For example, when the photovoltaic output fluctuates greatly, the dependence on photovoltaic can be appropriately reduced, the output of other stable power sources can be increased, or the energy storage system can be used to smooth the fluctuation of photovoltaic output, thereby improving the overall operation performance of the power grid.

[0093] Figure 2 It is the second flow schematic diagram of a method for estimating the maximum wind - solar power fluctuation range based on wavelet decomposition provided by the present invention. Obtain the historical wind power output power and historical photovoltaic output power of the power system; select a wavelet basis function and determine the decomposition level, and then decompose the historical wind power output power and the historical photovoltaic output power to determine the wind power output power fluctuation corresponding to the historical wind power output power and the photovoltaic output power fluctuation corresponding to the historical photovoltaic output power.

[0094] Define the wind power output power fluctuation and the photovoltaic output power fluctuation according to the smoothing effect, and determine the corresponding wind power minute-level volatility and photovoltaic power minute-level volatility. Use a double-exponential function fitting to determine the wind power interval estimation model and the photovoltaic interval estimation model.

[0095] Finally, based on the wind power interval estimation model and the actual wind power output power of the power system, determine the maximum estimated value of the wind power minute-level power fluctuation. And based on the photovoltaic interval estimation model and the actual photovoltaic power output power of the power system, determine the maximum estimated value of the photovoltaic minute-level power fluctuation.

[0096] The maximum estimated value of the wind power minute-level power fluctuation and the maximum estimated value of the photovoltaic minute-level power fluctuation can be used to update the wind power output power and the photovoltaic output power. Furthermore, a simple and efficient estimation of the maximum wind-solar power fluctuation result can be achieved.

[0097] In some embodiments, the proportion of the wind-solar minute-level fluctuation component in the output will decrease with the smoothing effect of wind power and photovoltaic power. In order to analyze the proportion of the wind and photovoltaic power minute-level fluctuation components in the wind and photovoltaic power output respectively, the wind and photovoltaic power minute-level volatilities are defined as follows: Among them, is the wind power minute-level volatility, is the amplitude of the wind power minute-level fluctuation component at the th minute, is the th minute of wind power; is the photovoltaic power minute-level volatility, is the amplitude of the photovoltaic minute-level fluctuation component at the th minute, is the th minute of photovoltaic power.

[0098] Statistical historical wind power data amplitudes are obtained to produce a scatter plot of wind power volatility against output power magnitude, and outliers in the wind and photovoltaic minute-level volatilities are removed according to the 3 principle. For wind power, the relationship between the wind power minute-level volatility and the per-unit value of wind power output can be obtained. To estimate the maximum value of the wind power minute-level volatility, its outer envelope can be fitted with a double-exponential function. Thus, the relationship between the maximum wind power minute-level volatility and the wind power is: Among them, is the wind power minute-level volatility, , , , are the fitting parameters between the minute - level volatility of the maximum wind power and the wind power respectively, is the per - unit value of the wind power output and the thermal power installed capacity. Similarly, the relationship between the maximum minute - level fluctuating power of photovoltaic and the minute - level volatility of photovoltaic can be obtained as: where, is the minute - level volatility of the photovoltaic power, 、 、 、 are the fitting parameters between the maximum minute - level volatility of photovoltaic and the photovoltaic power respectively, is the per - unit value of the photovoltaic output and the thermal power installed capacity.

[0099] It should be noted that the final formula for representing the maximum estimated value of the minute - level power fluctuation of wind power is: where, is the maximum estimated value of the minute - level power fluctuation of wind power, is the wind power at the th minute, 、 、 、 are the fitting parameters between the maximum minute - level volatility of wind power and the wind power respectively, is the per - unit value of the wind power output and the thermal power installed capacity.

[0100] The final formula for representing the maximum estimated value of the minute - level power fluctuation of photovoltaic can be: where, is the maximum estimated value of the minute - level power fluctuation of photovoltaic, 、 、 、 are the fitting parameters between the maximum minute - level volatility of photovoltaic and the photovoltaic power respectively, is the th minute of the photovoltaic power, is the per - unit value of the photovoltaic output and the thermal power installed capacity.

[0101] A method, device, equipment and storage medium for estimating the maximum wind-solar power fluctuation range based on wavelet decomposition provided by the present invention obtain the historical wind power output and historical photovoltaic power output of the power system; respectively perform decomposition processing on the historical wind power output and the historical photovoltaic power output based on the wavelet decomposition strategy to determine the wind power output fluctuation corresponding to the historical wind power output and the photovoltaic power output fluctuation corresponding to the historical photovoltaic power output; construct a power fluctuation range estimation model based on the wind power output fluctuation and the photovoltaic power output fluctuation; and determine the maximum estimated value of the power fluctuation based on the power fluctuation range estimation model and the actual output data of the power system. It is used to solve the defect of large estimation error and low accuracy in estimating the maximum wind-solar power fluctuation range in the prior art, and realize the simple and efficient estimation of the maximum wind-solar power fluctuation result.

[0102] The following describes the device for estimating the maximum wind-solar power fluctuation range based on wavelet decomposition provided by the present invention. The device for estimating the maximum wind-solar power fluctuation range based on wavelet decomposition described below can be mutually corresponding and referred to the method for estimating the maximum wind-solar power fluctuation range based on wavelet decomposition described above.

[0103] As Figure 3 shown is a schematic structural diagram of a device for estimating the maximum wind-solar power fluctuation range based on wavelet decomposition provided by the present invention. A device for estimating the maximum wind-solar power fluctuation range based on wavelet decomposition includes the following modules: An acquisition module 310, configured to acquire the historical wind power output and historical photovoltaic power output of the power system; A determining output power fluctuation module 320, configured to respectively perform decomposition processing on the historical wind power output and the historical photovoltaic power output based on the wavelet decomposition strategy to determine the wind power output fluctuation corresponding to the historical wind power output and the photovoltaic power output fluctuation corresponding to the historical photovoltaic power output; A construction module 330, configured to construct a power fluctuation range estimation model based on the wind power output fluctuation and the photovoltaic power output fluctuation; A determining maximum estimated value module 340, configured to determine the maximum estimated value of the power fluctuation based on the power fluctuation range estimation model and the actual output data of the power system.

[0104] Preferably, the device for estimating the maximum wind-solar power fluctuation range based on wavelet decomposition provided by the present invention is specifically further configured to determine the wavelet basis function for analyzing the new energy power signal based on the historical wind power output and the historical photovoltaic power output, where the wavelet basis function is the function corresponding to the wavelet decomposition strategy; Determine the decomposition level for decomposing the historical wind power output and the historical photovoltaic power output, and based on the wavelet basis function, layer-by-layer decompose the historical wind power output and the historical photovoltaic power output with reference to the decomposition level to obtain the wind power decomposition result and the photovoltaic power decomposition result corresponding to each layer; Based on the wind power decomposition result and the photovoltaic power decomposition result, perform minute-level fluctuation component extraction processing respectively to obtain the corresponding wind power output fluctuation and the photovoltaic power output fluctuation.

[0105] Preferably, the device for estimating the maximum wind-solar power fluctuation interval based on wavelet decomposition provided by the present invention is specifically further used for the power fluctuation interval estimation model including: a wind power interval estimation model and a photovoltaic interval estimation model; Based on the smoothing effect strategy, perform definition processing on the wind power output fluctuation and the photovoltaic power output fluctuation to determine the corresponding wind power minute-level volatility and photovoltaic power minute-level volatility; Based on the wind power minute-level volatility and the per-unit value of wind power output, construct a wind power scatter plot, and based on the photovoltaic power minute-level volatility and the per-unit value of photovoltaic power output, construct a photovoltaic power scatter plot; Use a double-exponential function to fit the outer envelope of the wind power scatter plot to determine the wind power interval estimation model, and use a double-exponential function to fit the outer envelope of the photovoltaic power scatter plot to determine the photovoltaic interval estimation model.

[0106] Preferably, the device for estimating the maximum wind-solar power fluctuation interval based on wavelet decomposition provided by the present invention is specifically further used for the wind power interval estimation model to represent the wind power mapping relationship between the wind power minute-level volatility and the historical wind power output; The photovoltaic interval estimation model represents the photovoltaic mapping relationship between the photovoltaic power minute-level volatility and the historical photovoltaic power output.

[0107] Preferably, the device for estimating the maximum wind-solar power fluctuation interval based on wavelet decomposition provided by the present invention is specifically further used for the actual output data to at least include: the actual wind power output and the actual photovoltaic power output; The maximum estimated value of power fluctuation at least includes: the maximum estimated value of wind power minute-level power fluctuation and the maximum estimated value of photovoltaic minute-level power fluctuation; Based on the wind power mapping function corresponding to the wind power mapping relationship and the actual wind power output of the power system, determine the maximum estimated value of wind power minute-level power fluctuation; Based on the photovoltaic mapping function corresponding to the photovoltaic mapping relationship and the actual photovoltaic power output of the power system, determine the maximum estimated value of photovoltaic minute-level power fluctuation.

[0108] Preferably, the maximum wind-solar power fluctuation interval estimation device based on wavelet decomposition provided by the present invention is further specifically configured to calculate the wind power mean value and the wind power standard deviation of the minute-level volatility of the wind power; calculate the wind power deviation between each data point in the wind power scatter plot and the wind power mean value, and compare and process the wind power deviation with 3 times the wind power standard deviation; Remove the data points whose wind power deviation is greater than or equal to 3 times the wind power standard deviation from the wind power scatter plot.

[0109] Preferably, the maximum wind-solar power fluctuation interval estimation device based on wavelet decomposition provided by the present invention is further specifically configured to calculate the photovoltaic mean value and the photovoltaic standard deviation of the minute-level volatility of the photovoltaic power; calculate the photovoltaic deviation between each data point in the photovoltaic scatter plot and the photovoltaic mean value, and compare and process the photovoltaic deviation with 3 times the photovoltaic standard deviation; Remove the data points whose photovoltaic deviation is greater than or equal to 3 times the photovoltaic standard deviation from the photovoltaic scatter plot.

[0110] A method, device, equipment and storage medium for estimating the maximum wind-solar power fluctuation interval based on wavelet decomposition provided by the present invention obtain the historical wind power output and the historical photovoltaic power output of the power system; respectively decompose the historical wind power output and the historical photovoltaic power output based on the wavelet decomposition strategy to determine the wind power output fluctuation corresponding to the historical wind power output and determine the photovoltaic power output fluctuation corresponding to the historical photovoltaic power output; construct a power fluctuation interval estimation model based on the wind power output fluctuation and the photovoltaic power output fluctuation; determine the maximum estimated value of the power fluctuation based on the power fluctuation interval estimation model and the actual output data of the power system. It is used to solve the defect of large estimation error and low accuracy of the maximum wind-solar power fluctuation interval in the prior art, and realize the simple and efficient estimation of the maximum wind-solar power fluctuation result.

[0111] Figure 4 Illustrates a schematic diagram of the physical structure of an electronic device, such as Figure 4As shown, the electronic device may include: a processor 410, a communications interface 420, a memory 430, and a communication bus 440. Among them, the processor 410, the communications interface 420, and the memory 430 complete communication with each other through the communication bus 440. The processor 410 may call the logical instructions in the memory 430 to execute the method for estimating the maximum wind-solar power fluctuation interval based on wavelet decomposition. The method includes: obtaining the historical wind power output and the historical photovoltaic power output of the power system; respectively performing decomposition processing on the historical wind power output and the historical photovoltaic power output based on the wavelet decomposition strategy to determine the wind power output fluctuation corresponding to the historical wind power output and to determine the photovoltaic power output fluctuation corresponding to the historical photovoltaic power output; constructing a power fluctuation interval estimation model based on the wind power output fluctuation and the photovoltaic power output fluctuation; and determining the maximum estimated value of the power fluctuation based on the power fluctuation interval estimation model and the actual output data of the power system.

[0112] In addition, when the logical instructions in the foregoing memory 430 are implemented in the form of software functional units and sold or used as independent products, they may be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, may be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as a USB flash drive, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk, or an optical disc that can store program codes.

[0113] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the method for estimating the maximum wind-solar power fluctuation interval based on wavelet decomposition provided by the above-mentioned various methods. The method includes: obtaining the historical wind power output and historical photovoltaic power output of the power system; respectively performing decomposition processing on the historical wind power output and the historical photovoltaic power output based on a wavelet decomposition strategy to determine the wind power output fluctuation corresponding to the historical wind power output and the photovoltaic power output fluctuation corresponding to the historical photovoltaic power output; constructing a power fluctuation interval estimation model based on the wind power output fluctuation and the photovoltaic power output fluctuation; and determining the maximum estimated value of the power fluctuation based on the power fluctuation interval estimation model and the actual output data of the power system.

[0114] In another aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it realizes the method for estimating the maximum wind-solar power fluctuation interval based on wavelet decomposition provided by the above-mentioned various methods. The method includes: obtaining the historical wind power output and historical photovoltaic power output of the power system; respectively performing decomposition processing on the historical wind power output and the historical photovoltaic power output based on a wavelet decomposition strategy to determine the wind power output fluctuation corresponding to the historical wind power output and the photovoltaic power output fluctuation corresponding to the historical photovoltaic power output; constructing a power fluctuation interval estimation model based on the wind power output fluctuation and the photovoltaic power output fluctuation; and determining the maximum estimated value of the power fluctuation based on the power fluctuation interval estimation model and the actual output data of the power system The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0115] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0116] 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 them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for estimating the maximum wind and light power fluctuation interval based on wavelet decomposition, characterized in that Including: Obtaining the historical wind power output and historical photovoltaic power output of the power system; Based on the wavelet decomposition strategy, respectively decompose the historical wind power output and the historical photovoltaic power output, determine the wind power output fluctuation corresponding to the historical wind power output, and determine the photovoltaic power output fluctuation corresponding to the historical photovoltaic power output; Based on the wind power output fluctuation and the photovoltaic power output fluctuation, construct a power fluctuation interval estimation model; Based on the power fluctuation interval estimation model and the actual output data of the power system, determine the maximum estimated value of the power fluctuation.

2. The method for estimating the maximum wind and light power fluctuation interval based on wavelet decomposition according to claim 1, wherein The step of respectively decomposing the historical wind power output and the historical photovoltaic power output based on the wavelet decomposition strategy, determining the wind power output fluctuation corresponding to the historical wind power output, and determining the photovoltaic power output fluctuation corresponding to the historical photovoltaic power output includes: Based on the historical wind power output and the historical photovoltaic power output, determine the wavelet basis function for analyzing the new energy power signal, where the wavelet basis function is the function corresponding to the wavelet decomposition strategy; Determine the decomposition level for decomposing the historical wind power output and the historical photovoltaic power output, and based on the wavelet basis function, decompose the historical wind power output and the historical photovoltaic power output layer by layer according to the decomposition level to obtain the wind power decomposition result and the photovoltaic power decomposition result corresponding to each layer; Based on the wind power decomposition result and the photovoltaic power decomposition result, respectively perform minute-level fluctuation component extraction processing to obtain the corresponding wind power output fluctuation and the photovoltaic power output fluctuation.

3. The method for estimating the maximum wind and light power fluctuation interval based on wavelet decomposition according to claim 1, wherein The power fluctuation interval estimation model includes: a wind power interval estimation model and a photovoltaic power interval estimation model; The step of constructing a power fluctuation interval estimation model based on the wind power output fluctuation and the photovoltaic power output fluctuation includes: Based on the smoothing effect strategy, define the wind power output fluctuation and the photovoltaic power output fluctuation to determine the corresponding minute-level wind power volatility and minute-level photovoltaic power volatility; Based on the minute-level wind power volatility and the per-unit value of the wind power output, construct a wind power scatter plot, and based on the minute-level photovoltaic power volatility and the per-unit value of the photovoltaic power output, construct a photovoltaic power scatter plot; Use a double-exponential function to fit the outer envelope of the wind power scatter plot to determine the wind power interval estimation model, and use a double-exponential function to fit the outer envelope of the photovoltaic power scatter plot to determine the photovoltaic power interval estimation model.

4. The method for estimating the maximum wind and light power fluctuation interval based on wavelet decomposition according to claim 3, characterized in that The method includes: The wind power interval estimation model represents the wind power mapping relationship between the minute-level wind power volatility and the historical wind power output; The photovoltaic power interval estimation model represents the photovoltaic power mapping relationship between the minute-level photovoltaic power volatility and the historical photovoltaic power output.

5. The method for estimating the maximum wind and light power fluctuation interval based on wavelet decomposition according to claim 4, wherein The actual output data at least includes: the actual wind power output and the actual photovoltaic power output; The maximum estimated value of the power fluctuation at least includes: the maximum estimated value of the minute-level wind power fluctuation and the maximum estimated value of the minute-level photovoltaic power fluctuation; Determining a maximum estimated power fluctuation value based on the power fluctuation interval estimation model and the actual output data of the power system includes: Determining a maximum estimated minute-level power fluctuation value of wind power based on a wind power mapping function corresponding to the wind power mapping relationship and the actual output power of wind power of the power system; Determining a maximum estimated minute-level power fluctuation value of photovoltaic power based on a photovoltaic power mapping function corresponding to the photovoltaic mapping relationship and the actual output power of photovoltaic power of the power system.

6. The method for estimating the maximum wind-solar power fluctuation interval based on wavelet decomposition according to claim 3, wherein After the step of constructing a wind power scatter plot based on the minute-level volatility of wind power and the per-unit value of wind power output, the method includes: Calculating the mean value of wind power and the standard deviation of wind power of the minute-level volatility of wind power; Calculating the wind power deviation between each data point in the wind power scatter plot and the mean value of wind power, and comparing and processing the wind power deviation with three times the standard deviation of wind power; Removing the data points with wind power deviation greater than or equal to three times the standard deviation of wind power from the wind power scatter plot; After the step of constructing a photovoltaic power scatter plot based on the minute-level volatility of photovoltaic power and the per-unit value of photovoltaic power output, the method includes: Calculating the mean value of photovoltaic power and the standard deviation of photovoltaic power of the minute-level volatility of photovoltaic power; Calculating the photovoltaic power deviation between each data point in the photovoltaic power scatter plot and the mean value of photovoltaic power, and comparing and processing the photovoltaic power deviation with three times the standard deviation of photovoltaic power; Removing the data points with photovoltaic power deviation greater than or equal to three times the standard deviation of photovoltaic power from the photovoltaic power scatter plot.

7. An apparatus for estimating the maximum fluctuation interval of wind and light power based on wavelet decomposition, characterized in that, Including: An acquisition module for acquiring the historical output power of wind power and the historical output power of photovoltaic power of the power system; An output power fluctuation determination module for respectively performing decomposition processing on the historical output power of wind power and the historical output power of photovoltaic power based on a wavelet decomposition strategy to determine the output power fluctuation of wind power corresponding to the historical output power of wind power and to determine the output power fluctuation of photovoltaic power corresponding to the historical output power of photovoltaic power; A construction module for constructing a power fluctuation interval estimation model based on the output power fluctuation of wind power and the output power fluctuation of photovoltaic power; A maximum estimated value determination module for determining a maximum estimated power fluctuation value based on the power fluctuation interval estimation model and the actual output data of the power system.

8. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and running on the processor, characterized in that, When the processor executes the program, it implements the maximum wind-solar power fluctuation interval estimation method based on wavelet decomposition according to any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the maximum wind-solar power fluctuation interval estimation method based on wavelet decomposition according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the maximum wind-solar power fluctuation interval estimation method based on wavelet decomposition according to any one of claims 1 to 6.

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