Method, apparatus, equipment, and storage medium for estimating the maximum wind and solar power fluctuation range based on wavelet decomposition.

Abstract

This invention provides a method, apparatus, device, and storage medium for estimating the maximum wind and solar power fluctuation range based on wavelet decomposition, relating to the field of new energy power generation technology. The method includes: acquiring historical wind power output and historical photovoltaic power output of the power system; decomposing the historical wind power output and historical photovoltaic power output respectively 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 range estimation model based on the wind power output fluctuation and photovoltaic power output fluctuation; and determining the maximum estimated value of the power fluctuation based on the power fluctuation range estimation model and the actual power output data of the power system. The embodiments provided by this invention address the shortcomings of existing technologies in estimating the maximum wind and solar power fluctuation range, which suffers from large errors and low accuracy, achieving a simple and efficient estimation of the maximum wind and solar power fluctuation result.

Description

Technical Field

[0001] This invention relates to the field of new energy power generation technology, and in particular to a method, apparatus, equipment and storage medium for estimating the maximum wind and solar power fluctuation range based on wavelet decomposition. Background Technology

[0002] As the global energy structure transitions towards low-carbon and clean energy, the penetration rate of renewable energy sources, such as wind and solar power, 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 power output, especially on minute-level time scales. Dramatic power fluctuations can trigger problems such as frequency exceeding limits and voltage instability, seriously threatening the safe operation of the power grid. Among related technologies, quantitative analysis methods for wind and solar power fluctuations are mainly divided into two categories: statistical modeling methods and physical mechanism methods.

[0003] I. Statistical modeling methods construct probability distribution models (such as normal distribution and Weibull distribution) from historical data to describe the statistical characteristics of power output fluctuations. However, such methods have significant limitations: First, traditional single-scale statistical models struggle to capture dynamic behaviors that change rapidly in minute-level intervals; second, the power output characteristics of wind power and solar power differ significantly (wind power is affected by wind speed turbulence, while solar power is affected by cloud cover), and existing methods often employ independent analysis models, failing to fully consider the "smoothing effect" of the combined fluctuations of the two, resulting in large biases in the estimation of joint fluctuations.

[0004] II. The physical mechanism method is based on numerical meteorological forecasts and equipment physical models to simulate the power output fluctuation pattern. However, this method relies on high-precision meteorological data and complex parameter calibration, resulting in low computational efficiency and difficulty in adapting to real-time scheduling requirements, especially with significant errors in extreme weather scenarios.

[0005] In summary, accurately estimating the maximum fluctuation range of wind and solar power is a crucial prerequisite for real-time power system dispatch, reserve capacity allocation, and stability control. Therefore, how to accurately estimate the maximum fluctuation range of wind and solar power is a pressing technical problem that needs to be solved. Summary of the Invention

[0006] This invention provides a method, apparatus, device, and storage medium for estimating the maximum wind and solar power fluctuation range based on wavelet decomposition, which solves the defects of large estimation error and low accuracy of the maximum wind and solar power fluctuation range in the prior art, and realizes a 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:

[0008] Obtain historical wind power output and historical photovoltaic power output of the power system;

[0009] Based on the wavelet decomposition strategy, the historical wind power output and the historical photovoltaic power output are decomposed respectively 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.

[0010] Based on the power output fluctuations of wind power and photovoltaic power, a power fluctuation range estimation model is constructed.

[0011] Based on the power fluctuation range estimation model and the actual power output data of the power system, the maximum estimated value of power fluctuation is determined.

[0012] Preferably, according to the maximum wind and solar power fluctuation range estimation method based on wavelet decomposition provided by the present invention, the wavelet decomposition strategy decomposes the historical wind power output and the historical photovoltaic power output respectively 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, including:

[0013] Based on the historical wind power output and the historical photovoltaic power output, the wavelet basis function for analyzing the new energy power signal is determined, wherein the wavelet basis function is a function corresponding to the wavelet decomposition strategy;

[0014] The number of decomposition layers for the historical wind power output and the historical photovoltaic power output is determined, and based on the wavelet basis function, the historical wind power output and the historical photovoltaic power output are decomposed layer by layer with reference to the number of decomposition layers to obtain the wind power decomposition result and photovoltaic power decomposition result corresponding to each layer;

[0015] Based on the wind power decomposition results and the photovoltaic power decomposition results, minute-level fluctuation component extraction processing is performed to obtain the corresponding wind power output fluctuation and the photovoltaic power output fluctuation, respectively.

[0016] Preferably, according to the maximum wind and solar power fluctuation range estimation method based on wavelet decomposition provided by the present invention, the power fluctuation range estimation model includes: a wind power range estimation model and a photovoltaic range estimation model;

[0017] The method for constructing a power fluctuation range estimation model based on the wind power output fluctuation and the photovoltaic power output fluctuation includes:

[0018] Based on the smoothing effect strategy, the fluctuations in wind power output and photovoltaic power output are defined and processed to determine the corresponding minute-level fluctuation rates of wind power output and photovoltaic power output.

[0019] A scatter plot of wind power is constructed based on the minute-level volatility of wind power and the per-unit value of wind power output, and a scatter plot of photovoltaic power is constructed based on the minute-level volatility of photovoltaic power and the per-unit value of photovoltaic output.

[0020] The outer envelope of the wind power scatter plot is fitted with a double exponential function to determine the wind power interval estimation model, and the outer envelope of the photovoltaic scatter plot is fitted with a double exponential function to determine the photovoltaic interval estimation model.

[0021] Preferably, according to the wavelet decomposition-based maximum wind and solar power fluctuation range estimation method provided by the present invention, the wind power range estimation model characterizes the wind power mapping relationship between the minute-level fluctuation rate of wind power and the historical wind power output.

[0022] The photovoltaic interval estimation model characterizes the photovoltaic mapping relationship between the minute-level fluctuation of photovoltaic power and the historical photovoltaic output power.

[0023] Preferably, in the maximum wind and solar power fluctuation range estimation method based on wavelet decomposition provided by the present invention, the actual power output data includes at least: actual wind power output and actual photovoltaic power output.

[0024] The maximum power fluctuation estimate includes at least: the maximum power fluctuation estimate for wind power at the minute level and the maximum power fluctuation estimate for photovoltaic power at the minute level;

[0025] The determination of the maximum estimated value of power fluctuation based on the power fluctuation range estimation model and the actual power output data of the power system includes:

[0026] Based on the wind power mapping function corresponding to the wind power mapping relationship and the actual wind power output of the power system, the maximum estimated value of wind power minute-level power fluctuation is determined.

[0027] Based on the photovoltaic mapping function corresponding to the photovoltaic mapping relationship and the actual photovoltaic output power of the power system, the maximum estimated value of photovoltaic power fluctuation at the minute level is determined.

[0028] Preferably, according to the wavelet decomposition-based maximum wind and solar power fluctuation range estimation method provided by the present invention, after the step of constructing a wind power scatter plot based on the minute-level fluctuation rate of wind power and the per-unit value of wind power output, the method includes:

[0029] Calculate the mean and standard deviation of the wind power fluctuation rate in minutes;

[0030] Calculate the wind power deviation between each data point in the wind power scatter plot and the wind power mean, and compare the wind power deviation with 3 times the wind power standard deviation.

[0031] Data points whose wind power deviation is greater than or equal to 3 times the wind power standard deviation are removed from the wind power scatter plot;

[0032] After the step of constructing a photovoltaic scatter plot based on the photovoltaic power minute-level volatility and the photovoltaic output per-unit value, the method includes:

[0033] Calculate the photovoltaic mean and standard deviation of the minute-level fluctuation of the photovoltaic power;

[0034] Calculate the photovoltaic deviation between each data point in the photovoltaic scatter plot and the photovoltaic mean, and compare the photovoltaic deviation with 3 times the photovoltaic standard deviation.

[0035] Data points whose photovoltaic deviation is greater than or equal to 3 times the photovoltaic standard deviation are removed from the photovoltaic scatter plot.

[0036] Secondly, the present invention also provides a device for estimating the maximum wind and solar power fluctuation range based on wavelet decomposition, comprising:

[0037] The acquisition module is used to acquire the historical wind power output and historical photovoltaic power output of the power system;

[0038] The output power fluctuation determination module is used to decompose the historical wind power output power and the historical photovoltaic power output power based on the wavelet decomposition strategy, and determine the wind power output power fluctuation corresponding to the historical wind power output power, and determine the photovoltaic power output power fluctuation corresponding to the historical photovoltaic power output power.

[0039] The module is used to construct a power fluctuation range estimation model based on the power fluctuation of wind power output and the power fluctuation of photovoltaic power output;

[0040] The maximum estimate determination module is used to determine the maximum estimate of power fluctuation based on the power fluctuation range estimation model and the actual power output data of the power system.

[0041] Thirdly, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the maximum wind and solar power fluctuation range estimation method based on wavelet decomposition as described above.

[0042] Fourthly, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the maximum wind and solar power fluctuation range estimation method based on wavelet decomposition as described above.

[0043] Fifthly, the present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the maximum wind and solar power fluctuation range estimation method based on wavelet decomposition as described above.

[0044] This invention provides a method, apparatus, device, and storage medium for estimating the maximum wind and solar power fluctuation range based on wavelet decomposition. The method involves acquiring historical wind power output and historical solar power output of a power system; decomposing these historical wind power output and solar power output respectively using a wavelet decomposition strategy to determine the wind power output fluctuation corresponding to the historical wind power output and the solar power output fluctuation corresponding to the historical solar power output; constructing a power fluctuation range estimation model based on the wind power output fluctuation and solar power output fluctuation; and determining the maximum estimated value of the power fluctuation based on the power fluctuation range estimation model and the actual power output data of the power system. This addresses the shortcomings of existing technologies, which suffer from large estimation errors and low accuracy in estimating the maximum wind and solar power fluctuation range, and achieves a simple and efficient estimation of the maximum wind and solar power fluctuation result. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0046] Figure 1 This is one of the flowcharts of a method for estimating the maximum wind and solar power fluctuation range based on wavelet decomposition provided by the present invention.

[0047] Figure 2 This is the second flowchart of a method for estimating the maximum wind and solar power fluctuation range based on wavelet decomposition provided by the present invention.

[0048] Figure 3 This is a schematic diagram of the structure of a maximum wind and solar power fluctuation range estimation device based on wavelet decomposition provided by the present invention.

[0049] Figure 4 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0051] The following is combined Figures 1-4 This invention describes a method, apparatus, device, and storage medium for estimating the maximum wind and solar power fluctuation range based on wavelet decomposition. The method involves acquiring historical wind power output and historical solar power output of a power system; decomposing these historical wind power output and solar power output based on a wavelet decomposition strategy to determine the wind power output fluctuation corresponding to the historical wind power output and the solar power output fluctuation corresponding to the historical solar power output; constructing a power fluctuation range estimation model based on the wind power output fluctuation and solar power output fluctuation; and determining the maximum estimated value of the power fluctuation based on the power fluctuation range estimation model and the actual power output data of the power system. This invention addresses the shortcomings of existing technologies, such as large estimation errors and low accuracy in estimating the maximum wind and solar power fluctuation range, and achieves a simple and efficient estimation of the maximum wind and solar power fluctuation result.

[0052] Figure 1 This is one of the flowcharts illustrating a method for estimating the maximum wind and solar power fluctuation range based on wavelet decomposition provided by the present invention, as shown below. Figure 1 As shown, the method may include, but is not limited to, steps S100 to S400:

[0053] S100, obtains the historical wind power output and historical photovoltaic power output of the power system;

[0054] S200, 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;

[0055] S300, Based on the power output fluctuation of the wind power and the power output fluctuation of the photovoltaic power, a power fluctuation range estimation model is constructed;

[0056] S400, based on the power fluctuation range estimation model and the actual power output data of the power system, determine the maximum estimated value of power fluctuation.

[0057] In step S100 of some embodiments, the historical wind power output and historical photovoltaic power output of the power system are obtained.

[0058] Understandably, power monitoring systems collect and record relevant operational data from various power generation units within the power system in real time, including the output power of wind farms and photovoltaic power plants. This data is collected from sensors on the power generation equipment, such as speed sensors and power factor controllers for wind turbines, as well as output power monitoring devices for photovoltaic inverters.

[0059] The collected data will be transmitted to the monitoring center and stored in the database for subsequent querying and analysis.

[0060] In some embodiments of the present invention, the type and architecture of the power monitoring system are first determined, and its data acquisition method and storage mechanism are understood. Common power monitoring systems include SCADA (Supervisory Control and Data Acquisition) systems.

[0061] Once you have access to the power monitoring system database, you can then query the database for wind and solar power output data for a specific time period. This can be done by writing SQL (Structured Query Language) queries or using the data query tools built into the monitoring system.

[0062] In step S200 of some embodiments, the historical wind power output and the historical photovoltaic power output are decomposed 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.

[0063] First, it's important to clarify that wavelet decomposition is a signal processing technique that decomposes a signal into components with different time scales and frequency ranges. Compared to traditional Fourier transform, wavelet decomposition can analyze signals simultaneously in the time and frequency domains, making it particularly suitable for processing non-stationary signals. This characteristic gives wavelet decomposition a significant advantage in the decomposition of new energy power fluctuations. Furthermore, the method based on double exponential function fitting, which describes the relationship between the minute-level fluctuation rate of new energy power and the actual per-unit output, and estimates the fluctuation range, has the following significant advantages: it can accurately characterize the nonlinear relationship between the minute-level fluctuation rate of new energy power and the actual output; each exponential term in the double exponential function can also be assigned a clear physical meaning; it allows adjustment of fitting parameters to adapt to the data characteristics under different scenarios; and the interval estimation method based on double exponential function fitting is computationally simple and efficient, suitable for large-scale real-time applications.

[0064] It is understandable that after executing step S100, the specific execution steps can be as follows:

[0065] Based on the historical wind power output and the historical photovoltaic power output, the wavelet basis function for analyzing the new energy power signal is determined, wherein the wavelet basis function is a function corresponding to the wavelet decomposition strategy;

[0066] The number of decomposition layers for the historical wind power output and the historical photovoltaic power output is determined, and based on the wavelet basis function, the historical wind power output and the historical photovoltaic power output are decomposed layer by layer with reference to the number of decomposition layers to obtain the wind power decomposition result and photovoltaic power decomposition result corresponding to each layer;

[0067] Based on the wind power decomposition results and the photovoltaic power decomposition results, minute-level fluctuation component extraction processing is performed to obtain the corresponding wind power output fluctuation and the photovoltaic power output fluctuation, respectively.

[0068] Furthermore, we first analyzed the historical wind power output and historical photovoltaic power output signals, observing their periodicity, volatility, and abrupt changes. For example, wind power output may exhibit daily periodic fluctuations, while also showing random fluctuations due to changes in wind speed; photovoltaic power output fluctuates significantly during the day and is affected by factors such as weather and time.

[0069] Next, select a suitable wavelet basis function category: Based on the characteristics of the power signal, initially screen from common wavelet basis function categories. For example, for signals with smooth and strong periodicity, db4 or db5 from the Daubechies wavelet family can be selected; for signals containing many abrupt changes, Haar wavelets or Coiflet wavelets can be selected.

[0070] Experiments and comparisons were conducted: different wavelet basis functions were used to decompose a subset of sample data. By calculating the energy distribution and reconstruction error of each component after decomposition, the adaptability of different wavelet basis functions to new energy power signals was evaluated. The wavelet basis function that accurately reflects the signal characteristics and gives each component after decomposition a clear physical meaning was selected as the final analytical wavelet basis function.

[0071] In some embodiments, historical wind power output and historical photovoltaic power output are input, and a wavelet basis function suitable for analyzing new energy power signals is selected.

[0072] The formula for the wavelet basis function is:

[0073]

[0074] in, denoted as wavelet basis functions, which are the basic building blocks of wavelet decomposition, similar to the sine and cosine functions in Fourier transform, but with better localization properties.

[0075] This represents the original input signal (historical wind power output and historical photovoltaic power output). This represents the subwavelet function after scaling and translation operations.

[0076]

[0077] Where a is the scaling 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).

[0078] The choice of wavelet basis functions directly affects the results of subsequent wavelet decomposition. Appropriate wavelet basis functions can more accurately capture the characteristics of different frequency bands in the renewable energy power signal, making the decomposed components more reflective of the actual fluctuations in wind and solar power output, thus providing a good foundation for subsequent analysis and processing.

[0079] In some embodiments, the method for determining the number of decomposition layers can be varied. One method is based on empirical formulas, determining an approximate range of decomposition layers according to the signal length and sampling frequency. For example, for a signal of length N, the number of decomposition layers J can be initially estimated as J = log2(N). Then, within this range, the optimal number of layers is determined by experimentation and comparison of the decomposition effects at different numbers. Another method is to determine the number of decomposition layers based on the change in information entropy. As the number of decomposition layers increases, the information entropy of each component is calculated; when the change in information entropy tends to level off, a suitable number of decomposition layers can be considered reached.

[0080] Decomposition based on wavelet basis functions: Using selected wavelet basis functions and a determined number of decomposition levels, historical wind power output and historical photovoltaic power output are decomposed layer by layer. The specific decomposition process is implemented using a wavelet transform algorithm. Taking the Mallat algorithm as an example, it decomposes the signal into low-frequency approximate components (reflecting the general trend of the signal) and high-frequency detail components (reflecting local fluctuations in the signal). In each decomposition level, the low-frequency approximate components of the previous level are further decomposed into low-frequency approximate components and high-frequency detail components of the next level, until the predetermined number of decomposition levels is reached. This yields the wind power decomposition results (including low-frequency and high-frequency components) and photovoltaic power decomposition results corresponding to each level.

[0081] Determining a reasonable number of decomposition levels allows for the breakdown of new energy power signals into components at different frequency scales, each representing fluctuation characteristics at different time scales. By decomposing wind and solar power output layer by layer, a more comprehensive analysis of their fluctuation patterns can be achieved. For example, low-frequency components can reflect long-term trends and periodic changes in power output, while high-frequency components can reflect short-term fluctuations and abrupt changes, providing the basis for accurately extracting minute-level fluctuation components.

[0082] Furthermore, to extract minute-level fluctuation components, a relatively shallow decomposition level (1-2 levels) is typically chosen, and Discrete Wavelet Transform (DWT) is used to analyze historical wind power output and historical photovoltaic power output. A layer-by-layer decomposition is performed. The decomposition process involves inputting historical wind power output and historical photovoltaic power output. It is divided into low-frequency components (approximate part) ) and high-frequency components (details) The result of each level of decomposition can be represented as:

[0083]

[0084] in, This represents the original input signal (historical wind power output and historical photovoltaic power output). It is the first The low-frequency components of the layer, It is the first High-frequency components of the layer.

[0085] In some embodiments, the specific steps for extracting minute-level fluctuation components based on the decomposition results can be as follows:

[0086] Identifying the frequency range of minute-level fluctuation components: Based on the correspondence between time and frequency and the characteristics of new energy power signals, determine the frequency range corresponding to minute-level fluctuations. For example, minute-level fluctuations usually correspond to higher frequencies and may be reflected in high-frequency components within a certain range.

[0087] Extracting minute-level fluctuation components from the decomposition results: In the obtained wind power and photovoltaic power decomposition results, identify the components corresponding to the minute-level fluctuations within the frequency range. This may require further analysis and filtering of the decomposed components. For example, relevant high-frequency detail components can be extracted as minute-level fluctuation components by setting thresholds or frequency ranges. These extracted components are then reconstructed or combined to obtain the complete wind power output fluctuations and photovoltaic power output fluctuations.

[0088] Furthermore, from the results of the first level decomposition Minute-level fluctuation components are extracted from the data, which reflect the minute-level power fluctuations of new energy sources, thus yielding the corresponding wind power output fluctuations and photovoltaic power output fluctuations.

[0089] By extracting minute-level fluctuation components, we can focus on the rapid changes in the output power of new energy sources within a short period. This is of great significance for studying the short-term power generation characteristics of wind and solar power, assessing their real-time impact on the power grid, and formulating corresponding power dispatch strategies. For example, minute-level fluctuation components can help dispatchers more accurately predict short-term power changes, enabling timely adjustments to the output of other power sources or load arrangements in the power grid, thus ensuring the stable operation of the power system.

[0090] In step S300 of some embodiments, a power fluctuation range estimation model is constructed based on the wind power output fluctuation and the photovoltaic power output fluctuation.

[0091] First, it should be noted that the power fluctuation range estimation model includes: wind power range estimation model and photovoltaic range estimation model.

[0092] The wind power interval estimation model characterizes the wind power mapping relationship between the minute-level fluctuation of wind power and the historical wind power output.

[0093] The photovoltaic interval estimation model characterizes the photovoltaic mapping relationship between the minute-level fluctuation of photovoltaic power and the historical photovoltaic output power.

[0094] It is understandable that after completing step S200, the specific execution steps can be: defining and processing the wind power output fluctuation and the photovoltaic power output fluctuation based on the smoothing effect strategy, and determining the corresponding wind power minute-level fluctuation rate and photovoltaic power minute-level fluctuation rate;

[0095] A scatter plot of wind power is constructed based on the minute-level volatility of wind power and the per-unit value of wind power output, and a scatter plot of photovoltaic power is constructed based on the minute-level volatility of photovoltaic power and the per-unit value of photovoltaic output.

[0096] The outer envelope of the wind power scatter plot is fitted with a double exponential function to determine the wind power interval estimation model, and the outer envelope of the photovoltaic scatter plot is fitted with a double exponential function to determine the photovoltaic interval estimation model.

[0097] It should be noted that the smoothing effect strategy for wind power output fluctuations and photovoltaic power output fluctuations is primarily aimed at eliminating random noise and abnormal fluctuations in the data, highlighting the main trends and periodic patterns. This helps to more accurately define the minute-level fluctuation rates of wind power and photovoltaic power, providing a more reliable data foundation for subsequent model construction.

[0098] The steps for processing wind power output fluctuations and photovoltaic power output fluctuations based on the smoothing effect strategy can specifically include:

[0099] Moving average method: For example, for wind power output time series data, a certain time window (e.g., 5 minutes) can be selected, and the average value of the data within each window can be calculated. These average values ​​are then used to construct a new data series. This yields a smoothed wind power output curve, making its fluctuations more stable and facilitating the analysis of its long-term trends and periodic changes. The same method can be used to process photovoltaic power output data.

[0100] Exponential smoothing: This method assigns higher weight to recent data and lower weight to older data. By adjusting the smoothing coefficient, the data can better reflect the latest trends while retaining some historical information. For example, for wind power output data, an appropriate smoothing coefficient can be selected based on practical experience to perform exponential smoothing, resulting in a smoother curve used to define the minute-level volatility of wind power output.

[0101] Further, the data preparation for constructing the wind power scatter plot involves using the smoothed minute-level fluctuation data of wind power as the horizontal axis and the corresponding per-unit value of wind power output (the ratio of actual output power to rated power) as the vertical axis, thus forming a set of data points. For example, if the minute-level fluctuation rate of wind power is calculated to be 0.05 (assuming units) within a certain minute, and the corresponding per-unit value of wind power output is 0.8, then a point with coordinates (0.05, 0.8) can be found in the scatter plot.

[0102] The purpose of constructing a wind power scatter plot is to visually observe the relationship between the minute-level fluctuation rate of wind power and the per-unit value of wind power output. If there is a significant correlation between the two, the data points may exhibit a specific distribution pattern, such as linear or nonlinear, which provides a basis for subsequent model selection and fitting.

[0103] Data preparation for constructing a photovoltaic scatter plot: Similar to wind power, the processed minute-level fluctuation data of photovoltaic power is used as the horizontal axis, and the per-unit value of photovoltaic output is used as the vertical axis to construct the data points of the photovoltaic scatter plot. For example, when the minute-level fluctuation rate of photovoltaic power is 0.03 (assuming unit), and the corresponding per-unit value of photovoltaic output is 0.7, a point with coordinates (0.03, 0.7) will appear in the scatter plot.

[0104] The purpose of constructing a photovoltaic scatter plot: Similarly, a photovoltaic scatter plot can help us analyze the mapping relationship between the minute-level fluctuation of photovoltaic power and the per-unit value of photovoltaic output. By observing the distribution pattern of the scatter plot, we can preliminarily determine the type of relationship between the two, such as whether there is a linear relationship, an exponential relationship, etc., thus providing a reference for determining the form of the photovoltaic interval estimation model.

[0105] Furthermore, the steps for determining the wind power range estimation model include:

[0106] Choosing the double exponential function as the fitting function, its formula is:

[0107] in, For wind power power fluctuations on a minute-by-minute scale, , , , These are the minute-level fluctuations of wind power and the fitting parameters between wind power output, respectively. These are the per-unit values ​​for wind power output and thermal power installed capacity. This function can adapt well to various data trends and has a strong fitting ability.

[0108] The specific fitting process can be as follows: using professional data analysis software or programming languages ​​(such as the SciPy library in Python), the constructed wind power scatter plot data is input into the algorithm, and the parameters are adjusted through optimization methods such as the least squares method. , , , The value of is determined so that the fitting function approximates the data points in the scatter plot as closely as possible. For example, by continuously adjusting the parameters, the sum of squared errors between the fitted curve and the actual data points is minimized, thus obtaining the optimal fitting effect. The final determined double exponential function is the wind power interval estimation model, which can characterize the mapping relationship between the minute-level fluctuation rate of wind power and the historical wind power output.

[0109] Furthermore, the steps for determining the photovoltaic interval estimation model include:

[0110] Similarly, a double exponential function is used as the fitting function, and its formula is:

[0111]

[0112] in, This refers to the minute-level fluctuation rate of photovoltaic power. , , , These are the fitting parameters between the minute-level volatility of photovoltaic power and the photovoltaic power output, respectively. This represents the per-unit value for photovoltaic power output and thermal power installed capacity. This function can, to some extent, reflect the complex variation of photovoltaic power output under the influence of various factors such as sunlight intensity.

[0113] The specific fitting process can be as follows: Substitute the photovoltaic scatter plot data into the selected double exponential function, and use a suitable optimization algorithm (such as gradient descent) to estimate the parameters. During the fitting process, the daily periodicity of the photovoltaic data needs to be considered; that is, factors such as daily solar intensity and temperature exhibit similar changing patterns. Therefore, appropriate periodic constraints can be added during the fitting process to improve the model's fitting accuracy. Through continuous iterative optimization, a photovoltaic interval estimation model that accurately reflects the relationship between the minute-level fluctuation rate of photovoltaic power and historical photovoltaic output power is obtained.

[0114] Furthermore, in some embodiments of the present invention, after the step of constructing a wind power scatter plot based on the minute-level fluctuation rate of wind power and the per-unit value of wind power output, the method includes:

[0115] Calculate the mean and standard deviation of the wind power fluctuation rate in minutes;

[0116] Calculate the wind power deviation between each data point in the wind power scatter plot and the wind power mean, and compare the wind power deviation with 3 times the wind power standard deviation.

[0117] Data points whose wind power deviation is greater than or equal to 3 times the wind power standard deviation are removed from the wind power scatter plot;

[0118] After the step of constructing a photovoltaic scatter plot based on the photovoltaic power minute-level volatility and the photovoltaic output per-unit value, the method includes:

[0119] Calculate the photovoltaic mean and standard deviation of the minute-level fluctuation of the photovoltaic power;

[0120] Calculate the photovoltaic deviation between each data point in the photovoltaic scatter plot and the photovoltaic mean, and compare the photovoltaic deviation with 3 times the photovoltaic standard deviation.

[0121] Data points whose photovoltaic deviation is greater than or equal to 3 times the photovoltaic standard deviation are removed from the photovoltaic scatter plot.

[0122] Understandably, the steps for calculating the mean and standard deviation of wind power fluctuations in minute-level ranges are as follows:

[0123] The wind power mean is calculated by summing all the minute-level fluctuation data of wind power and then dividing by the total number of data points. For example, if there are 100 data points of minute-level fluctuation of wind power, the sum is divided by 100 to obtain the wind power mean.

[0124] The standard deviation of wind power is used to measure the dispersion of these data points relative to the mean. The calculation involves first finding the square of the difference between each data point and the mean, then summing these squares, dividing by the total number of data points (for sample data, some statistical methods divide by the total number minus 1), and finally taking the square root of this result to obtain the standard deviation of wind power.

[0125] It's important to note that the wind power mean reflects the average level of wind power fluctuations at the minute level, providing a benchmark for assessing data point deviations. The wind power standard deviation, on the other hand, indicates the degree of data volatility; a larger standard deviation indicates greater data dispersion. These two statistics help us understand the overall characteristics of wind power fluctuation data at the minute level and form the basis for subsequent deviation analysis and outlier removal.

[0126] The steps for 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, include:

[0127] For each data point in the wind power scatter plot (assuming its corresponding minute-level wind power fluctuation rate is x), calculate its relationship with the wind power mean (let's say x). The difference between () and (), i.e., wind power deviation Then, this wind power deviation is compared with 3 times the wind power standard deviation (let the wind power standard deviation be 1). Then 3 times the standard deviation of wind power is 3 (Compare)

[0128] Based on the comparison results, 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 is removed from the wind power scatter plot.

[0129] By calculating the wind power deviation, the magnitude of the difference between each data point and the mean can be clearly determined. Comparing the wind power deviation to three times the standard deviation of wind power is based on the statistical principle of "3..." The principle states that in a normal distribution, data points that deviate more than three standard deviations from the mean have a very low probability of occurring. This comparison can help identify data points that deviate significantly from the majority of the data, i.e., potential outliers.

[0130] Outliers are removed to improve the accuracy and reliability of subsequent wind power range estimation models. Outliers can significantly interfere with model fitting, causing the model to fail to accurately reflect the true relationship between minute-level fluctuations in wind power and per-unit wind power output. By removing these outliers, the remaining data can better conform to normal distribution patterns, allowing the constructed wind power range estimation model to better reflect the overall characteristics and trends of the data.

[0131] In some embodiments, the steps of calculating the photovoltaic mean and standard deviation of the minute-level volatility of photovoltaic power include: the calculation method for the photovoltaic mean is similar to that for the wind power mean, that is, adding up all the data points 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 minute-level volatility of photovoltaic power, the photovoltaic mean is obtained by summing them and dividing by 200.

[0132] The calculation of the standard deviation of photovoltaic data also involves first finding the square of the difference between each data point and the mean, then adding these squared values ​​together, dividing by the total number of data points (for sample data, some statistical methods divide by the total number minus 1), and finally taking the square root.

[0133] The photovoltaic mean reflects the average level of the minute-level fluctuation of photovoltaic power, providing a benchmark for subsequent assessment of data point deviations. The photovoltaic standard deviation indicates the degree of data volatility; a larger standard deviation indicates greater data dispersion. These two statistics help understand the overall characteristics of the minute-level fluctuation data of photovoltaic power and form the basis for subsequent deviation analysis and outlier removal.

[0134] The steps for calculating the photovoltaic deviation between each data point in the photovoltaic scatter plot and the photovoltaic mean, and comparing the photovoltaic deviation with three times the photovoltaic standard deviation, include:

[0135] For each data point in the photovoltaic scatter plot (assuming its corresponding photovoltaic power minute-level fluctuation rate is y), calculate its relationship with the photovoltaic mean (let's say y). The difference between () and (), i.e., photovoltaic bias .

[0136] Then this photovoltaic deviation is compared with 3 times the photovoltaic standard deviation (let the photovoltaic standard deviation be 1). Then 3 times the standard deviation of photovoltaic is 3 (Compare)

[0137] Based on the comparison results of the photovoltaic deviations mentioned above, 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 is removed from the photovoltaic scatter plot.

[0138] By calculating the photovoltaic deviation, the magnitude of the difference between each data point and the mean can be clearly identified. Comparing the photovoltaic deviation to three times the photovoltaic standard deviation is based on the statistical "3σ principle." In a normal distribution, the probability of a data point being more than three times the standard deviation from the mean is very low. This comparison can help identify data points that deviate significantly from the majority of the data, i.e., potential outliers.

[0139] Outliers are removed to improve the accuracy and reliability of the subsequent photovoltaic (PV) interval estimation model. Outliers can significantly interfere with the model's fit, causing it to fail to accurately reflect the true relationship between minute-level fluctuations in PV power and per-unit PV output. By removing these outliers, the remaining data can better conform to normal distribution patterns, making the constructed PV interval estimation model more reflective of the overall characteristics and trends of the data.

[0140] In step S400 of some embodiments, the maximum estimated value of power fluctuation is determined based on the power fluctuation range estimation model and the actual output data of the power system.

[0141] It should be noted that the actual output data includes at least: actual wind power output and actual photovoltaic power output.

[0142] The maximum power fluctuation estimate includes at least: the maximum power fluctuation estimate for wind power at the minute level and the maximum power fluctuation estimate for photovoltaic power at the minute level.

[0143] It is understandable that after executing 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 wind power output of the power system, determine the maximum estimated value of wind power minute-level power fluctuation;

[0144] Based on the photovoltaic mapping function corresponding to the photovoltaic mapping relationship and the actual photovoltaic output power of the power system, the maximum estimated value of photovoltaic power fluctuation at the minute level is determined.

[0145] Furthermore, the actual output power of wind power is measured by sensors and other equipment installed on the wind turbine generator. These sensors can monitor information such as the wind turbine's rotational speed, blade angle, and wind speed in real time, and then calculate the actual output power based on this information. The actual output power reflects the instantaneous value of electrical energy delivered to the grid by the wind power generation system during actual operation. It is one of the key input data for constructing a wind power range estimation model, used to determine the maximum estimated value of wind power fluctuations. By analyzing the changes in actual wind power output power over time, the fluctuation characteristics of wind power can be understood, providing basic data support for subsequent power fluctuation range estimation.

[0146] The actual output power of photovoltaics (PV) systems is primarily achieved by converting solar energy into electrical energy through photovoltaic (PV) panels. This direct current (DC) is then converted to alternating current (AC) by an inverter. During this conversion process, relevant electrical parameters, such as current and voltage, are acquired. These parameters, along with factors like the PV array area, irradiance, and temperature correction factor, are used to calculate the actual output power using power calculation formulas. For example, based on parameters such as the short-circuit current, open-circuit voltage, and maximum power point current and voltage of the PV panels, combined with the current irradiance and ambient temperature, a specific algorithm can be used to calculate the actual output power of the PV system.

[0147] Actual photovoltaic (PV) output power reflects the actual power generation capacity of a PV system under different environmental conditions. It is also crucial input data for constructing PV range estimation models, used to determine the maximum estimated value of PV power fluctuations. Analyzing the variation patterns of actual PV output power helps to understand the characteristics of PV output, thus providing a basis for accurately estimating the range of PV power fluctuations.

[0148] In some embodiments, the maximum estimated value of wind power fluctuations at the minute level is determined based on the wind power mapping function corresponding to the wind power mapping relationship and the actual wind power output of the power system. First, it is necessary to establish a wind power mapping function that reflects the mapping relationship between the actual wind power output and wind power fluctuations.

[0149] In some embodiments, the mapping can also be obtained by analyzing and fitting a large amount of historical wind power output data and corresponding power fluctuation data. For example, linear regression, neural networks, and other methods can be used to establish this mapping relationship. Then, the actual wind power output obtained from real-time monitoring is substituted into the wind power mapping function to calculate the maximum estimated value of wind power power fluctuation at the minute level.

[0150] The maximum minute-level power fluctuation estimate of wind power provides a quantitative indicator for assessing the uncertainty of wind power output. Understanding the fluctuation range of wind power is crucial for ensuring the safe and stable operation of the power system during operation and dispatch. Accurately estimating the maximum minute-level power fluctuation of wind power helps dispatchers formulate corresponding dispatch strategies in advance, such as adjusting the output of conventional energy units and arranging the charging and discharging schedules of energy storage systems, to cope with wind power output fluctuations and ensure the supply-demand balance and power quality of the power system.

[0151] In some embodiments, the maximum estimated value of photovoltaic power fluctuations at the minute level 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 the photovoltaic mapping function, which describes the mapping relationship between the actual photovoltaic output power and the photovoltaic power fluctuations.

[0152] In some embodiments, this mapping function can also be obtained by analyzing and processing historical photovoltaic power output data and power fluctuation data. For example, considering the influence of factors such as irradiance and temperature on photovoltaic power output, a nonlinear mapping model can be established using machine learning algorithms. Then, the currently measured actual photovoltaic power output is input into the photovoltaic mapping function to calculate the maximum estimated value of the photovoltaic power fluctuation at the minute level.

[0153] The maximum minute-level power fluctuation estimate of photovoltaic (PV) power generation helps assess the reliability and stability of PV power generation. In power systems, the proportion of PV power generation is gradually increasing, and its output fluctuations have a more significant impact on the power grid. Accurately estimating the maximum minute-level power fluctuation of PV can provide important reference for grid dispatching, enabling dispatchers to rationally allocate PV output and optimize grid operation. For example, when PV output fluctuations are large, reliance on PV can be appropriately reduced, while increasing the output of other stable power sources, or energy storage systems can be used to smooth PV output fluctuations, thereby improving the overall operating performance of the power grid.

[0154] Figure 2 This is the second flowchart of a method for estimating the maximum wind and solar power fluctuation range based on wavelet decomposition provided by this invention. The method involves obtaining the historical wind power output and historical solar power output of the power system; selecting wavelet basis functions and determining the number of decomposition levels; then decomposing the historical wind power output and historical solar power output to determine the wind power output fluctuation corresponding to the historical wind power output and the solar power output fluctuation corresponding to the historical solar power output.

[0155] Based on the smoothing effect, wind power output fluctuations and photovoltaic power output fluctuations are defined, and the corresponding minute-level fluctuation rates of wind power and photovoltaic power are determined. A double exponential function fitting method is used to determine the interval estimation models for wind power and photovoltaic power.

[0156] Finally, based on the wind power interval estimation model and the actual wind power output of the power system, the maximum estimated value of wind power power fluctuation at the minute level is determined. Similarly, based on the photovoltaic interval estimation model and the actual photovoltaic power output of the power system, the maximum estimated value of photovoltaic power fluctuation at the minute level is determined.

[0157] By using the maximum minute-level power fluctuation estimates for wind power and photovoltaic power, the output power of wind power and photovoltaic power can be updated. This allows for a simple and efficient estimation of the maximum wind and solar power fluctuation results.

[0158] In some embodiments, the proportion of wind and solar power minute-level fluctuation components in the output will decrease with the smoothing effect of wind and solar power. In order to analyze the proportion of wind and solar power minute-level fluctuation components in wind and solar power output separately, the wind and solar power minute-level fluctuation rates are defined as follows:

[0159]

[0160] in, For wind power power fluctuations on a minute-by-minute scale, For the minute-level fluctuation component of wind power in the first The amplitude of min. For the first min wind power; This refers to the minute-level fluctuation rate of photovoltaic power. For the minute-level fluctuation component of photovoltaics in the first... The amplitude of min. For the first The photovoltaic power of min.

[0161] By statistically analyzing historical wind power data amplitudes, a scatter plot of wind power fluctuation rate versus power output was obtained, and based on 3 Outliers in the minute-level volatility of wind and solar power are removed as a principle. For wind power, the relationship between the minute-level volatility of wind power and the per-unit value of wind power output can be obtained. To estimate the maximum value of the minute-level volatility of wind power, its outer envelope can be fitted using a double exponential function. Therefore, the relationship between the maximum wind power minute-level volatility and wind power is:

[0162]

[0163] in, For wind power power fluctuations on a minute-by-minute scale, , , , These are the minute-level fluctuations of maximum wind power and the fitting parameters between wind power, respectively. These are the per-unit values ​​for wind power output and thermal power installed capacity. Similarly, the relationship between the maximum minute-level fluctuation power of photovoltaic power and the minute-level fluctuation rate of photovoltaic power can be obtained as follows:

[0164]

[0165] in, This refers to the minute-level fluctuation rate of photovoltaic power. , , , These are the fitting parameters between the maximum minute-level fluctuation of photovoltaic power and the photovoltaic power, respectively. These are per-unit values ​​for photovoltaic power output and thermal power installed capacity.

[0166] It should be noted that the final formula for calculating the maximum estimate of wind power fluctuations at the minute level is:

[0167]

[0168] in, This represents the maximum estimated value for minute-level power fluctuations in wind power. For the first min wind power , , , These are the minute-level fluctuations of maximum wind power and the fitting parameters between wind power, respectively. These are per-unit values ​​for wind power output and thermal power installed capacity.

[0169] The final formula for calculating the maximum estimate of photovoltaic power fluctuations in minutes can be:

[0170]

[0171] in, This represents the maximum estimated power fluctuation in photovoltaic systems at the minute level. , , , These are the fitting parameters between the maximum minute-level fluctuation of photovoltaic power and the photovoltaic power, respectively. For the first The minimum photovoltaic power, These are per-unit values ​​for photovoltaic power output and thermal power installed capacity.

[0172] This invention provides a method, apparatus, device, and storage medium for estimating the maximum wind and solar power fluctuation range based on wavelet decomposition. The method involves acquiring historical wind power output and historical solar power output of a power system; decomposing these historical wind power output and solar power output respectively using a wavelet decomposition strategy to determine the wind power output fluctuation corresponding to the historical wind power output and the solar power output fluctuation corresponding to the historical solar power output; constructing a power fluctuation range estimation model based on the wind power output fluctuation and solar power output fluctuation; and determining the maximum estimated value of the power fluctuation based on the power fluctuation range estimation model and the actual power output data of the power system. This addresses the shortcomings of existing technologies, which suffer from large estimation errors and low accuracy in estimating the maximum wind and solar power fluctuation range, and achieves a simple and efficient estimation of the maximum wind and solar power fluctuation result.

[0173] The maximum wind and solar power fluctuation range estimation device based on wavelet decomposition provided by the present invention is described below. The maximum wind and solar power fluctuation range estimation device based on wavelet decomposition described below can be referred to in correspondence with the maximum wind and solar power fluctuation range estimation method based on wavelet decomposition described above.

[0174] like Figure 3 The diagram shown is a schematic representation of a maximum wind and solar power fluctuation range estimation device based on wavelet decomposition provided by the present invention. The device includes the following modules:

[0175] The acquisition module 310 is used to acquire the historical wind power output and historical photovoltaic power output of the power system;

[0176] The output power fluctuation determination module 320 is used to decompose the historical wind power output power and the historical photovoltaic power output power based on the wavelet decomposition strategy, and determine the wind power output power fluctuation corresponding to the historical wind power output power, and determine the photovoltaic power output power fluctuation corresponding to the historical photovoltaic power output power.

[0177] Module 330 is used to construct a power fluctuation range estimation model based on the wind power output fluctuation and the photovoltaic power output fluctuation;

[0178] The maximum estimate module 340 is used to determine the maximum estimate of power fluctuation based on the power fluctuation range estimation model and the actual output data of the power system.

[0179] Preferably, the maximum wind and solar power fluctuation range estimation device based on wavelet decomposition provided by the present invention is further used 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, wherein the wavelet basis function is a function corresponding to the wavelet decomposition strategy;

[0180] The number of decomposition layers for the historical wind power output and the historical photovoltaic power output is determined, and based on the wavelet basis function, the historical wind power output and the historical photovoltaic power output are decomposed layer by layer with reference to the number of decomposition layers to obtain the wind power decomposition result and photovoltaic power decomposition result corresponding to each layer;

[0181] Based on the wind power decomposition results and the photovoltaic power decomposition results, minute-level fluctuation component extraction processing is performed to obtain the corresponding wind power output fluctuation and the photovoltaic power output fluctuation, respectively.

[0182] Preferably, the maximum wind and solar power fluctuation range estimation device based on wavelet decomposition provided by the present invention is further used in the power fluctuation range estimation model, which includes: a wind power range estimation model and a photovoltaic range estimation model.

[0183] Based on the smoothing effect strategy, the fluctuations in wind power output and photovoltaic power output are defined and processed to determine the corresponding minute-level fluctuation rates of wind power output and photovoltaic power output.

[0184] A scatter plot of wind power is constructed based on the minute-level volatility of wind power and the per-unit value of wind power output, and a scatter plot of photovoltaic power is constructed based on the minute-level volatility of photovoltaic power and the per-unit value of photovoltaic output.

[0185] The outer envelope of the wind power scatter plot is fitted with a double exponential function to determine the wind power interval estimation model, and the outer envelope of the photovoltaic scatter plot is fitted with a double exponential function to determine the photovoltaic interval estimation model.

[0186] Preferably, the maximum wind and solar power fluctuation range estimation device based on wavelet decomposition provided by the present invention is further used to characterize the wind power range estimation model as a wind power mapping relationship between the minute-level fluctuation rate of wind power and the historical wind power output.

[0187] The photovoltaic interval estimation model characterizes the photovoltaic mapping relationship between the minute-level fluctuation of photovoltaic power and the historical photovoltaic output power.

[0188] Preferably, the maximum wind and solar power fluctuation range estimation device based on wavelet decomposition provided by the present invention is further used to include, in particular, the actual power output data including at least: actual wind power output and actual photovoltaic power output.

[0189] The maximum power fluctuation estimate includes at least: the maximum power fluctuation estimate for wind power at the minute level and the maximum power fluctuation estimate for photovoltaic power at the minute level;

[0190] Based on the wind power mapping function corresponding to the wind power mapping relationship and the actual wind power output of the power system, the maximum estimated value of wind power minute-level power fluctuation is determined.

[0191] Based on the photovoltaic mapping function corresponding to the photovoltaic mapping relationship and the actual photovoltaic output power of the power system, the maximum estimated value of photovoltaic power fluctuation at the minute level is determined.

[0192] Preferably, the maximum wind and solar power fluctuation range estimation device based on wavelet decomposition provided by the present invention is further used to calculate the wind power mean and wind power standard deviation of the minute-level fluctuation rate of the wind power.

[0193] Calculate the wind power deviation between each data point in the wind power scatter plot and the wind power mean, and compare the wind power deviation with 3 times the wind power standard deviation.

[0194] Data points whose wind power deviation is greater than or equal to 3 times the wind power standard deviation are removed from the wind power scatter plot.

[0195] Preferably, the maximum wind and solar power fluctuation range estimation device based on wavelet decomposition provided by the present invention is further used to calculate the photovoltaic mean and photovoltaic standard deviation of the minute-level fluctuation rate of the photovoltaic power;

[0196] Calculate the photovoltaic deviation between each data point in the photovoltaic scatter plot and the photovoltaic mean, and compare the photovoltaic deviation with 3 times the photovoltaic standard deviation.

[0197] Data points whose photovoltaic deviation is greater than or equal to 3 times the photovoltaic standard deviation are removed from the photovoltaic scatter plot.

[0198] This invention provides a method, apparatus, device, and storage medium for estimating the maximum wind and solar power fluctuation range based on wavelet decomposition. The method involves acquiring historical wind power output and historical solar power output of a power system; decomposing these historical wind power output and solar power output respectively using a wavelet decomposition strategy to determine the wind power output fluctuation corresponding to the historical wind power output and the solar power output fluctuation corresponding to the historical solar power output; constructing a power fluctuation range estimation model based on the wind power output fluctuation and solar power output fluctuation; and determining the maximum estimated value of the power fluctuation based on the power fluctuation range estimation model and the actual power output data of the power system. This addresses the shortcomings of existing technologies, which suffer from large estimation errors and low accuracy in estimating the maximum wind and solar power fluctuation range, and achieves a simple and efficient estimation of the maximum wind and solar power fluctuation result.

[0199] Figure 4 An example is 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 communication interface 420, a memory 430, and a communication bus 440, wherein the processor 410, the communication interface 420, and the memory 430 communicate with each other through the communication bus 440. The processor 410 can call logical instructions in the memory 430 to execute a method for estimating the maximum wind and solar power fluctuation range based on wavelet decomposition. This method includes: obtaining the historical wind power output and historical photovoltaic power output of the power system; decomposing the historical wind power output and the historical photovoltaic power output respectively 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 range 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 range estimation model and the actual power output data of the power system.

[0200] Furthermore, the logical instructions in the aforementioned memory 430 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0201] 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 maximum wind and solar power fluctuation range estimation method based on wavelet decomposition provided by the above methods. The method includes: obtaining the historical wind power output and historical photovoltaic power output of the power system; decomposing the historical wind power output and the historical photovoltaic power output respectively 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 range 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 range estimation model and the actual power output data of the power system.

[0202] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements the wavelet decomposition-based maximum wind and solar power fluctuation interval estimation method provided by the above methods. This method includes: acquiring historical wind power output and historical photovoltaic power output of a power system; decomposing the historical wind power output and the historical photovoltaic power output respectively 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 power output data of the power system.

[0203] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0204] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence 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 cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0205] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to 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 solar power fluctuation range based on wavelet decomposition, characterized in that, include: Obtain historical wind power output and historical photovoltaic power output of the power system; Based on the wavelet decomposition strategy, the historical wind power output and the historical photovoltaic power output are decomposed respectively 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. Based on the power output fluctuations of wind power and photovoltaic power, a power fluctuation range estimation model is constructed; the power fluctuation range estimation model includes: a wind power range estimation model and a photovoltaic range estimation model. The method for constructing a power fluctuation range estimation model based on the wind power output fluctuation and the photovoltaic power output fluctuation includes: The smoothing effect strategy defines and processes the fluctuations in wind power output and photovoltaic power output to determine the corresponding minute-level fluctuation rates of wind power and photovoltaic power. The smoothing effect strategy is characterized by using a moving average method and an exponential smoothing method for smoothing. The moving average method is used to calculate the average value of wind power output data based on a time window to obtain a smoothed wind power output curve, and to calculate the average value of photovoltaic power output data based on a time window to obtain a smoothed photovoltaic power output curve. The exponential smoothing method is used to select a smoothing coefficient to exponentially smooth the wind power output data to obtain minute-level fluctuation rate data, and to select a smoothing coefficient to exponentially smooth the photovoltaic power output data to obtain minute-level fluctuation rate data. A scatter plot of wind power is constructed based on the minute-level volatility of wind power and the per-unit value of wind power output, and a scatter plot of photovoltaic power is constructed based on the minute-level volatility of photovoltaic power and the per-unit value of photovoltaic output. The wind power interval estimation model is determined by fitting the outer envelope of the wind power scatter plot with a double exponential function, and the photovoltaic interval estimation model is determined by fitting the outer envelope of the photovoltaic scatter plot with a double exponential function. Based on the power fluctuation range estimation model and the actual power output data of the power system, the maximum estimated value of power fluctuation is determined.

2. The method for estimating the maximum wind and solar power fluctuation range based on wavelet decomposition according to claim 1, characterized in that, The wavelet decomposition strategy is used to decompose the historical wind power output and the historical photovoltaic power output, respectively, 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, including: Based on the historical wind power output and the historical photovoltaic power output, the wavelet basis function for analyzing the new energy power signal is determined, wherein the wavelet basis function is a function corresponding to the wavelet decomposition strategy; The number of decomposition layers for the historical wind power output and the historical photovoltaic power output is determined, and based on the wavelet basis function, the historical wind power output and the historical photovoltaic power output are decomposed layer by layer with reference to the number of decomposition layers to obtain the wind power decomposition result and photovoltaic power decomposition result corresponding to each layer; Based on the wind power decomposition results and the photovoltaic power decomposition results, minute-level fluctuation component extraction processing is performed to obtain the corresponding wind power output fluctuation and the photovoltaic power output fluctuation, respectively.

3. The method for estimating the maximum wind and solar power fluctuation range based on wavelet decomposition according to claim 1, characterized in that, The method includes: The wind power interval estimation model characterizes the wind power mapping relationship between the minute-level fluctuation rate of wind power and the historical wind power output. The photovoltaic interval estimation model characterizes the photovoltaic mapping relationship between the minute-level fluctuation of photovoltaic power and the historical photovoltaic output power.

4. The method for estimating the maximum wind and solar power fluctuation range based on wavelet decomposition according to claim 3, characterized in that, The actual output data includes at least: actual wind power output and actual photovoltaic power output; The maximum power fluctuation estimate includes at least: the maximum power fluctuation estimate for wind power at the minute level and the maximum power fluctuation estimate for photovoltaic power at the minute level; The determination of the maximum estimated value of power fluctuation based on the power fluctuation range estimation model and the actual power 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, the maximum estimated value of wind power 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, the maximum estimated value of photovoltaic power fluctuation at the minute level is determined.

5. The method for estimating the maximum wind and solar power fluctuation range based on wavelet decomposition according to claim 4, characterized in that, After the step of constructing a wind power scatter plot based on the minute-level fluctuation rate of wind power and the per-unit value of wind power output, the method includes: Calculate the mean and standard deviation of the wind power fluctuation rate in minutes; Calculate the wind power deviation between each data point in the wind power scatter plot and the wind power mean, and compare the wind power deviation with 3 times the wind power standard deviation. Data points whose wind power deviation is greater than or equal to 3 times the wind power standard deviation are removed from the wind power scatter plot; After the step of constructing a photovoltaic scatter plot based on the photovoltaic power minute-level volatility and the photovoltaic output per-unit value, the method includes: Calculate the photovoltaic mean and standard deviation of the minute-level fluctuation of the photovoltaic power; Calculate the photovoltaic deviation between each data point in the photovoltaic scatter plot and the photovoltaic mean, and compare the photovoltaic deviation with 3 times the photovoltaic standard deviation. Data points whose photovoltaic deviation is greater than or equal to 3 times the photovoltaic standard deviation are removed from the photovoltaic scatter plot.

6. A device for estimating the maximum wind and solar power fluctuation range based on wavelet decomposition, characterized in that, include: The acquisition module is used to acquire the historical wind power output and historical photovoltaic power output of the power system; The output power fluctuation determination module is used to decompose the historical wind power output power and the historical photovoltaic power output power based on the wavelet decomposition strategy, and determine the wind power output power fluctuation corresponding to the historical wind power output power, and determine the photovoltaic power output power fluctuation corresponding to the historical photovoltaic power output power. A construction module is used to construct a power fluctuation range estimation model based on the power fluctuation of wind power output and the power fluctuation of photovoltaic power output; the power fluctuation range estimation model includes: a wind power range estimation model and a photovoltaic range estimation model; The method for constructing a power fluctuation range estimation model based on the wind power output fluctuation and the photovoltaic power output fluctuation includes: The smoothing effect strategy defines and processes the fluctuations in wind power output and photovoltaic power output to determine the corresponding minute-level fluctuation rates of wind power and photovoltaic power. The smoothing effect strategy is characterized by using a moving average method and an exponential smoothing method for smoothing. The moving average method is used to calculate the average value of wind power output data based on a time window to obtain a smoothed wind power output curve, and to calculate the average value of photovoltaic power output data based on a time window to obtain a smoothed photovoltaic power output curve. The exponential smoothing method is used to select a smoothing coefficient to exponentially smooth the wind power output data to obtain minute-level fluctuation rate data, and to select a smoothing coefficient to exponentially smooth the photovoltaic power output data to obtain minute-level fluctuation rate data. A scatter plot of wind power is constructed based on the minute-level volatility of wind power and the per-unit value of wind power output, and a scatter plot of photovoltaic power is constructed based on the minute-level volatility of photovoltaic power and the per-unit value of photovoltaic output. The wind power interval estimation model is determined by fitting the outer envelope of the wind power scatter plot with a double exponential function, and the photovoltaic interval estimation model is determined by fitting the outer envelope of the photovoltaic scatter plot with a double exponential function. The maximum estimate determination module is used to determine the maximum estimate of power fluctuation based on the power fluctuation range estimation model and the actual power output data of the power system.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the program, it implements the maximum wind and solar power fluctuation range estimation method based on wavelet decomposition as described in any one of claims 1 to 5.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the maximum wind and solar power fluctuation range estimation method based on wavelet decomposition as described in any one of claims 1 to 5.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the maximum wind and solar power fluctuation range estimation method based on wavelet decomposition as described in any one of claims 1 to 5.

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