Frequency characteristic analysis and modeling method for high-proportion new energy power grid

Abstract

The invention relates to the field of high-proportion new energy power grids, in particular to a frequency characteristic analysis and modeling method for a high-proportion new energy power grid. The method comprises the steps of collecting real-time operation data of a high-proportion new energy power grid, and calculating a frequency change rate through a frequency response characteristic analysis algorithm; and based on the real-time operation data and the frequency change rate, constructing a dynamic frequency prediction model, predicting the frequency of the power grid, and carrying out iterative updating on model parameters to generate a dynamic frequency prediction model suitable for the high-proportion new energy power grid. The problems that in a traditional frequency characteristic analysis and modeling method, the frequency change rate is calculated based on a static or simplified power balance model, and the real-time dynamic behavior of a power grid cannot be accurately reflected are solved; generating power is predicted mostly based on a simple statistical model or short-term data, and long-term fluctuation modes and rapid change characteristics of the generating power are difficult to capture. And parameter adjustment is carried out on a frequency prediction model mostly depending on artificial experience or fixed parameters, so that the prediction precision is influenced.

Description

Technical Field

[0001] The present invention relates to the field of high-proportion new energy power grids, and in particular to a frequency characteristic analysis and modeling method for high-proportion new energy power grids. Background Art

[0002] With the transformation of the global energy structure and the advancement of sustainable development goals, the proportion of renewable energy (such as wind power, solar power, and energy storage) in the power system is rapidly increasing. High-proportion renewable energy power grids have become a major trend in future power system development. However, renewable energy generation is intermittent, fluctuating, and has low inertia, which poses significant challenges to the frequency stability of the power grid. Traditional power systems primarily rely on synchronous generators to provide inertia and frequency regulation capabilities. However, in high-proportion renewable energy power grids, distributed energy resources with a large number of inverter interfaces replace traditional synchronous machines, resulting in a decrease in system inertia and a more complex dynamic frequency response. Therefore, in-depth analysis and modeling of the frequency characteristics of high-proportion renewable energy power grids have become a key technical issue to ensure the safe and stable operation of the power grid.

[0003] Frequency characteristic analysis and modeling of power grids with high penetrations of renewable energy are at the forefront of power system research, involving multidisciplinary cross-disciplinary collaboration and the integration of multiple technologies. Future research must continue to deepen theoretical modeling, control strategies, and practical applications to address the frequency stability challenges posed by high penetrations of renewable energy and provide technical support for building a safe, efficient, and green power system.

[0004] However, traditional frequency characteristic analysis and modeling methods still have problems such as insufficient frequency stability of power grids with a high proportion of renewable energy, challenges posed by low inertia characteristics to the dynamic response of the power grid, and disconnection between renewable energy output prediction and frequency control. Summary of the Invention

[0005] The present invention provides a frequency characteristic analysis and modeling method for a high-proportion renewable energy power grid, so as to solve the technical problems that, in traditional frequency characteristic analysis and modeling methods, the calculation of the frequency change rate is mostly based on a static or simplified power balance model, which fails to fully consider the randomness and volatility of renewable energy output, as well as the influence of low inertia characteristics on frequency dynamics, resulting in the frequency change rate calculation result being unable to accurately reflect the real-time dynamic behavior of the power grid; the prediction of generated power is mostly based on simple statistical models or short-term data, which makes it difficult to capture the long-term fluctuation pattern and rapid change characteristics of generated power, thus limiting the accuracy of the frequency prediction model; and the parameter adjustment of the frequency prediction model mostly relies on manual experience or fixed parameters, lacks an efficient automated optimization method, and is difficult to quickly converge to the optimal parameter combination, thus affecting the prediction accuracy of the frequency prediction model.

[0006] The present invention provides a method for analyzing and modeling the frequency characteristics of a high-proportion new energy power grid, which specifically includes the following technical solutions: A frequency characteristic analysis and modeling method for a high-proportion new energy power grid includes the following steps: S1. Collect real-time operating data of the high-proportion new energy power grid and calculate the frequency change rate through the frequency response characteristic analysis algorithm; S2. Based on the real-time operating data and frequency change rate of the high-proportion renewable energy power grid, a dynamic frequency prediction model is constructed to obtain the predicted grid frequency at a future time point; based on the predicted grid frequency at the future time point, the parameters of the dynamic frequency prediction model are iteratively updated to generate a dynamic frequency prediction model suitable for the high-proportion renewable energy power grid.

[0007] Preferably, the S1 specifically includes: The real-time operating data of the high-proportion new energy power grid includes grid frequency, new energy output, traditional generator set output and load power.

[0008] Preferably, the S1 specifically includes: In the implementation of the frequency response characteristic analysis algorithm, the output of renewable energy and traditional generator sets is added to obtain the total generated power. The power mismatch is calculated by calculating the difference between the total generated power and the load power. Based on the power mismatch, the system equivalent inertia constant and the rated capacity of the grid are introduced to obtain the frequency change rate.

[0009] Preferably, the S2 specifically includes: Based on the frequency change rate and the current grid frequency in the real-time operation data of the high-proportion renewable energy power grid, combined with the adaptive inertia compensation factor, a dynamic frequency prediction model is constructed to predict the frequency dynamic behavior of the high-proportion renewable energy power grid.

[0010] Preferably, the S2 specifically includes: In the process of constructing the dynamic frequency prediction model, by learning the fluctuation pattern of the historical operating data of the high-proportion new energy power grid, the new energy output forecast value, the traditional generator set output forecast value and the load power forecast value are predicted, and the new energy penetration rate and the standardized new energy output change rate are further calculated.

[0011] Preferably, the S2 specifically includes: Based on the new energy penetration rate and the standardized new energy output change rate, an adjustment coefficient is introduced to generate an adaptive inertia compensation factor.

[0012] Preferably, the S2 specifically includes: The frequency change rate is combined with the adaptive inertia compensation factor to calculate the adjusted frequency change rate; the adjusted frequency change rate is time-integrated, starting from the current grid frequency, and the frequency change is accumulated to obtain the predicted grid frequency at a future time point.

[0013] Preferably, the S2 specifically includes: Based on the mean square error between the predicted grid frequency at future time points and the actually measured grid frequency, an objective function is constructed, and the parameters of the dynamic frequency prediction model are iteratively updated through the gradient descent method to generate a dynamic frequency prediction model suitable for power grids with a high proportion of new energy.

[0014] The beneficial effects of the technical solution of the present invention are: 1. Based on the power balance principle, combined with the system equivalent inertia constant and the rated capacity of the power grid, the frequency change rate is calculated to accurately reflect the impact of the randomness and low inertia characteristics of renewable energy output on the grid frequency stability. This provides a key dynamic frequency indicator for grid dispatch and control, and improves the understanding and response capabilities to frequency fluctuations in power grids with a high proportion of renewable energy.

[0015] 2. Utilizing the real-time operating data of a high-proportion renewable energy power grid, the current grid frequency, and the frequency change rate, combined with a machine learning model, the new energy output forecast, the traditional generator output forecast, and the load power forecast are predicted. The frequency change rate is adjusted through an adaptive inertia compensation factor to construct a dynamic frequency prediction model. This significantly improves the prediction accuracy of the grid frequency at future time points, providing a reliable tool for the operation optimization and frequency stability control of a high-proportion renewable energy power grid.

[0016] 3. By iteratively optimizing the adjustment coefficient, system equivalent inertia constant, and prediction time step through the gradient descent method, the deviation between the predicted frequency and the actual frequency is reduced, enabling the dynamic frequency prediction model to adapt to the complex operating characteristics of a high-proportion new energy power grid, and improving the robustness and applicability of the dynamic frequency prediction model in different grid scales and new energy penetration scenarios, providing technical support for the stable operation of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flow chart of the frequency characteristic analysis and modeling method of a high-proportion new energy power grid described in the present invention. DETAILED DESCRIPTION

[0018] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0019] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0020] The following describes in detail a specific scheme of a frequency characteristic analysis and modeling method for a high-proportion new energy power grid provided by the present invention with reference to the accompanying drawings.

[0021] Refer to the attached Figure 1 , which shows a flow chart of a frequency characteristic analysis and modeling method for a high-proportion new energy power grid provided by one embodiment of the present invention, the method comprising the following steps: S1. Collect real-time operating data of the high-proportion new energy power grid and calculate the frequency change rate through the frequency response characteristic analysis algorithm; Real-time operating data of a power grid with a high proportion of renewable energy is collected through high-precision sensors, including grid frequency, renewable energy output, traditional generator output, and load power. The grid frequency is measured in real time by frequency sensors, reflecting the real-time frequency dynamics of the power grid with a high proportion of renewable energy. The renewable energy output represents the output of renewable energy power generation equipment such as wind power and photovoltaic power, reflecting the randomness and volatility of renewable energy power generation. The traditional generator output represents the output of synchronous generators (such as thermal power and hydropower), reflecting the contribution of traditional power generation equipment. The load power reflects the total load demand of the power grid with a high proportion of renewable energy. To capture the dynamic characteristics of frequency fluctuations, the sampling frequency of real-time operating data is recommended to be no less than 1Hz (i.e., data is collected at least once per second) to ensure that the real-time operating data can reflect the transient behavior of rapid fluctuations in renewable energy output and load changes. Based on real-time operating data of power grids with a high proportion of renewable energy, the frequency change rate is calculated using a frequency response characteristic analysis algorithm to capture the impact of renewable energy output fluctuations and low inertia characteristics on grid frequency stability. The frequency response characteristic analysis algorithm is based on power balance. It calculates the difference between the total generated power (including the output of new energy and traditional generator sets) and the load power, combines the system equivalent inertia constant and the rated capacity of the power grid, and outputs the frequency change rate. The frequency change rate is used as the input of the dynamic frequency prediction model. The frequency response characteristic analysis algorithm adds the output of renewable energy and the output of traditional generator sets to obtain the total generated power, which reflects the total contribution of all power generation resources in a high-proportion renewable energy grid, where renewable energy output is random and volatile, while the output of traditional generator sets is relatively stable. The power mismatch is obtained by calculating the difference between the total generated power (including the output of renewable energy and traditional generator sets) and the load power, which is used to represent the imbalance between power generation and load in a high-proportion renewable energy grid. The power mismatch is the main driving force for changes in grid frequency. If the total generated power is greater than the load power, it may cause the grid frequency to rise. If the load power is greater than the total generated power, it may cause the grid frequency to fall. If the two are equal, it means that the power generation and load are in a balanced state and the grid frequency remains stable. Based on the swing equation of the power system, the power mismatch is divided by the product of the system equivalent inertia constant and the rated capacity of the grid to obtain the frequency change rate, which quantifies the dynamic change speed of the grid frequency. The system equivalent inertia constant reflects the inertia level of the grid, that is, the ability of the grid to resist frequency changes. The rated capacity of the grid represents the scale of the grid. The frequency change rate is calculated as: , in, Indicates the frequency change rate, in Hz / s, which is the derivative of the grid frequency with respect to time and is used to quantify the dynamic response of the grid frequency; Indicates that the high proportion of new energy grid in time Load power, unit MW; Indicates time The total power generated is at time New energy output and traditional generator output sum; Indicates the system equivalent inertia constant, which determines the influence of power mismatch on the frequency change rate. The smaller the system equivalent inertia constant, the greater the grid frequency fluctuation. It is set by expert experience and has a value range of 2–10 seconds. Indicates the rated capacity of the power grid, which depends on the scale of the power grid, in MW; Indicates the rated frequency of the power grid, which depends on the scale of the power grid, in Hz; By calculating the frequency change rate, the impact of renewable energy output fluctuations and low inertia characteristics on frequency stability is captured, providing key input for grid stability analysis and control.

[0022] S2. Based on the real-time operating data and frequency change rate of the high-proportion renewable energy power grid, a dynamic frequency prediction model is constructed to obtain a predicted grid frequency at a future time point; based on the predicted grid frequency at the future time point, the parameters of the dynamic frequency prediction model are iteratively updated to generate a dynamic frequency prediction model suitable for the high-proportion renewable energy power grid; Because traditional fixed inertia models cannot adapt to the randomness and rapid fluctuations of renewable energy output, a dynamic frequency prediction model is constructed based on the current grid frequency and frequency change rate in real-time operating data of a high-proportion renewable energy grid, combined with an adaptive inertia compensation factor, to accurately predict the frequency dynamic behavior of a high-proportion renewable energy grid. The specific implementation process is as follows: First, based on the historical operating data of a high-proportion renewable energy grid, a machine learning model (such as a long-short-term memory neural network) is used to predict the output of renewable energy, the output of conventional generators, and the load power. The historical operating data of the high-proportion renewable energy grid is obtained from an existing database. The long-short-term memory neural network model can learn the fluctuation patterns of the historical operating data and generate predictions for future time points. This is a well-known technical method for those skilled in the art and will not be described in detail here. Furthermore, based on the predicted output values ​​of renewable energy and conventional generator sets, the renewable energy penetration rate and the standardized renewable energy output change rate are calculated. By multiplying the renewable energy penetration rate and the standardized renewable energy output change rate and introducing an adjustment coefficient for weighting, an adaptive inertia compensation factor between 0 and 1 is generated. The renewable energy penetration rate can reflect the proportion of renewable energy output in the total power generation. The standardized renewable energy output change rate is used to quantify the severity of renewable energy fluctuations. The frequency change rate is combined with the adaptive inertia compensation factor to calculate the adjusted frequency change rate, which reflects the weakening effect of new energy fluctuations on inertia. The adjusted frequency change rate is integrated over the forecast period. Starting from the current frequency, the frequency change is accumulated to obtain the forecast grid frequency at the future time point. The calculation formula is: , in, Indicates time The predicted grid frequency; Indicates time The grid frequency; Indicates from time arrive The definite integral of Indicates a time interval; Indicates time Load power forecast value; Indicates time The forecast value of new energy output; Indicates time The output forecast value of traditional generator sets; Represents the adaptive inertia compensation factor, which is used to dynamically adjust the system inertia compensation factor and can reflect the impact of new energy fluctuations. The value range is ; Indicates the reference power change rate, which is set by expert experience; The adjustment coefficient represents the degree of influence of the adaptive inertia compensation factor, which is used to control the influence of the new energy penetration rate and the normalized new energy output change rate on the inertia. The initial value of the adjustment coefficient is determined based on the expert experience method and the value range is ; Indicates time The new energy penetration rate, that is, the proportion of new energy output in the total power generation, is calculated as follows: ; It represents the standardized rate of change of new energy output, which is used to quantify the fluctuation speed of new energy output and can affect the degree of inertia weakening. Indicates time Changes in renewable energy output; represents the prediction time step; Represents the time variable in the integral, and its value range is from arrive ; Indicates the frequency change rate after adjustment; An objective function is constructed based on the mean square error between the predicted grid frequency at a future time point and the actually measured grid frequency. The gradient descent method is used to iteratively update the parameters of the dynamic frequency prediction model to gradually reduce the deviation until the deviation converges, so as to find the parameter combination that minimizes the objective function, generate a dynamic frequency prediction model suitable for a high proportion of new energy power grids, improve the prediction accuracy, and provide a reliable tool for the stability analysis of the frequency of a high proportion of new energy power grids; the parameters include the adjustment coefficient, the system equivalent inertia constant and the prediction time step; the construction of the objective function and the gradient descent method are technical means well known to those skilled in the art and will not be elaborated here.

[0023] In summary, a frequency characteristic analysis and modeling method for a high-proportion renewable energy power grid has been completed.

[0024] The order in which the embodiments of the invention are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0025] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0026] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A frequency characteristic analysis and modeling method for a high-proportion renewable energy power grid, characterized in that: The following steps are involved: S1. Collect real-time operating data of the high-proportion new energy power grid and calculate the frequency change rate through the frequency response characteristic analysis algorithm; S2. Based on the real-time operating data and frequency change rate of the high-proportion renewable energy power grid, a dynamic frequency prediction model is constructed to obtain the predicted grid frequency at a future time point; based on the predicted grid frequency at the future time point, the parameters of the dynamic frequency prediction model are iteratively updated to generate a dynamic frequency prediction model suitable for the high-proportion renewable energy power grid.

2. The frequency characteristic analysis and modeling method of a high-proportion new energy power grid according to claim 1 is characterized in that: Said S1 specifically includes: The real-time operating data of the high-proportion new energy power grid includes grid frequency, new energy output, traditional generator set output and load power.

3. The frequency characteristic analysis and modeling method of a high-proportion new energy power grid according to claim 2 is characterized in that: Said S1 specifically includes: In the implementation of the frequency response characteristic analysis algorithm, the output of renewable energy and traditional generator sets is added to obtain the total generated power. The power mismatch is calculated by calculating the difference between the total generated power and the load power. Based on the power mismatch, the system equivalent inertia constant and the rated capacity of the grid are introduced to obtain the frequency change rate.

4. The frequency characteristic analysis and modeling method of a high-proportion new energy power grid according to claim 3 is characterized in that: Said S2 specifically includes: Based on the frequency change rate and the current grid frequency in the real-time operation data of the high-proportion renewable energy power grid, combined with the adaptive inertia compensation factor, a dynamic frequency prediction model is constructed to predict the frequency dynamic behavior of the high-proportion renewable energy power grid.

5. The frequency characteristic analysis and modeling method of a high-proportion new energy power grid according to claim 4 is characterized in that: Said S2 specifically includes: In the process of constructing the dynamic frequency prediction model, by learning the fluctuation pattern of the historical operating data of the high-proportion new energy power grid, the new energy output forecast value, the traditional generator set output forecast value and the load power forecast value are predicted, and the new energy penetration rate and the standardized new energy output change rate are further calculated.

6. The frequency characteristic analysis and modeling method of a high-proportion new energy power grid according to claim 5 is characterized in that: Said S2 specifically includes: Based on the new energy penetration rate and the standardized new energy output change rate, an adjustment coefficient is introduced to generate an adaptive inertia compensation factor.

7. The frequency characteristic analysis and modeling method of a high-proportion new energy power grid according to claim 6 is characterized in that: Said S2 specifically includes: The frequency change rate is combined with the adaptive inertia compensation factor to calculate the adjusted frequency change rate; the adjusted frequency change rate is time-integrated, starting from the current grid frequency, and the frequency change is accumulated to obtain the predicted grid frequency at a future time point.

8. The frequency characteristic analysis and modeling method of a high-proportion new energy power grid according to claim 7 is characterized in that: Said S2 specifically includes: Based on the mean square error between the predicted grid frequency at future time points and the actually measured grid frequency, an objective function is constructed, and the parameters of the dynamic frequency prediction model are iteratively updated through the gradient descent method to generate a dynamic frequency prediction model suitable for power grids with a high proportion of new energy.

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