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

Abstract

The present application relates to the field of high-proportion new energy power grid, and particularly relates to a frequency characteristic analysis and modeling method of high-proportion new energy power grid. The content includes: collecting real-time operation data of the high-proportion new energy power grid, and calculating the frequency change rate through the frequency response characteristic analysis algorithm; based on the real-time operation data and the frequency change rate, a dynamic frequency prediction model is constructed to predict the power grid frequency, and the model parameters are iteratively updated to generate a dynamic frequency prediction model suitable for the high-proportion new energy power grid. The problems that in the traditional frequency characteristic analysis and modeling method, the frequency change rate is calculated based on static or simplified power balance model, which cannot accurately reflect the real-time dynamic behavior of the power grid; the power generation power is predicted based on simple statistical model or short-term data, which is difficult to capture the long-term fluctuation mode and rapid change characteristics of the power generation power; and the frequency prediction model is adjusted in parameters depending on artificial experience or fixed parameters, which affects the prediction accuracy.

Description

Technical Field

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

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

[0003] Frequency characteristic analysis and modeling of power grids with high proportions of renewable energy is a cutting-edge field in power system research, involving multidisciplinary collaboration and the integration of multiple technologies. Future research needs to delve deeper into theoretical modeling, control strategies, and practical applications to address the frequency stability challenges brought about by the high penetration rate of renewable energy, and to 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 high-proportion renewable energy power grids, challenges to the dynamic response of power grids due to low inertia characteristics, and the disconnect between renewable energy output prediction and frequency control. Summary of the Invention

[0005] This invention provides a frequency characteristic analysis and modeling method for high-proportion renewable energy power grids. It addresses the shortcomings of traditional frequency characteristic analysis and modeling methods. Traditional methods often rely on static or simplified power balance models for frequency change rate calculations, failing to adequately consider the randomness and volatility of renewable energy output, as well as the impact of low inertia on frequency dynamics. This results in inaccurate frequency change rate calculations reflecting the real-time dynamic behavior of the power grid. Furthermore, traditional methods often rely on simple statistical models or short-term data for power generation prediction, making it difficult to capture long-term fluctuation patterns and rapid changes in power generation, thus limiting the accuracy of frequency prediction models. Finally, traditional methods often depend on manual experience or fixed parameters for adjusting frequency prediction models, lacking efficient automated optimization methods and hindering rapid convergence to the optimal parameter combination, thus affecting the prediction accuracy of frequency prediction models.

[0006] The present invention provides a method for frequency characteristic analysis and modeling of a high-proportion renewable energy power grid, specifically including the following technical solutions:

[0007] A method for frequency characteristic analysis and modeling of a high-proportion renewable energy power grid includes the following steps:

[0008] S1. Collect real-time operation data of high-proportion renewable energy power grids and calculate the frequency change rate through frequency response characteristic analysis algorithms;

[0009] S2. Based on the real-time operation data and frequency change rate of the high-proportion renewable energy power grid, a dynamic frequency prediction model is constructed to obtain the predicted power grid frequency at future time points. Based on the predicted power grid frequency at future time points, 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.

[0010] Preferably, S1 specifically includes:

[0011] The real-time operation data of the high-proportion renewable energy power grid includes grid frequency, renewable energy output, traditional generator output, and load power.

[0012] Preferably, S1 specifically includes:

[0013] In the implementation of the frequency response characteristic analysis algorithm, the output of new energy sources is added to the output of traditional generator sets to obtain the total power generation; the power mismatch is obtained by calculating the difference between the total power generation and the load power; based on the power mismatch, the equivalent inertial constant of the system and the rated capacity of the power grid are introduced to obtain the frequency change rate.

[0014] Preferably, S2 specifically includes:

[0015] Based on the frequency change rate and the current grid frequency in the real-time operation data of the high-proportion renewable energy grid, and 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 grid.

[0016] Preferably, S2 specifically includes:

[0017] In the process of constructing the dynamic frequency prediction model, by learning the fluctuation patterns of historical operating data of high-proportion renewable energy power grids, the predicted values ​​of renewable energy output, traditional generator output, and load power are obtained, and the renewable energy penetration rate and the standardized renewable energy output change rate are further calculated.

[0018] Preferably, S2 specifically includes:

[0019] Based on the penetration rate of new energy sources and the standardized rate of change in new energy output, an adjustment coefficient is introduced to generate an adaptive inertia compensation factor.

[0020] Preferably, S2 specifically includes:

[0021] The adjusted frequency change rate is calculated by combining the frequency change rate with the adaptive inertia compensation factor; the adjusted frequency change rate is then integrated over time, starting from the current grid frequency, to accumulate the frequency change and obtain the predicted grid frequency at future time points.

[0022] Preferably, S2 specifically includes:

[0023] Based on the mean square error between the predicted grid frequency and the actual measured grid frequency at future time points, an objective function is constructed, and the parameters of the dynamic frequency prediction model are iteratively updated using the gradient descent method to generate a dynamic frequency prediction model suitable for grids with a high proportion of new energy.

[0024] The beneficial effects of the technical solution of the present invention are:

[0025] 1. Based on the power balance principle, combined with the system's equivalent inertia constant and the grid's rated capacity, the frequency change rate is calculated, accurately reflecting the impact of the randomness and low inertia characteristics of new energy output on the grid's frequency stability. This provides key dynamic frequency indicators for grid dispatch and control, and enhances the understanding and response capabilities to frequency fluctuations in grids with a high proportion of new energy.

[0026] 2. By utilizing real-time operating data of high-proportion renewable energy power grids, current grid frequency and frequency change rate, and combining machine learning models to predict renewable energy output, traditional generator output, and load power, and by adjusting the frequency change rate through an adaptive inertia compensation factor, a dynamic frequency prediction model is constructed. This significantly improves the accuracy of predicting grid frequency at future points in time, providing a reliable tool for optimizing the operation and stabilizing the frequency of high-proportion renewable energy power grids.

[0027] 3. By iteratively optimizing the adjustment coefficient, system equivalent inertia constant, and prediction time step using 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 high-proportion renewable energy power grids. This improves the robustness and applicability of the dynamic frequency prediction model under different power grid scales and renewable energy penetration scenarios, providing technical support for the stable operation of the power system. Attached Figure Description

[0028] Figure 1 This is a flowchart of a frequency characteristic analysis and modeling method for a high-proportion renewable energy power grid as described in this invention. Detailed Implementation

[0029] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] Unless otherwise defined, 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 pertains.

[0031] The following description, in conjunction with the accompanying drawings, details the specific scheme of the frequency characteristic analysis and modeling method for a high-proportion new energy power grid provided by this invention.

[0032] See attached document Figure 1 The diagram illustrates a flowchart of a frequency characteristic analysis and modeling method for a high-proportion renewable energy power grid according to an embodiment of the present invention. The method includes the following steps:

[0033] S1. Collect real-time operation data of high-proportion renewable energy power grids and calculate the frequency change rate through frequency response characteristic analysis algorithms;

[0034] Real-time operational data of a high-proportion renewable energy power grid is collected using high-precision sensors, including grid frequency, renewable energy output, traditional generator output, and load power. The grid frequency is measured in real time by a frequency sensor, reflecting the real-time frequency dynamics of the high-proportion renewable energy power grid. Renewable energy output refers to 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. Traditional generator output refers to 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 high-proportion renewable energy power grid. To capture the dynamic characteristics of frequency fluctuations, it is recommended that the sampling frequency of real-time operational data be no less than 1Hz (i.e., at least once per second) to ensure that the real-time operational data can reflect the rapid fluctuations in renewable energy output and the transient behavior of load changes.

[0035] Based on real-time operation data of a high-proportion renewable energy power grid, the frequency change rate is calculated through a frequency response characteristic analysis algorithm to capture the impact of renewable energy output fluctuations and low inertia characteristics on the frequency stability of the power grid.

[0036] The frequency response characteristic analysis algorithm is based on power balance. It calculates the difference between the total power generation (including the output of new energy sources and the output of traditional generator sets) and the load power, and combines the system equivalent inertia constant and the grid rated capacity to output the frequency change rate, which is then used as the input of the dynamic frequency prediction model.

[0037] The frequency response characteristic analysis algorithm adds the output of new energy sources to the output of traditional generator sets to obtain the total power generation, reflecting the total contribution of all power generation resources in a high-proportion new energy power grid. New energy output exhibits randomness and volatility, while traditional generator set output is relatively stable. By calculating the difference between the total power generation (including both new energy and traditional generator set output) and the load power, power mismatch is obtained, representing the imbalance between power generation and load in a high-proportion new energy power grid. Power mismatch is the main driving force for grid frequency changes. If the total power generation is greater than the load power, the grid frequency may rise; if the load power is greater than the total power generation, the grid frequency may fall; if they are equal, it indicates that power generation and load are in a balanced state, and the grid frequency remains stable. Based on the power system's oscillation equation, the power mismatch is divided by the product of the system's equivalent inertia constant and the grid's rated capacity to obtain the frequency change rate, quantifying the dynamic speed of grid frequency change. The system's equivalent inertia constant reflects the grid's inertia level, i.e., the grid's ability to resist frequency changes, while the grid's rated capacity represents the grid's scale.

[0038] The formula for calculating the rate of change of frequency is:

[0039] ,

[0040] in, It represents the rate of change of frequency, in Hz / s, and is the derivative of the power grid frequency with respect to time. It is used to quantify the dynamic response of the power grid frequency. This indicates that the high proportion of new energy power grids in time The load power, in MW; Indicates time Total power generation, in time New energy output Compared with traditional generator set output sum; The equivalent inertia constant of the system determines the degree of influence of power mismatch on the frequency change rate. The smaller the equivalent inertia constant of the system, the greater the fluctuation of the grid frequency. It is set by expert experience and the value range is 2–10 seconds. This indicates the rated capacity of the power grid, which varies depending on the scale of the power grid, and is measured in MW. Indicates the rated frequency of the power grid, which depends on the scale of the power grid, and is measured in Hz;

[0041] By calculating the rate of frequency change, the impact of new energy output fluctuations and low inertia characteristics on frequency stability is captured, providing key inputs for power grid stability analysis and control.

[0042] S2. Based on the real-time operation data and frequency change rate of the high-proportion renewable energy power grid, a dynamic frequency prediction model is constructed to obtain the predicted power grid frequency at future time points; based on the predicted power grid frequency at future time points, 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.

[0043] Since 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 from real-time operating data of high-proportion renewable energy power grids, combined with an adaptive inertia compensation factor. This model accurately predicts the dynamic frequency behavior of high-proportion renewable energy power grids. The specific implementation process is as follows:

[0044] First, based on the historical operating data of the high-proportion renewable energy power grid, the predicted values ​​of renewable energy output, traditional generator output, and load power are obtained through machine learning models (such as long short-term memory neural networks). The historical operating data of the high-proportion renewable energy power grid comes from an existing database. The long short-term memory neural network model can learn the fluctuation patterns of historical operating data and generate predicted values ​​for future time points. This is a technical means well known to those skilled in the art and will not be elaborated here.

[0045] Furthermore, based on the predicted output values ​​of new energy sources and traditional generator units, the new energy penetration rate and the standardized new energy output change rate are calculated. By calculating the product of the new energy penetration rate and the standardized new energy output change rate, and introducing an adjustment coefficient for weighting, an adaptive inertia compensation factor between 0 and 1 is generated. The new energy penetration rate reflects the proportion of new energy output in the total power generation. The standardized new energy output change rate is used to quantify the severity of new energy fluctuations.

[0046] By combining the frequency change rate with the adaptive inertia compensation factor, the adjusted frequency change rate is calculated. The adjusted frequency change rate reflects the weakening effect of new energy fluctuations on inertia.

[0047] Integrating the adjusted rate of frequency change over the forecast period, starting from the current frequency, the cumulative frequency change yields the predicted grid frequency at future time points. The calculation formula is as follows:

[0048] ,

[0049] in, Indicates time Predicted grid frequency; Indicates time The power grid frequency; Indicates from time arrive definite integral, Indicates a time interval; Indicates time The predicted load power; Indicates time Forecast values ​​of new energy power output; Indicates time The predicted output value of traditional generator sets; This represents the adaptive inertia compensation factor, a compensation factor used to dynamically adjust system inertia. It reflects the impact of new energy fluctuations and has a value range of [value range missing]. ; The reference power change rate is set using expert experience. This adjustment coefficient represents the degree of influence of the adaptive inertia compensation factor. It is used to control the influence of the new energy penetration rate and the standardized rate of change of new energy output on inertia. The initial value of the adjustment coefficient is determined based on expert experience, and its range is [insert range here]. ; Indicates time The renewable energy penetration rate, which is the proportion of renewable energy output to total power generation, is calculated using the following formula: ; This represents the standardized rate of change in renewable energy output, used to quantify the fluctuation speed of renewable energy output, and can affect the degree of inertia attenuation. Indicates time Changes in the output of new energy sources; Indicates the prediction time step; Represents the time variable in the integral, with a range from arrive ; This represents the adjusted rate of change of frequency.

[0050] An objective function is constructed based on the mean square error between the predicted grid frequency and the actual measured grid frequency at future time points. A gradient descent method is used to iteratively update the parameters of the dynamic frequency prediction model, gradually reducing the deviation until it converges. This process aims to find the parameter combination that minimizes the objective function, generating a dynamic frequency prediction model suitable for high-proportion renewable energy grids. This improves prediction accuracy and provides a reliable tool for the stability analysis of high-proportion renewable energy grid frequencies. The parameters include adjustment coefficients, system equivalent inertia constants, and prediction time steps. The construction of the objective function and the gradient descent method are well-known techniques to those skilled in the art and will not be elaborated upon here.

[0051] In summary, a method for frequency characteristic analysis and modeling of a high-proportion renewable energy power grid has been developed.

[0052] The order of the embodiments is for illustrative purposes only and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0053] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0054] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. 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. Such 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, and should all be included within the protection scope of the present invention.

Claims

1. A method for frequency characteristic analysis and modeling of a high-proportion renewable energy power grid, characterized in that, Includes the following steps: S1. Collect real-time operation data of the high-proportion renewable energy power grid, and add the renewable energy output to the output of traditional generator units through a frequency response characteristic analysis algorithm to obtain the total power generation; calculate the power mismatch by calculating the difference between the total power generation and the load power; based on the power mismatch, introduce the system equivalent inertia constant and the grid rated capacity to calculate the frequency change rate. S2. Based on the current grid frequency and frequency change rate from the real-time operation data of the high-proportion renewable energy grid, and combined with the adaptive inertia compensation factor, a dynamic frequency prediction model is constructed to predict the dynamic frequency behavior of the high-proportion renewable energy grid. The specific implementation process is as follows: By learning the fluctuation patterns of historical operating data from high-proportion renewable energy power grids, the project predicts renewable energy output, traditional generator output, and load power. It also calculates the renewable energy penetration rate and the standardized renewable energy output change rate. Based on these two factors, an adjustment coefficient is introduced to generate an adaptive inertia compensation factor. ,in, The adjustment coefficient represents the degree to which the adaptive inertia compensation factor is affected. Indicates time The penetration rate of new energy sources Indicates time Changes in the output of new energy sources Indicates the prediction time step. Represents the time variable in the integral. Indicates the rate of change of reference power; 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 integrated over time, starting from the current grid frequency, to accumulate frequency changes and predict the frequency dynamic behavior of the high-proportion new energy grid, thus obtaining the predicted grid frequency at future time points. Based on the predicted grid frequency at future time points, the parameters of the dynamic frequency prediction model are iteratively updated to generate a dynamic frequency prediction model suitable for grids with a high proportion of new energy sources.

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

3. The frequency characteristic analysis and modeling method for a high-proportion renewable energy power grid according to claim 1, characterized in that, S2 specifically includes: Based on the mean square error between the predicted grid frequency and the actual measured grid frequency at future time points, an objective function is constructed, and the parameters of the dynamic frequency prediction model are iteratively updated using the gradient descent method to generate a dynamic frequency prediction model suitable for grids with a high proportion of new energy.

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