Flexible load collaborative optimization scheduling system for high proportion of new energy access

Abstract

The application relates to the technical field of smart grids, in particular to a power grid flexible load collaborative optimization scheduling system for high-proportion new energy access. The system comprises a data acquisition module, which acquires voltage data of a new energy access port; a low-frequency oscillation analysis module, which obtains a voltage fluctuation sequence of a current monitoring period according to the fundamental frequency characteristics of the voltage data of the current monitoring period; and obtains low-frequency oscillation energy degrees of different time periods according to the difference between noise energy characteristics and total energy characteristics of the different time periods in the voltage fluctuation sequence; and a low-frequency oscillation suppression module, which obtains an adaptive Kpss gain parameter of a next monitoring period according to the change trend and size of the low-frequency oscillation energy degree in the current monitoring period, and suppresses low-frequency oscillation in the voltage of the new energy access port in the next monitoring period. The application adaptively obtains the Kpss gain parameter, improves the stability of the voltage of the new energy access port, and further improves the accuracy of the power grid flexible load collaborative scheduling.

Description

Technical Field

[0001] This application relates to the field of smart grid technology, specifically to a flexible load collaborative optimization scheduling system for power grids with a high proportion of renewable energy integration. Background Technology

[0002] With breakthroughs in modern motor technology, including high-frequency drives, permanent magnet synchronous motors, and wide-bandgap devices, the proportion of new energy sources in smart grids continues to rise. High-performance motors, characterized by high dynamic response and low losses, can improve the grid integration efficiency of wind, solar, and energy storage systems. Combined with flexible DC transmission and AI dispatching, they can optimize the grid's flexible loads when influenced by new energy sources or when lines are affected by low-frequency fluctuations, thereby improving the stability of smart grid operation and reducing damage to electrical equipment caused by power instability. Therefore, the application of new energy sources in the power grid is becoming increasingly widespread.

[0003] When renewable energy sources are integrated into the smart grid, they interact with the grid, generating low-frequency oscillations that affect the grid's stable operation. Traditionally, this is addressed by setting a fixed Kpss gain parameter in the power system stabilizer (PSS) to regulate these low-frequency oscillations in the renewable energy connection voltage. However, during renewable energy generation, the voltage supplied by hydropower, solar power, or wind power is unstable, resulting in varying renewable energy power output at different times. Consequently, the intensity of the low-frequency oscillations generated when renewable energy is integrated into the smart grid also varies. Therefore, using a fixed Kpss gain parameter to suppress low-frequency oscillations in the renewable energy connection voltage cannot adapt to the constantly changing intensity of these oscillations, thus failing to effectively suppress them and hindering the efficient and stable operation of the smart grid. Summary of the Invention

[0004] To address the aforementioned technical problems, the purpose of this application is to provide a flexible load collaborative optimization scheduling system for power grids with a high proportion of renewable energy integration. The specific technical solution adopted is as follows: This application proposes a flexible load collaborative optimization scheduling system for power grids with high proportion of renewable energy integration, the system comprising: Data acquisition module: Acquires voltage data at the new energy access points of the power grid in real time; Low-frequency oscillation analysis module: Based on the difference between the voltage data of the current monitoring period and the voltage corresponding to its fundamental frequency in the frequency domain, the voltage fluctuation sequence of the current monitoring period is obtained and divided into multiple fluctuation subsequences; based on the mean of the data in each fluctuation subsequence and the difference between all adjacent data, the noise energy of each fluctuation subsequence is obtained; based on the mean of the absolute values ​​of the data in each fluctuation subsequence and the difference between the absolute values ​​of all adjacent data, the total energy of each fluctuation subsequence is obtained; based on the difference between the total energy and the noise energy of each fluctuation subsequence, the low-frequency oscillation energy of each fluctuation subsequence is obtained. Low-frequency oscillation suppression module: Based on the changing trend of low-frequency oscillation energy of all fluctuation subsequences in the current monitoring period, and the low-frequency oscillation energy of the last fluctuation subsequence, the low-frequency oscillation time-varying degree of the current monitoring period is obtained. Based on the difference between the low-frequency oscillation time-varying degree of the current monitoring period and that of the previous monitoring period, the adaptive Kpss gain parameter for the next monitoring period is obtained. Then, the power system stabilizer is used to suppress the low-frequency oscillation in the voltage data of the new energy access point in the next monitoring period.

[0005] Preferably, the method for obtaining the voltage fluctuation sequence for the current monitoring period is as follows: The voltage data within the current monitoring period are arranged in chronological order and recorded as the voltage sequence of the current monitoring period; Obtain the spectral sequence of the voltage sequence for the current monitoring period, and use the fundamental frequency and its corresponding amplitude in the spectral sequence as the input of the inverse Fourier transform algorithm, and output the fundamental frequency voltage sequence for the current monitoring period; The difference between the voltage sequence of the current monitoring period and all data with the same position in the base frequency voltage sequence is arranged according to their corresponding position and recorded as the voltage fluctuation sequence of the current monitoring period.

[0006] Preferably, the method for obtaining the noise energy level of each wave subsequence is as follows: Calculate the absolute value of the mean of all data in each fluctuation subsequence; Calculate the mean of the absolute differences between all adjacent data in each fluctuation subsequence; The sum of the absolute value and the mean is denoted as the noise energy level of each pulsar sequence.

[0007] Preferably, the method for obtaining the total energy of each wave subsequence is as follows: Calculate the mean of the absolute values ​​of all data in each fluctuation subsequence; Calculate the mean of the absolute differences between the absolute values ​​of all adjacent data in each fluctuation subsequence; The sum of the two means is denoted as the total energy of each wavelet sequence.

[0008] Preferably, the low-frequency oscillation energy level of each wave subsequence refers to the difference between the total energy level of each wave subsequence and the noise energy level.

[0009] Preferably, the formula for calculating the low-frequency oscillation time-varying degree during the current monitoring period is: In the formula, This indicates the time-varying degree of low-frequency oscillations during the current monitoring period; This represents the slope of the fitted straight line of the low-frequency oscillation energy sequence during the current monitoring period; This represents the last data point in the low-frequency oscillation energy sequence for the current monitoring period; This refers to the sigmoid() function.

[0010] Preferably, the low-frequency oscillation energy sequence of the current monitoring period refers to the sequence composed of the low-frequency oscillation energy of all fluctuation subsequences within the current monitoring period arranged in chronological order.

[0011] Preferably, the adaptive Kpss gain parameter for the next monitoring period refers to the maximum value between the preset minimum Kpss gain parameter and the corrected Kpss gain parameter for the next monitoring period.

[0012] Preferably, the formula for calculating the corrected Kpss gain parameter for the next monitoring period is: In the formula, This indicates the corrected Kpss gain parameter for the next monitoring period; This indicates the preset Kpss gain parameter for the current monitoring period; This indicates the time-varying degree of low-frequency oscillations in the previous monitoring period; This indicates the time-varying degree of low-frequency oscillations during the current monitoring period.

[0013] Preferably, in the process of suppressing low-frequency oscillations in the voltage data of the new energy access point in the next monitoring period, the calculated adaptive Kpss gain parameter for the next monitoring period is used as the Kpss gain parameter of the power system stabilizer for the next monitoring period.

[0014] This application has the following beneficial effects: This application addresses the problem that traditional methods using fixed Kpss gain parameters to suppress low-frequency oscillations in the voltage of renewable energy access are ineffective due to their inability to adapt to constantly changing low-frequency oscillation intensities. Based on the characteristic that the main frequency of the renewable energy access voltage corresponds to the fundamental frequency in the frequency domain representation, a voltage fluctuation sequence for the current monitoring period is obtained to characterize the voltage disturbances caused by low-frequency oscillations and noise interference during the current monitoring period. Based on the difference between the total energy and noise energy in each fluctuation subsequence, a low-frequency oscillation energy level is constructed for each fluctuation subsequence, enabling accurate assessment of the low-frequency oscillation intensity in each subsequence, which is beneficial for subsequent accurate adjustment of the Kpss gain parameter. By analyzing the low-frequency oscillation trend during the current monitoring period, a low-frequency oscillation time-varying degree is constructed, thereby determining the effectiveness of the current Kpss gain parameter for grid stability control. This allows for adaptive acquisition of the Kpss gain parameter for the next monitoring period to adapt to the impact of low-frequency oscillations of varying intensities on power system stability. This reduces the control lag and overshoot caused by traditional fixed Kpss gain parameters, improves the stability of renewable energy access voltage, and ultimately enhances the accuracy and robustness of flexible load collaborative optimization scheduling in the power grid. Attached Figure Description

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of this application 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 only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a block diagram of a grid flexible load collaborative optimization scheduling system for high-proportion renewable energy access, provided in one embodiment of this application. Figure 2 This is a flowchart illustrating the steps for suppressing low-frequency oscillations in the voltage data of a new energy access point during the next monitoring period, as provided in one embodiment of this application. Detailed Implementation

[0017] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of the power grid flexible load collaborative optimization scheduling system for high-proportion renewable energy access proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0018] 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 application pertains.

[0019] The following description, in conjunction with the accompanying drawings, details the specific scheme of the flexible load collaborative optimization scheduling system for high-proportion renewable energy access provided in this application.

[0020] Please see Figure 1 The diagram illustrates a block diagram of a grid flexible load collaborative optimization scheduling system for high-proportion renewable energy access, provided in one embodiment of this application. The system includes: a data acquisition module, a low-frequency oscillation analysis module, and a low-frequency oscillation suppression module.

[0021] Data acquisition module: Acquires voltage data at the new energy access points of the power grid in real time.

[0022] First, the voltage data at the new energy input point in the smart grid is collected in real time by smart meters. Since the new energy source needs to undergo DC-AC conversion via an inverter before being transmitted to the grid, the collected voltage data is AC voltage data. The voltage data collection frequency is FkHz, with each Hmin serving as a monitoring period. Here, F is a preset frequency that must satisfy the Nyquist sampling theorem; in this embodiment, F is set to 1. H is a preset duration, representing the time required to adjust the Kpss gain parameter once; in this embodiment, H is set to 1. Implementers can adjust this value according to actual conditions.

[0023] Due to the instability of electricity production from new energy sources per unit time, data acquisition may result in missing data. Therefore, in this embodiment, linear interpolation is used to complete the missing voltage data for each monitoring period. The completed voltage data for the current monitoring period are arranged in chronological order and denoted as the voltage sequence for the current monitoring period. Linear interpolation is a well-known technique, and the specific calculation steps will not be detailed here.

[0024] Low-frequency oscillation analysis module: Based on the difference between the voltage data of the current monitoring period and the voltage corresponding to its fundamental frequency in the frequency domain, the voltage fluctuation sequence of the current monitoring period is obtained and divided into multiple fluctuation sub-sequences; based on the mean of the data in each fluctuation sub-sequence and the difference between all adjacent data, the noise energy of each fluctuation sub-sequence is obtained; based on the mean of the absolute values ​​of the data in each fluctuation sub-sequence and the difference between the absolute values ​​of all adjacent data, the total energy of each fluctuation sub-sequence is obtained; based on the difference between the total energy and the noise energy of each fluctuation sub-sequence, the low-frequency oscillation energy of each fluctuation sub-sequence is obtained.

[0025] When renewable energy sources are connected to the smart grid, they interact with the grid, resulting in low-frequency oscillations in the voltage data at the renewable energy connection point. However, because the connected renewable energy is in an unstable state, the intensity of these low-frequency oscillations varies at different times, thus requiring different levels of low-frequency oscillation suppression from the power system stabilizer. If the Kpss gain parameter is set too high, the stabilizer's damping will be excessive, leading to a slow power system response and wasted energy resources. Conversely, if the Kpss gain parameter is set too low, the stabilizer's damping will be insufficient to suppress low-frequency oscillations in a timely manner, causing instability in the smart grid. Therefore, the Kpss gain parameter needs to be adaptively adjusted based on the degree of low-frequency oscillations in the voltage data at the renewable energy connection point.

[0026] During data acquisition, sensor errors and other factors may introduce noise into the collected voltage data. However, this noise-induced voltage data deviation does not represent actual voltage fluctuations in the real-world scenario, and therefore does not affect grid stability. When analyzing the impact of low-frequency oscillations on grid stability, it is easy to mistake noise-induced voltage fluctuations for those caused by low-frequency oscillations, leading to inaccurate grid stability control. Therefore, it is necessary to distinguish between low-frequency oscillation characteristics and noise characteristics in the voltage data from renewable energy access points.

[0027] Low-frequency oscillations and noise generated by new energy sources exhibit different states in voltage data. Low-frequency oscillations are periodic and rhythmic voltage fluctuations; noise is a random state that causes the voltage waveform to no longer be smooth, but rather a steep change. Therefore, the voltage sequence of the current monitoring period is used as the input to the Fourier transform algorithm, and the output of the algorithm is the spectrum sequence of the voltage sequence of the current monitoring period. Here, the fundamental frequency is the main frequency of the smart grid. Therefore, the fundamental frequency and its corresponding amplitude in the spectrum sequence of the voltage sequence of the current monitoring period are used as the input to the inverse Fourier transform algorithm, and the output is the fundamental frequency voltage sequence of the current monitoring period. The resulting fundamental frequency voltage sequence represents the voltage state of the new energy access point, which is not affected by low-frequency oscillations and noise during the current monitoring period. The Fourier transform algorithm and the inverse Fourier transform algorithm are well-known technologies, and the specific calculation steps will not be elaborated further.

[0028] Furthermore, the difference between the voltage sequence of the current monitoring period and all data with the same position in the base frequency voltage sequence is calculated, and all differences are arranged according to their corresponding position, which is recorded as the voltage fluctuation sequence of the current monitoring period. This sequence is used to characterize the voltage disturbance caused by low-frequency oscillations and noise in the new energy access voltage during the current monitoring period.

[0029] If the energy of low-frequency oscillations in the converter-side voltage data increases over time, it indicates a stronger interference from low-frequency oscillations, and a greater tendency for the power grid to become unstable. Therefore, it is necessary to analyze the intensity of low-frequency oscillation interference in different time periods. The voltage fluctuation sequence of the current monitoring period is evenly divided into N subsequences, denoted as the fluctuation subsequence. N is a preset number, which is 60 in this embodiment.

[0030] The greater the intensity of voltage data changes caused by noise in a fluctuating subsequence, the greater the randomness of the data fluctuations in that subsequence. This disrupts the originally sinusoidal voltage waveform, making it more chaotic. Therefore, the smaller the noise interference in a fluctuating subsequence, the closer the mean of all elements in the subsequence is to zero; conversely, the greater the noise interference, the further the mean of all elements is from zero. Furthermore, the greater the degree of noise interference, the more spikes will appear in the originally smooth sine wave, resulting in greater fluctuations between adjacent elements in the fluctuating subsequence.

[0031] In a preferred embodiment, the noise energy level of each oscillating subsequence is obtained based on the mean of the data in each oscillating subsequence and the difference between all adjacent data, which is used to characterize the level of noise energy contained in each oscillating subsequence.

[0032] In this embodiment, the noise energy fluctuation degree of the i-th fluctuation subsequence is denoted as... Its specific expression is: In the formula, This represents the noise energy level of the i-th wave subsequence; This represents the absolute value of the mean of all data in the i-th fluctuation subsequence; It represents the mean of the absolute differences between all adjacent data in the i-th oscillating subsequence.

[0033] Among them, through It can effectively characterize the random fluctuation features in the fluctuation subsequence, and through It can effectively characterize the spike features caused by noise in volatile subsequences. The larger the value, the higher the noise energy contained in the i-th wavelet subsequence.

[0034] Furthermore, for the total energy of low-frequency oscillations and noise in a single wave subsequence, the absolute values ​​of all elements in the wave subsequence can be taken. The larger the absolute value of all elements in the wave subsequence, the greater the intensity of the low-frequency oscillations and noise. Therefore, the mean of the absolute values ​​of all data in the i-th wave subsequence is calculated, and the mean of the absolute differences between the absolute values ​​of all adjacent data in the i-th wave subsequence is also calculated. The sum of the two means is recorded as the total energy of the i-th wave subsequence, which is used to characterize the total energy of noise and low-frequency oscillations within the i-th wave subsequence.

[0035] The total energy of each wave subsequence is a combination of aperiodic and periodic energies, while the noise energy is composed of aperiodic energy. Therefore, by subtracting the noise energy from the total energy of each wave subsequence, we can obtain the periodic energy, i.e., the low-frequency oscillation energy, for the corresponding time period of each wave subsequence. The difference between the total energy and the noise energy of the i-th wave subsequence is denoted as the low-frequency oscillation energy of the i-th wave subsequence; the larger the value, the higher the low-frequency oscillation energy in the i-th wave subsequence, and the more significant the low-frequency oscillation characteristics.

[0036] Low-frequency oscillation suppression module: Based on the changing trend of low-frequency oscillation energy of all fluctuation subsequences in the current monitoring period, and the low-frequency oscillation energy of the last fluctuation subsequence, the low-frequency oscillation time-varying degree of the current monitoring period is obtained. Based on the difference between the low-frequency oscillation time-varying degree of the current monitoring period and that of the previous monitoring period, the adaptive Kpss gain parameter for the next monitoring period is obtained. Then, the power system stabilizer is used to suppress the low-frequency oscillation in the voltage data of the new energy access point in the next monitoring period.

[0037] Furthermore, the grid flexible load optimization control for renewable energy access is performed in real time. If the Kpss gain parameter has a better effect on the voltage stability control of the renewable energy access point during the current monitoring period, then the intensity of the low-frequency oscillations in the voltage of the renewable energy access point will continuously weaken during the current monitoring period, and the operating state will become more stable.

[0038] Therefore, the low-frequency oscillation energy levels of all fluctuation subsequences within the current monitoring period are arranged in chronological order to obtain the low-frequency oscillation energy sequence for the current monitoring period, which is used to characterize the changing state of low-frequency oscillations within the current monitoring period. If the changing trend of elements in the low-frequency oscillation energy sequence for the current monitoring period is upward, it indicates that the grid stability is worse within the current monitoring period. Thus, the low-frequency oscillation energy sequence for the current monitoring period is used as the input for least-squares linear fitting, and the output is the slope of the fitted line for the low-frequency oscillation energy sequence for the current monitoring period, used to characterize the trend change of the low-frequency oscillation energy sequence for the current monitoring period. The calculation of least-squares linear fitting is a well-known technique, and the specific calculation steps will not be elaborated here.

[0039] When the slope is positive and the larger the value, the more the low-frequency oscillation characteristics are increasing during the current monitoring period, and the voltage at the new energy access point of the power grid becomes more unstable. When the slope is negative and the smaller the value, the more the low-frequency oscillation characteristics are decreasing during the current monitoring period, and the low-frequency oscillations in the voltage at the new energy access point of the power grid are being effectively suppressed, and the voltage at the new energy access point of the power grid becomes more stable.

[0040] Therefore, as a preferred implementation, the low-frequency oscillation time-varying degree of the current monitoring period is obtained based on the changing trend of the low-frequency oscillation energy of all fluctuation subsequences within the current monitoring period, as well as the low-frequency oscillation energy of the last fluctuation subsequence, and is used to characterize the voltage instability of the new energy access point of the power grid within the current monitoring period.

[0041] In this embodiment, the low-frequency oscillation time-varying degree of the current monitoring period is denoted as... Its specific expression is: In the formula, This indicates the time-varying degree of low-frequency oscillations during the current monitoring period; This represents the slope of the fitted straight line of the low-frequency oscillation energy sequence during the current monitoring period; This represents the last data point in the low-frequency oscillation energy sequence for the current monitoring period; This refers to the sigmoid() function, which is used to normalize data.

[0042] During the current monitoring period, the better the power system stabilizer suppresses low-frequency oscillations in the voltage at the renewable energy inlet, the smaller the value of the last element in the low-frequency oscillation energy sequence during that period. Simultaneously, the slope of the fitted straight line for the low-frequency oscillation energy sequence during the current monitoring period is also smaller. Therefore, the smaller the time-varying degree of the low-frequency oscillations during the current monitoring period, the smaller the low-frequency oscillations in the voltage at the renewable energy inlet during that period, and the better the grid stability regulation effect during that period.

[0043] Furthermore, if the low-frequency oscillation time-varying degree of the current monitoring period is less than that of the previous monitoring period, it indicates that the low-frequency oscillation in the new energy access voltage of the smart grid can be well suppressed during the current monitoring period. The greater the difference, the better the suppression effect of the low-frequency oscillation. In this case, the Kpss gain parameter of the power system stabilizer should be reduced in the next monitoring period to decrease the damping of the power system stabilizer and reduce the energy loss of the power system. Conversely, if the low-frequency oscillation time-varying degree of the current monitoring period is greater than that of the previous monitoring period, it indicates that the degree of low-frequency oscillation in the new energy access voltage of the smart grid is increasing during the current monitoring period. The greater the difference, the more unstable the voltage of the new energy access of the grid is during the current monitoring period. In this case, the Kpss gain parameter of the power system stabilizer should be increased in the next monitoring period to increase the damping of the power system stabilizer and thus stabilize the voltage of the new energy access of the grid.

[0044] Based on the above analysis, and taking into account the difference in low-frequency oscillation time-varying degree between the current monitoring period and the previous monitoring period, the Kpss gain parameter for the current monitoring period is corrected to obtain the corrected Kpss gain parameter for the next monitoring period. The specific correction formula is as follows: In the formula, This indicates the corrected Kpss gain parameter for the next monitoring period; This indicates the preset Kpss gain parameter for the current monitoring period; This indicates the time-varying degree of low-frequency oscillations in the previous monitoring period; This indicates the time-varying degree of low-frequency oscillations during the current monitoring period.

[0045] When the low-frequency oscillation time variation in the current monitoring period is less than that in the previous monitoring period, A negative value is used to reduce the Kpss gain parameter for the next monitoring period; when the low-frequency oscillation time variation of the current monitoring period is greater than that of the previous monitoring period, A positive number is used to increase the Kpss gain parameter for the next monitoring period.

[0046] Furthermore, to prevent the calculated corrected Kpss gain parameter for the next monitoring period from being too small, which could cause the power system stabilizer to malfunction, a minimum value needs to be set for the Kpss gain parameter for each monitoring period. Specifically, the adaptive Kpss gain parameter for the next monitoring period is calculated as follows: In the formula, This indicates the adaptive Kpss gain parameter for the next monitoring period; This represents the preset minimum Kpss gain parameter, which is set to 0.1 in this embodiment. This is used to prevent the power system stabilizer's Kpss gain parameter from being too small, which would prevent the effective suppression of low-frequency oscillations in the new energy access voltage. This indicates the corrected Kpss gain parameter for the next monitoring period; This represents the function that takes the maximum value.

[0047] Next, the calculated adaptive Kpss gain parameter for the next monitoring period is used as the Kpss gain parameter for the power system stabilizer in the next monitoring period. This allows for adaptive adjustment of the damping of the power system stabilizer in the next monitoring period, thereby suppressing low-frequency oscillations in the voltage at the renewable energy access point during the next monitoring period. The flowchart illustrating the steps for suppressing low-frequency oscillations in the voltage data at the renewable energy access point during the next monitoring period is shown below. Figure 2 As shown.

[0048] The above methods suppress low-frequency oscillations in the voltage of the renewable energy access points during each monitoring period, thereby ensuring the voltage of the renewable energy access points remains stable. This makes the subsequent flexible load adjustment by the smart grid based on the input power of the renewable energy access points more stable and reliable. The flexible load adjustment based on the input power of the renewable energy access points is a well-known technology in the field, and the specific process will not be elaborated further.

[0049] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0050] 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.

[0051] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A flexible load collaborative optimization dispatching system for power grids with a high proportion of renewable energy integration, characterized in that: The system includes: Data acquisition module: Acquires voltage data at the new energy access points of the power grid in real time; Low-frequency oscillation analysis module: Based on the difference between the voltage data of the current monitoring period and the voltage corresponding to its fundamental frequency in the frequency domain, the voltage fluctuation sequence of the current monitoring period is obtained and divided into multiple fluctuation subsequences; based on the mean of the data in each fluctuation subsequence and the difference between all adjacent data, the noise energy of each fluctuation subsequence is obtained; based on the mean of the absolute values ​​of the data in each fluctuation subsequence and the difference between the absolute values ​​of all adjacent data, the total energy of each fluctuation subsequence is obtained; based on the difference between the total energy and the noise energy of each fluctuation subsequence, the low-frequency oscillation energy of each fluctuation subsequence is obtained. Low-frequency oscillation suppression module: Based on the changing trend of low-frequency oscillation energy of all fluctuation subsequences in the current monitoring period, and the low-frequency oscillation energy of the last fluctuation subsequence, the low-frequency oscillation time-varying degree of the current monitoring period is obtained. Based on the difference between the low-frequency oscillation time-varying degree of the current monitoring period and that of the previous monitoring period, the adaptive Kpss gain parameter for the next monitoring period is obtained. Then, the power system stabilizer is used to suppress the low-frequency oscillation in the voltage data of the new energy access point in the next monitoring period.

2. The power grid flexible load collaborative optimization scheduling system for high-proportion renewable energy access as described in claim 1, characterized in that, The method for obtaining the voltage fluctuation sequence during the current monitoring period is as follows: The voltage data within the current monitoring period are arranged in chronological order and recorded as the voltage sequence of the current monitoring period; Obtain the spectral sequence of the voltage sequence for the current monitoring period, and use the fundamental frequency and its corresponding amplitude in the spectral sequence as the input of the inverse Fourier transform algorithm, and output the fundamental frequency voltage sequence for the current monitoring period; The difference between the voltage sequence of the current monitoring period and all data with the same position in the base frequency voltage sequence is arranged according to their corresponding position and recorded as the voltage fluctuation sequence of the current monitoring period.

3. The power grid flexible load collaborative optimization scheduling system for high-proportion renewable energy access as described in claim 1, characterized in that, The method for obtaining the noise energy of each wave subsequence is as follows: Calculate the absolute value of the mean of all data in each fluctuation subsequence; Calculate the mean of the absolute differences between all adjacent data in each fluctuation subsequence; The sum of the absolute value and the mean is denoted as the noise energy level of each pulsar sequence.

4. The power grid flexible load collaborative optimization scheduling system for high-proportion renewable energy access as described in claim 1, characterized in that, The method for obtaining the total energy of each wave subsequence is as follows: Calculate the mean of the absolute values ​​of all data in each fluctuation subsequence; Calculate the mean of the absolute differences between the absolute values ​​of all adjacent data in each fluctuation subsequence; The sum of the two means is denoted as the total energy of each wavelet sequence.

5. The power grid flexible load collaborative optimization scheduling system for high-proportion renewable energy access as described in claim 1, characterized in that, The low-frequency oscillation energy level of each wave subsequence refers to the difference between the total energy level of each wave subsequence and the noise energy level.

6. The power grid flexible load collaborative optimization scheduling system for high-proportion renewable energy access as described in claim 1, characterized in that, The formula for calculating the low-frequency oscillation time variation during the current monitoring period is as follows: In the formula, This indicates the time-varying degree of low-frequency oscillations during the current monitoring period; This represents the slope of the fitted straight line of the low-frequency oscillation energy sequence during the current monitoring period; This represents the last data point in the low-frequency oscillation energy sequence for the current monitoring period; This refers to the sigmoid() function.

7. The power grid flexible load collaborative optimization scheduling system for high-proportion renewable energy access as described in claim 6, characterized in that, The low-frequency oscillation energy sequence for the current monitoring period refers to the sequence composed of the low-frequency oscillation energy of all fluctuation subsequences within the current monitoring period arranged in chronological order.

8. The power grid flexible load collaborative optimization scheduling system for high-proportion renewable energy access as described in claim 1, characterized in that, The adaptive Kpss gain parameter for the next monitoring period refers to the maximum value between the preset minimum Kpss gain parameter and the corrected Kpss gain parameter for the next monitoring period.

9. The power grid flexible load collaborative optimization scheduling system for high-proportion renewable energy access as described in claim 8, characterized in that, The formula for calculating the corrected Kpss gain parameter for the next monitoring period is as follows: In the formula, This indicates the corrected Kpss gain parameter for the next monitoring period; This indicates the preset Kpss gain parameter for the current monitoring period; This indicates the time-varying degree of low-frequency oscillations in the previous monitoring period; This indicates the time-varying degree of low-frequency oscillations during the current monitoring period.

10. The power grid flexible load collaborative optimization scheduling system for high-proportion renewable energy access as described in claim 1, characterized in that, In the process of suppressing low-frequency oscillations in the voltage data of new energy access points during the next monitoring period, the calculated adaptive Kpss gain parameter for the next monitoring period is used as the Kpss gain parameter of the power system stabilizer for the next monitoring period.

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