A low-frequency load shedding master station control method for a high-proportion new energy power grid

Abstract

The present invention relates to the field of power grid control technology, and in particular to a low-frequency load shedding master station control method for a high-proportion renewable energy power grid. The method comprises: collecting frequency data, performing anomaly detection on the frequency data, introducing a dynamic load shedding decision algorithm based on multi-dimensional influencing parameters upon detection of an anomaly, and obtaining a preliminary load shedding decision; generating preliminary load shedding control instructions based on the preliminary load shedding decision; and introducing a dynamic feedback adjustment mechanism based on the preliminary load shedding control instructions, gradually adjusting the preliminary load shedding decision according to grid frequency feedback and load changes, and obtaining an optimized load shedding decision. This method solves the technical problem of low efficiency and poor accuracy in frequency anomaly control in renewable energy power grids.

Description

Technical Field

[0001] The present invention relates to the field of power grid control technology, and in particular to a low-frequency load shedding master station control method for a high-proportion new energy power grid. Background Art

[0002] With the large-scale integration of renewable energy, particularly wind and solar power, the global power system is gradually evolving towards a new type of power system dominated by new energy. Against this backdrop, a high proportion of new energy power grids has gradually become the typical operating model. However, the inherent volatility, intermittency, and poor predictability of new energy sources such as wind and photovoltaic power have led to a gradual weakening of the inertial support and primary frequency regulation capabilities provided by traditional generators in the power grid, significantly reducing the overall frequency stability of the power system. Once the balance between power supply and demand is lost, the grid frequency will drop rapidly. Failure to implement effective frequency control measures in a timely manner could trigger a severe frequency collapse event, leading to power system collapse and even widespread blackouts.

[0003] In conventional power grids, when the power system frequency drops below a certain threshold, it typically relies on rotating machinery units such as thermal power and hydropower to maintain frequency stability through inertial response and primary frequency regulation. However, in scenarios with a high proportion of renewable energy, since renewable energy units generally use power electronic interfaces and lack natural inertia, their spontaneous response to frequency disturbances is poor, making them unable to provide effective inertial support and frequency regulation services. More seriously, renewable energy units may directly disconnect from the grid when experiencing a frequency drop, causing the frequency to further deteriorate, forming a positive feedback loop and rapidly deteriorating the power system's state. Therefore, the higher the proportion of renewable energy, the greater the power system's sensitivity to frequency changes, and the more urgent the reliance on fast and accurate frequency control methods.

[0004] However, the above existing technologies still have the technical problem that the new energy power grid has low efficiency and poor accuracy in controlling frequency anomalies. Summary of the Invention

[0005] The present invention provides a low-frequency load shedding master station control method for a high-proportion new energy power grid, so as to solve the technical problem of low efficiency and poor accuracy in frequency anomaly control in the new energy power grid.

[0006] The present invention provides a method for controlling a low-frequency load shedding master station for a high-proportion new energy power grid, specifically including the following technical solutions:

[0007] A method for controlling a low-frequency load shedding master station for a high-proportion new energy power grid comprises the following steps:

[0008] S1. Collect frequency data and perform anomaly detection on the frequency data. If an anomaly is found, a dynamic load reduction decision algorithm based on multi-dimensional influencing parameters is introduced to obtain a preliminary load reduction decision. Based on the preliminary load reduction decision, a preliminary load reduction control instruction is generated.

[0009] S2. Based on the preliminary load shedding control instructions, a dynamic feedback adjustment mechanism is introduced. According to the feedback of the grid frequency and the load changes, the preliminary load shedding decision is adjusted to obtain the optimized load shedding decision.

[0010] Preferably, the S1 specifically includes:

[0011] A sliding window anomaly detection model is introduced to detect anomalies in frequency data. During the anomaly detection process, a sliding window is set, the rate of change of the power grid frequency is calculated within the window, the frequency mean and standard deviation within each window are set, and the absolute value of the difference between the frequency mean and the target frequency, the standard deviation, and the absolute value of the rate of change of the power grid frequency are compared with the empirical thresholds respectively.

[0012] Preferably, the S1 specifically includes:

[0013] When the absolute value of the difference between the frequency mean and the target frequency, the standard deviation, and the absolute value of the rate of change of the grid frequency are greater than the set empirical threshold, it is determined to be abnormal.

[0014] Preferably, the S1 specifically includes:

[0015] In the process of implementing the dynamic load shedding decision algorithm based on multi-dimensional influencing parameters, the multi-dimensional influencing parameters are aggregated and normalized; a power model that requires load shedding is constructed to obtain the total target load shedding power, which is used as the input for load selection and priority scheduling in the next stage, and the total target load shedding power is corrected to obtain the corrected target load shedding power.

[0016] Preferably, the S1 specifically includes:

[0017] In the implementation process of the dynamic load reduction decision algorithm based on multi-dimensional influencing parameters, the revised target load reduction power is used as input to enter the load priority scheduling process to obtain a preliminary load reduction decision; based on the preliminary load reduction decision, a preliminary load reduction control instruction is generated, and the preliminary load reduction control instruction is issued by the master station to the substation and load-side equipment.

[0018] Preferably, the S2 specifically includes:

[0019] In the specific implementation process of the dynamic feedback adjustment mechanism, a short-period feedback detection window is set. After the initial load reduction control instruction is issued, the current frequency, the rate of change of the grid frequency, the load recovery rate, and the change in the grid power in the adjacent area are collected at each feedback cycle to form a set of feedback input parameters.

[0020] Preferably, the S2 specifically includes:

[0021] In the specific implementation process of the dynamic feedback adjustment mechanism, the feedback control error is obtained based on the feedback input parameter set and the fuzzy control and incremental error correction method to evaluate whether the current frequency deviates from the target stable value.

[0022] Preferably, the S2 specifically includes:

[0023] In the specific implementation process of the dynamic feedback adjustment mechanism, a discrimination threshold is set based on the feedback control error, the feedback control error is compared with the discrimination threshold, and whether the initial load reduction decision needs to be adjusted is determined based on the size of the error function.

[0024] Preferably, the S2 specifically includes:

[0025] When adjustment is required, additional load shedding power and restored load power are obtained, and the initial load shedding decision is adjusted based on the additional load shedding power and restored load power to obtain an optimized load shedding decision.

[0026] The beneficial effects of the technical solution of the present invention are:

[0027] 1. Through a joint detection mechanism combining high-precision frequency sampling equipment, sliding window statistics, and frequency change rate, it can effectively identify slight deviations in grid frequency, trends of accelerated decline, and frequency disturbances caused by the integration of new energy sources. This early judgment capability has higher response sensitivity and judgment accuracy than traditional simple threshold-triggered low-frequency load reduction control, effectively avoiding false triggering or delayed response.

[0028] 2. Based on multi-dimensional influencing parameters, a power model requiring load reduction is constructed, and the total target load reduction power is calculated. By introducing the frequency response sensitivity factor and the renewable energy fluctuation amplification term, the required load reduction power is dynamically adjusted, so that the load reduction target more accurately reflects the actual stability needs of the power grid. This solves the problem of load reduction power estimation deviation caused by high renewable energy fluctuations and low system inertia in the existing solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 This is a flow chart of a low-frequency load shedding master station control method for a high-proportion new energy power grid described in the present invention. DETAILED DESCRIPTION

[0030] 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.

[0031] 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.

[0032] The following describes in detail a specific scheme of a low-frequency load shedding master station control method for a high-proportion new energy power grid provided by the present invention with reference to the accompanying drawings.

[0033] Refer to the attached Figure 1 , which shows a flow chart of a low-frequency load shedding master station control method for a high-proportion new energy power grid provided by one embodiment of the present invention, the method comprising the following steps:

[0034] S1. Collect frequency data and perform anomaly detection on the frequency data. If an anomaly is found, a dynamic load reduction decision algorithm based on multi-dimensional influencing parameters is introduced to obtain a preliminary load reduction decision. Based on the preliminary load reduction decision, a preliminary load reduction control instruction is generated.

[0035] In the new energy grid, firstly, the frequency data is collected in real time by high-precision frequency measuring devices installed at each major node. The major nodes are determined by professional technicians, such as the substation outlet, the energy storage system connection point, the distributed new energy collection point, etc. The high-precision frequency measuring device adopts the existing one, and the sampling frequency is not less than 50Hz. The collected frequency data is reported to the master station through the existing IEC 61850 protocol, and the master station processes the collected frequency data. Perform preliminary noise filtering, specifically: use Kalman filter for smoothing and generate a continuous frequency change curve .

[0036] Next, we introduce the sliding window anomaly detection model. Set the sliding window size to cycles, and the sliding step is The sliding window size and sliding step size are determined according to the specific application scenario, such as , ; The frequency mean and standard deviation in each window are recorded as and Calculate the rate of change of the grid frequency within the window : ,in, It is the rate of change of the grid frequency (frequency gradient), which indicates the amplitude of the grid frequency change per unit time and is an important parameter for measuring the grid frequency stability and inertial response strength; is the current sampling time The smoothed frequency value of ; is the last sampling moment The smoothed frequency value of ; is the sampling period, i.e. the time interval, which is determined by the sampling frequency, such as ; When the following abnormal judgment conditions occur:

[0037] ,

[0038] The abnormal state of the power grid frequency is triggered and the abnormal flag is set .

[0039] in, is the target frequency, which is determined according to the specific application scenario, such as 50Hz; It is an empirical threshold, determined according to expert experience, and can be set to 0.2Hz, 0.05Hz, 0.10.1Hz / s.

[0040] Once a grid frequency anomaly is detected, the master station enters a dynamic load reduction calculation state and introduces a dynamic load reduction decision algorithm based on multi-dimensional influencing parameters. The implementation steps of the dynamic load reduction decision algorithm based on multi-dimensional influencing parameters are as follows:

[0041] The first step is to aggregate multi-dimensional influencing parameters, including: Total load power , obtained by accumulating real-time meter data; at the current moment Total power generation , including conventional power supply and new energy power generation, collected and summarized through power generation access points; at the current moment The smoothing frequency value of , frequency data after preliminary noise filtering; the rate of change of grid frequency , indicating the trend of grid frequency stability; the current proportion of renewable energy power generation , reflecting the impact intensity of new energy uncertainty, among which, It is the current moment The renewable energy power generation power represents the total active power actually output by all renewable energy power generation units in the power grid (such as wind farms, photovoltaic power stations, biomass power generation, tidal energy, etc.), which is collected and obtained through the existing master station dispatching system. It is the current moment The total power generation power of the power grid represents the total active power output of all power generation resources (including thermal power, hydropower, nuclear power, energy storage output, and all new energy sources). It represents the current power generation capacity of the entire power grid and is the basis for calculating the supply and demand relationship and frequency change trend of the power grid. It is the current moment The proportion of renewable energy power generation indicates the proportion of renewable energy in the total power generation of the power grid; the priority parameter of any load unit , based on preset rules and user declaration information, determined according to expert experience, such as ,when When , it indicates an absolutely rigid load. Load shedding will cause safety / economic disasters and will not participate in load shedding. For example, medical institutions (operating rooms, elevators), emergency communication equipment, and data center hosts are not included. When the load is reduced, a soft load reduction threshold can be set to reduce the load only in the event of an extreme grid frequency collapse warning, such as the city water supply plant, rail transit system master control, semiconductor plant constant temperature system, etc. When the load is reduced within the specified response time, advance notice or interval restoration is required, such as key processes of general industrial enterprises, fire protection facilities of residential buildings, and air conditioning systems in office areas. When the system is in operation, it can be interrupted quickly, with little impact on production and life, and fast response speed. It is often used as a primary response load pool, such as ordinary industrial loads, cold chain logistics, and supermarket lighting systems. When the load unit is set to low priority by default, any abnormal grid frequency situation can be directly cut off and configured in the fast response load list, such as landscape lighting, loads that can be delayed, and loads that can be interrupted by users themselves; the allowed time response window for load reduction , which represents the acceptable delayed load shedding time for different loads, for reference when allocating strategies, and is determined based on specific application scenarios using expert experience.

[0042] The multi-dimensional influencing parameters are normalized to ensure dimensional consistency. The normalization process is a technical means well known to those skilled in the art and will not be described in detail here.

[0043] The second step is to construct a power model that requires load shedding. When the power grid is unbalanced, in order to restore the grid frequency within a unit time, it is necessary to reduce some loads to reduce the difference between supply and demand. The power model that requires load shedding is a composite calculation formula obtained based on the above multi-dimensional influencing parameters, the existing power system power-frequency dynamic balance model and the frequency response sensitivity analysis theory:

[0044] ,

[0045] in, The power value that needs to be reduced, that is, the total target load reduction power, is the power that needs to be reduced from the load in order to restore the frequency balance when the power grid faces frequency fluctuations; It is the total load power of the power grid at the current moment, that is, the sum of the real-time power demand of all users in the power grid, obtained by real-time sampling and aggregation of all loads; The frequency response sensitivity factor is used to control the degree of influence of the rate of change of the grid frequency on the load shedding decision. It is determined based on expert experience and has a reference value range of 0.5 to 2.0. The new energy fluctuation compensation coefficient is used to measure the impact of new energy power generation fluctuations on load shedding power demand. It is adjusted based on the proportion of new energy power generation and fluctuation characteristics using expert experience, with a reference value range of 0.1 to 1.0; It is the current supply-demand gap of the power grid, indicating the difference between the load demand of the power grid and the power generation supply, and is the basic load reduction demand; It is the penalty factor for steep frequency drop. When the grid frequency drops rapidly, the rate of change of the grid frequency increases, the factor becomes smaller, thereby increasing the required load shedding power; It is an amplification factor of renewable energy volatility. When renewable energy power generation accounts for a high proportion, load shedding reserves are increased to prevent instantaneous power outages. In the case of frequency fluctuations in the power grid, the required load shedding power is adjusted by calculating the supply-demand gap and taking into account the speed of frequency change, thereby achieving stable frequency control; It indicates that the load shedding power required to restore the grid frequency balance is calculated by comprehensively considering the impact of the grid supply and demand gap, frequency changes and renewable energy fluctuations;

[0046] The total target load reduction power is finally output through the above formula , the total target load shedding power Serves as input for load selection and priority scheduling in the next stage.

[0047] The third step is to calculate the total target load reduction power based on the load forecast correction model and dynamic response adjustment factor model. Make corrections to obtain the corrected target load reduction power, the calculation formula is:

[0048] ,

[0049] in, is the corrected target load reduction power; The dynamic response adjustment coefficient is a parameter that adjusts the sensitivity of renewable energy fluctuations (such as unstable power sources such as wind power and photovoltaic power generation). It reflects the response strength of the power grid to renewable energy fluctuations. It is determined by expert experience based on the characteristics of the power grid and the amount of renewable energy connected. The reference value range is 0.5 to 2.0. is the new energy fluctuation response coefficient, that is, the dynamic response of new energy, which means at the time point The impact of renewable energy power generation fluctuations on load shedding power adjustment is measured, which measures the relationship between the volatility of renewable energy power output and the response demand of the power grid. It is dynamically calculated based on wind power, solar power and other power generation data and data predicted by existing meteorological models, with a reference value range of -0.2 to +0.3; Indicates the impact of renewable energy generation fluctuations on grid load shedding power, and uses Adjust the intensity of the grid's response to this impact;

[0050] The purpose of the above process is to avoid blind load reduction and take into account the utilization rate of new energy and the stability of grid frequency.

[0051] The revised target load reduction power As input, enter the load priority scheduling process. First, filter out the loads with a total capacity not less than the revised target load reduction power from the existing load database. The low priority load set , where any load unit can be used Indicates, including current operating power , priority parameters of any load unit , the allowable time response window for load shedding , area / feeder number, etc.; is the total number of load cells; is the index of the load unit; further use the multidimensional data sorting algorithm to calculate the The comprehensive dispatch priority weight of each load unit ; and all adjustable load units according to Arrange in descending order and select in sequence until the accumulated power is greater than or equal to , forming a load shedding candidate set , which is the preliminary load shedding decision, specifically, reducing the load units in the load shedding candidate set according to the comprehensive scheduling priority weight.

[0052] According to the load unit area, voltage level, execution interface protocol, etc. in the preliminary load shedding decision, preliminary load shedding control instructions are further packaged and generated. Furthermore, the master station sends preliminary load shedding control instructions to the substation and load-side equipment to achieve preliminary load shedding response.

[0053] S2. Based on the preliminary load shedding control instructions, a dynamic feedback adjustment mechanism is introduced. According to the feedback of the grid frequency and the load changes, the preliminary load shedding decision is adjusted to obtain the optimized load shedding decision.

[0054] At the master station, after the initial load reduction control command is executed, various factors in the power grid are still in a dynamic state of change, such as continuous fluctuations in renewable energy power generation, sudden recovery of local loads, and the effectiveness of neighboring area collaborative control strategies. Although the initial load reduction decision can quickly alleviate the risk of grid frequency decline, it may not be able to maintain the frequency stable near the target stable value. Therefore, a dynamic feedback adjustment mechanism is introduced to optimize the initial load reduction decision in real time, thereby forming the final low-frequency load reduction control closed loop, obtaining the optimized load reduction decision, and ensuring that the grid frequency is stable within the judgment threshold set according to the expert experience method. The specific implementation process is as follows:

[0055] After issuing the initial load reduction control command, the master station sets a short-cycle feedback detection window based on expert experience. For example, the feedback cycle is set to , that is, when the initial load reduction control instruction is issued After time, each feedback cycle , collect the current moment again Frequency of feedback 、Current moment The rate of change of the secondary feedback grid frequency 、Current moment Load recovery rate of secondary feedback And the current moment Secondary feedback of power change in adjacent area power grid , forming a set of feedback input parameters .in, , which is based on elastic load modeling theory and is used to evaluate the rebound or transfer effect of loads. It is the current moment Load recovery rate of secondary feedback; It is the current moment Total load power of secondary feedback; It is issued Total load power after load shedding;

[0056] The master station inputs a set of parameters based on the above feedback And the existing fuzzy control and incremental error correction method, define the feedback control error The calculation formula is used to evaluate whether the current frequency deviates from the target stable value , the specific calculation formula is:

[0057] ,

[0058] in, It is The secondary feedback control error is a comprehensive indicator indicating the deviation of the current frequency from the target stable value; The target stability value is the expected frequency of the power grid, which is preset based on expert experience, such as 50Hz / 60Hz; is the feedback adjustment coefficient, which is used to balance the influence of the rate of change of grid frequency, the current load recovery rate and the change of power in adjacent areas on the overall feedback control error. It is set according to expert experience, such as .

[0059] Furthermore, based on the feedback control error, the discrimination threshold is preset according to the expert experience method. , such as 0.05, compare the feedback control error with the discrimination threshold, and judge whether the initial load reduction decision needs to be adjusted based on the size of the error function. If , then the current grid frequency is considered to be basically stable and the initial load reduction decision is not adjusted; if , that is, the grid frequency is still lower than the target, the load shedding is insufficient, and additional load shedding power is required: , based on the proportional feedback regulator (P-Control) model, where It is to add load shedding power. It is the proportional coefficient of additional load shedding power, determined according to expert experience, with a reference value range of 0.2 to 0.5; if , that is, the grid frequency has risen too fast or the load has been reduced too much, and some loads need to be gradually restored: , based on the proportional feedback regulator (P-Control) model, where is the restored load power, It is the proportional coefficient of restored load power, determined based on expert experience, with a reference value range of 0.2 to 0.5.

[0060] Furthermore, if adjustment is required, additional load shedding power is obtained. and restore load power ,Based on the additional load shedding power and the restored load power, the ,initial load shedding decision is adjusted to obtain the optimized load shedding ,decision and realize the low-frequency load shedding master station control.

[0061] In summary, a low-frequency load shedding master station control method for a high-proportion new energy power grid has been completed.

[0062] 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.

[0063] 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.

[0064] 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 low-frequency load shedding master station control method for a high-proportion new energy power grid, characterized in that: The following steps are involved: S1. Frequency data is collected and anomaly detected using a sliding window anomaly detection model. Upon detection of anomalies, a dynamic load shedding decision algorithm based on multi-dimensional influencing parameters is introduced. A power model for load shedding is constructed to determine the total target load shedding power. This target load shedding power is then revised and a preliminary load shedding decision is made based on the revised target load shedding power. Based on the preliminary load shedding decision, a preliminary load shedding control instruction is generated; In the process of building a power model that requires load shedding, based on the power system power-frequency dynamic balance model and frequency response sensitivity analysis theory, a composite calculation formula is obtained: ， in, Indicates the power value required for load shedding, i.e. the total target load shedding power; is the total load power of the power grid at the current moment; It is the current moment The total power generation capacity of the power grid; is the frequency response sensitivity factor; is the rate of change of the grid frequency; is the new energy fluctuation compensation coefficient; It is the current moment the proportion of renewable energy power generation; Based on the load forecast correction model and dynamic response adjustment factor model, the total target load reduction power Make corrections to obtain the corrected target load reduction power, the calculation formula is: ， in, is the corrected target load reduction power; is the dynamic response adjustment coefficient; is the new energy fluctuation response coefficient; S2. Based on the preliminary load shedding control instructions, a dynamic feedback adjustment mechanism is introduced. According to the feedback of the grid frequency and the load changes, the preliminary load shedding decision is adjusted to obtain the optimized load shedding decision.

2. A low-frequency load shedding master station control method for a high-proportion new energy power grid according to claim 1, characterized in that: Said S1 specifically includes: During the anomaly detection process, a sliding window is set, the rate of change of the grid frequency is calculated within the window, the frequency mean and standard deviation within each window are set, and the absolute value of the difference between the frequency mean and the target frequency, the standard deviation, and the absolute value of the rate of change of the grid frequency are compared with the empirical thresholds respectively.

3. A method for controlling low-frequency load shedding master stations for a high-proportion new energy power grid according to claim 2, characterized in that: Said S1 specifically includes: When the absolute value of the difference between the frequency mean and the target frequency, the standard deviation, and the absolute value of the rate of change of the grid frequency are greater than the set empirical threshold, it is determined to be abnormal.

4. The method for controlling low-frequency load shedding master stations for a high-proportion new energy power grid according to claim 1, characterized in that: Said S1 specifically includes: In the process of implementing the dynamic load shedding decision algorithm based on multi-dimensional influencing parameters, the multi-dimensional influencing parameters are aggregated and normalized; a power model that requires load shedding is constructed to obtain the total target load shedding power, which is used as the input for load selection and priority scheduling in the next stage. The total target load shedding power is corrected based on the load forecast correction model and the dynamic response adjustment factor model to obtain the corrected target load shedding power.

5. A method for controlling low-frequency load shedding master stations for a high-proportion new energy power grid according to claim 4, characterized in that: Said S1 specifically includes: In the implementation process of the dynamic load shedding decision algorithm based on multi-dimensional influencing parameters, the revised target load shedding power is used as input to enter the load priority scheduling process. The preliminary load shedding decision is obtained through the screened low-priority load set and the calculated comprehensive scheduling priority weight; based on the preliminary load shedding decision, a preliminary load shedding control instruction is generated, and the preliminary load shedding control instruction is issued by the master station to the substation and load-side equipment.

6. The method for controlling low-frequency load shedding master stations for a high-proportion new energy power grid according to claim 1, characterized in that: Said S2 specifically includes: In the specific implementation process of the dynamic feedback adjustment mechanism, a short-period feedback detection window is set. After the initial load reduction control instruction is issued, the current frequency, the rate of change of the grid frequency, the load recovery rate, and the change in the grid power in the adjacent area are collected at each feedback cycle to form a set of feedback input parameters.

7. A method for controlling low-frequency load shedding master stations for a high-proportion new energy power grid according to claim 6, characterized in that: Said S2 specifically includes: In the specific implementation process of the dynamic feedback adjustment mechanism, the feedback control error is obtained based on the feedback input parameter set and the fuzzy control and incremental error correction method to evaluate whether the current frequency deviates from the target stable value.

8. A method for controlling low-frequency load shedding master stations for a high-proportion new energy power grid according to claim 7, characterized in that: Said S2 specifically includes: In the specific implementation process of the dynamic feedback adjustment mechanism, a discrimination threshold is set based on the feedback control error, the feedback control error is compared with the discrimination threshold, and whether the initial load reduction decision needs to be adjusted is determined based on the size of the error function.

9. A method for controlling low-frequency load shedding master stations for a high-proportion new energy power grid according to claim 8, characterized in that: Said S2 specifically includes: When adjustment is required, additional load shedding power and restored load power are obtained, and the initial load shedding decision is adjusted based on the additional load shedding power and restored load power to obtain an optimized load shedding decision.

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