Low-frequency load shedding master station control method for high-proportion new energy power grid

Abstract

The invention relates to the technical field of power grid control, in particular to a low-frequency load shedding master station control method for a high-proportion new energy power grid. Comprising the steps of collecting frequency data, performing anomaly detection on the frequency data, and introducing a dynamic load shedding decision algorithm based on multi-dimensional influence parameters after an anomaly is found to obtain a preliminary load shedding decision; generating a preliminary load shedding control instruction based on the preliminary load shedding decision; and based on the initial load shedding control instruction, introducing a dynamic feedback adjustment mechanism, and gradually adjusting the initial load shedding decision according to the feedback of the power grid frequency and the load change condition to obtain an optimized load shedding decision. The technical problem that a new energy power grid is low in frequency anomaly control efficiency and poor in accuracy is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid control, and particularly 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 access of renewable energy, especially wind energy and solar energy, the global power system is gradually evolving towards a new power system with new energy as the main body. Under this background, high-proportion new energy power grids have gradually become a typical operation form. However, new energy power sources such as wind power and photovoltaic power have natural volatility, intermittency, and weak predictability, resulting in a gradual weakening of the inertial support and primary frequency regulation capabilities provided by traditional generating units in the power grid, and a significant reduction in the overall frequency stability of the power system. Once the power supply and demand are unbalanced, the power grid frequency will drop rapidly. If effective frequency control measures are not implemented in time, serious frequency collapse events may be triggered, leading to the disintegration of the power system and even large-scale power outages.

[0003] In a conventional power grid, when the power system frequency drops to a certain threshold, it usually relies on rotating machinery units such as thermal power and hydropower to maintain frequency stability through inertial response and primary frequency regulation means. However, in a high-proportion new energy scenario, since new energy units generally have a power electronic interface and do not have natural inertia, their spontaneous response ability to frequency disturbances is poor, and they cannot provide effective inertial support and frequency regulation services. More seriously, new energy units may directly trip off the grid when encountering frequency drops, causing the frequency to deteriorate further, forming a positive feedback closed loop and rapidly deteriorating the power system state. Therefore, the higher the proportion of new energy, the stronger the sensitivity of the power system to frequency changes, and the more urgent the dependence on fast and accurate frequency control measures.

[0004] However, the above existing technologies still have the technical problems of low control efficiency and poor accuracy for frequency anomalies in new energy power grids. 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 to solve the technical problems of low control efficiency and poor accuracy for frequency anomalies in new energy power grids.

[0006] A low-frequency load shedding master station control method for a high-proportion new energy power grid according to the present invention specifically includes the following technical solutions: A low-frequency load shedding master station control method for a high-proportion new energy power grid includes the following steps: S1. Collect frequency data, perform anomaly detection on the frequency data, introduce a dynamic load shedding decision-making algorithm based on multi-dimensional influence parameters after discovering anomalies to obtain a preliminary load shedding decision; generate a preliminary load shedding control instruction based on the preliminary load shedding decision; S2. Based on the preliminary load shedding control instruction, introduce a dynamic feedback adjustment mechanism, and adjust the preliminary load shedding decision according to the feedback of the grid frequency and the load change situation to obtain an optimized load shedding decision.

[0007] Preferably, the S1 specifically includes: Introduce an anomaly detection model with a sliding window to detect anomalies in frequency data. During the anomaly detection process, set a sliding window, calculate the change rate of the grid frequency within the window, set the frequency mean and standard deviation within each window, and compare the absolute value of the difference between the frequency mean and the target frequency, the standard deviation, and the absolute value of the change rate of the grid frequency with the empirical threshold respectively.

[0008] Preferably, the 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 change rate of the grid frequency are respectively greater than the set empirical threshold, it is determined as an anomaly.

[0009] Preferably, the S1 specifically includes: In the implementation process of the dynamic load shedding decision algorithm based on multi-dimensional influence parameters, converge the multi-dimensional influence parameters and perform normalization processing on them; construct a power model that needs to be shed, obtain the total target load shedding power, use the total target load shedding power as the input for the next-stage load selection and priority scheduling, and correct the total target load shedding power to obtain the corrected target load shedding power.

[0010] Preferably, the S1 specifically includes: In the implementation process of the dynamic load shedding decision algorithm based on multi-dimensional influence parameters, use the corrected target load shedding power as the input, enter the load priority scheduling process to obtain a preliminary load shedding decision; based on the preliminary load shedding decision, generate a preliminary load shedding control instruction, and the master station issues the preliminary load shedding control instruction to the slave station and the load-side equipment.

[0011] Preferably, the S2 specifically includes: In the specific implementation process of the dynamic feedback adjustment mechanism, set a short-cycle feedback detection window. After the preliminary load shedding control instruction is issued, every feedback cycle, collect the current frequency, the change rate of the grid frequency, the load recovery rate, and the power change amount of the adjacent regional grid at the current moment to form a feedback input parameter set.

[0012] Preferably, the S2 specifically includes: In the specific implementation process of the dynamic feedback adjustment mechanism, based on the feedback input parameter set and the fuzzy control and incremental error correction method, obtain the feedback control error and evaluate whether the current frequency deviates from the target stable value.

[0013] Preferably, the 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 it is determined whether the initial load shedding decision needs to be adjusted according to the magnitude of the error function.

[0014] Preferably, S2 specifically includes: In the case of adjustment required, the additional load shedding power and the restored load power are obtained. Based on the additional load shedding power and the restored load power, the initial load shedding decision is adjusted to obtain an optimized load shedding decision.

[0015] The beneficial effects of the technical solution of the present invention are: 1. Through the combined detection mechanism of high-precision frequency sampling equipment, sliding window statistical method, and frequency change rate, it is possible to effectively identify the slight deviation of the power grid frequency, the trend of accelerating decline, and the frequency disturbance trend caused by the access of new energy. This early judgment ability has higher response sensitivity and judgment accuracy compared with the traditional low-frequency load shedding control triggered by a simple threshold, effectively avoiding mis-triggering or lagging response.

[0016] 2. Based on multi-dimensional influence parameters, a power model for load shedding is constructed, and the total target load shedding power is calculated. By introducing the frequency response sensitivity factor and the new energy fluctuation amplification term, the required load shedding power is dynamically adjusted, enabling the load shedding target to more accurately reflect the true stability demand of the power grid, and solving the problem of load shedding power estimation deviation caused by high new energy fluctuations and low system inertia in the existing solutions. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flowchart of a low-frequency load shedding master station control method for a high-proportion new energy power grid according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0020] The specific solution of a low-frequency load shedding master station control method for a high-proportion new energy power grid provided by the present invention will be specifically described below with reference to the accompanying drawings.

[0021] Referring to the attached Figure 1 , which shows a flowchart of a low-frequency load shedding master station control method for a high-proportion new energy power grid provided by an embodiment of the present invention. The method includes the following steps: S1. Collect frequency data, perform anomaly detection on the frequency data, introduce a dynamic load shedding decision algorithm based on multi-dimensional influence parameters after detecting anomalies, and obtain a preliminary load shedding decision; generate a preliminary load shedding control instruction based on the preliminary load shedding decision; In a new energy power grid, first, collect frequency data in real time through high-precision frequency measurement devices installed at each main node. The main nodes are determined by professional technical personnel, such as the outlet end of a substation, the connection point of an energy storage system, and the collection point of distributed new energy. The high-precision frequency measurement devices are existing ones, and the sampling frequency is not lower than 50Hz. And report the collected frequency data to the master station through the existing IEC 61850 protocol. The master station performs preliminary noise filtering processing on the collected frequency data. Specifically, a Kalman filter is used for smoothing to generate a continuous frequency change curve .

[0022] Next, introduce an anomaly detection model with a sliding window. Set the size of the sliding window to cycles, and the sliding step size to . The size of the sliding window and the sliding step size are determined according to specific application scenarios, such as , ; record the frequency mean and standard deviation within each window as and . Calculate the change rate of the grid frequency within the window: , where is the change rate (frequency gradient) of the grid frequency, representing the change amplitude of the grid frequency per unit time, and is an important parameter for measuring the grid frequency stability and inertia response intensity; is the smoothed frequency value at the current sampling time ; is the smoothed frequency value at the previous sampling time ; is the sampling period, that is, the time interval, determined according to the sampling frequency, such as ; when the following anomaly determination conditions occur: , then trigger the abnormal state of the grid frequency and set the anomaly flag bit .

[0023] Among them, is the target frequency, determined according to specific application scenarios, such as 50Hz; is an empirical threshold, determined according to the expert experience method, and can be set to 0.2Hz, 0.05Hz, 0.10.1Hz / s.

[0024] Once the abnormal grid frequency is detected, the master station enters the dynamic load shedding calculation state, and introduces a dynamic load shedding decision algorithm based on multi-dimensional influence parameters. The implementation steps of the dynamic load shedding decision algorithm based on multi-dimensional influence parameters are as follows: The first step is to converge multi-dimensional influence parameters, including: the total load power at the current moment , obtained by accumulating real-time electricity meter data; the total power generation at the current moment , including conventional power sources and new energy power generation, collected and summarized through power generation access points; the smoothed frequency value at the current moment , frequency data after preliminary noise filtering; the change rate of the grid frequency , indicating the stability trend of the grid frequency; the current new energy power generation ratio , reflecting the influence intensity of new energy uncertainty. Among them, is the new energy power generation at the current moment , representing the total active power actually output by all new energy power generation units (such as wind farms, photovoltaic power stations, biomass power generation, tidal energy, etc.) in the grid, obtained through the existing master station dispatching system, is the total grid power generation at the current moment , representing the total active power output of all power generation resources (including thermal power, hydropower, nuclear power, energy storage output, and all new energy), representing the current power generation capacity of the entire grid, and is the basis for calculating the grid supply-demand relationship and frequency change trend, is the new energy power generation ratio at the current moment , indicating the proportion of new energy in the total grid power generation; the priority parameter of any load unit , determined according to the preset rules and user declaration information by the expert experience method. For example, when , it represents an absolutely rigid load, and load shedding will cause safety / economic disasters and is not allowed to participate in load shedding, such as medical institutions (operating rooms, elevators), emergency communication equipment, and data center hosts. When , a soft load shedding threshold can be set, and only partial load shedding is allowed in the extreme grid frequency collapse warning, such as urban water supply plants, main control of rail transit systems, and constant temperature systems in semiconductor factories. When , load shedding can be carried out within the specified response time, and advance notice or interval restoration is required during regulation, such as key processes in general industrial enterprises, fire protection facilities in residential buildings, and air conditioning systems in office areas. When , it can be load shed within the specified response time, and advance notice or interval restoration is required during regulation, such as key processes in general industrial enterprises, fire protection facilities in residential buildings, and air conditioning systems in office areas. When , a soft load shedding threshold can be set, and only partial load shedding is allowed in the extreme grid frequency collapse warning, such as urban water supply plants, main control of rail transit systems, and constant temperature systems in semiconductor factories. When , load shedding can be carried out within the specified response time, and advance notice or interval restoration is required during regulation, such as key processes in general industrial enterprises, fire protection facilities in residential buildings, and air conditioning systems in office areas. When When there is a situation, it can be quickly interrupted, with little impact on production and life, fast response speed, and is often used as a primary response load pool, such as ordinary industrial loads, cold chain logistics, and supermarket lighting systems. When When it is in a certain state, the low priority of the default load unit can be directly cut off in any abnormal power grid frequency situation, and it is configured in the fast response load list, such as landscape lighting, delayable operation loads, and user-reported interruptible loads; the allowable time response window for load shedding represents the delay load shedding time that different loads can accept, which is for reference in strategy allocation and is determined according to specific application scenarios using the expert experience method.

[0025] Normalize the multi-dimensional influence parameters to ensure consistent dimensions. The normalization process is a well-known technical means for those skilled in the art and will not be elaborated here.

[0026] The second step is to construct a power model for load shedding. When the power grid is unbalanced, in order to restore the power grid frequency within a unit time, a part of the load needs to be cut to reduce the difference between supply and demand. The power model for load shedding is a composite calculation formula obtained based on the above multi-dimensional influence parameters, the existing power system power-frequency dynamic balance model, and the frequency response sensitivity analysis theory: , where represents the power value that needs to be shed, that is, the total target load shedding power, which is the power that needs to be cut from the load when the power grid faces frequency fluctuations to restore frequency balance; is the total load power of the power grid at the current moment, that is, the sum of the real-time electricity consumption demands of all users in the power grid, which is obtained by summarizing the real-time sampling of all loads; is the frequency response sensitivity factor, which is used to control the influence degree of the change rate of the power grid frequency on the load shedding decision, determined by the expert experience method, and the reference value range is 0.5 to 2.0; is the new energy fluctuation compensation coefficient, which is used to measure the influence of new energy power generation fluctuations on the load shedding power demand, adjusted by the expert experience method according to the proportion of new energy power generation and its fluctuation characteristics, and the reference value range is 0.1 to 1.0; is the current power grid supply-demand gap, which represents the difference between the power grid load demand and power generation supply, and is the basic load shedding demand; is the penalty factor for rapid frequency drop. When the power grid frequency drops rapidly, the change rate of the power grid frequency increases, then this factor becomes smaller, thereby increasing the required load shedding power; is the amplification term for new energy volatility. When the proportion of new energy power generation is relatively high, increase the load shedding reserve to prevent instantaneous power failure; It means that in the case of the power grid facing frequency fluctuations, the required load shedding power is adjusted by calculating the supply-demand gap and combining the speed of frequency change, so as to achieve stable control of the frequency. It means that considering the impacts of the power grid supply-demand difference, frequency change and new energy fluctuation, the load shedding power required to restore the power grid frequency balance is calculated. The total target load shedding power is finally output through the above formula. The total target load shedding power is used as the input quantity for load selection and priority scheduling in the next stage.

[0027] In the third step, based on the load forecasting correction model and the dynamic response adjustment factor model, the total target load shedding power is corrected to obtain the corrected target load shedding power, and the calculation formula is: , where, is the corrected target load shedding power; is the dynamic response adjustment coefficient, which is a parameter for adjusting the sensitivity of new energy fluctuations (such as unstable power sources like wind energy and photovoltaic energy), reflecting the response intensity of the power grid to new energy fluctuations, and is determined by using the expert experience method according to the characteristics of the power grid and the new energy access amount, and the reference value range is from 0.5 to 2.0; is the new energy fluctuation response coefficient, that is, the new energy dynamic response, which means at the time point , the influence degree of the new energy power generation fluctuation on the load shedding power adjustment, measuring the relationship between the new energy power output volatility and the response demand of the power grid, and is dynamically calculated from the power generation data of wind energy, solar energy, etc. and the data predicted by the existing meteorological model, and the reference value range is from -0.2 to +0.3; represents the influence degree of the new energy power generation fluctuation on the power grid load shedding power, and adjusts the response intensity of the power grid to this influence through ; The purpose of the above process is to avoid blind load shedding and take into account the new energy utilization rate and the power grid frequency stability.

[0028] Taking the corrected target load shedding power as the input, it enters the load priority scheduling process. First, a set of low-priority loads with a total capacity not less than the corrected target load shedding power is screened out from the existing load database , and any one load unit can be represented by , including the current operating power , the priority parameter of any load unit, the allowable time response window for load shedding, the affiliated area / feeder number, etc.; is the total number of load units; is the index of the load unit; further calculate the comprehensive scheduling priority weight of the th load unit by using the multi-dimensional data sorting algorithm ; and arrange all adjustable load units in descending order, and select them in turn until the cumulative power meets or exceeds , to form a load shedding candidate set , which is the preliminary load shedding decision, specifically to shed the load of the load units in the load shedding candidate set according to the comprehensive scheduling priority weight.

[0029] According to the region, voltage level, execution interface protocol, etc. to which the load units in the preliminary load shedding decision belong, further package to generate the preliminary load shedding control instruction. Further, the master station sends the preliminary load shedding control instruction to the slave station and the load side equipment to achieve the preliminary load shedding response.

[0030] S2. Based on the preliminary load shedding control instruction, introduce a dynamic feedback adjustment mechanism, and adjust the preliminary load shedding decision according to the feedback of the grid frequency and the load change situation to obtain an optimized load shedding decision.

[0031] At the master station, after the preliminary load shedding control instruction is executed, due to various factors in the power grid still being in a dynamic change state, such as the continuous fluctuation of new energy power generation, the sudden recovery of local load, the effectiveness of adjacent area collaborative control strategies, etc., although the preliminary load shedding decision can quickly alleviate the risk of grid frequency decline, it may not be able to keep the frequency stable near the target stable value. Therefore, introduce a dynamic feedback adjustment mechanism to optimize the preliminary load shedding decision in real time, so as to form a final low-frequency load shedding control closed loop, obtain an optimized load shedding decision, and ensure that the grid frequency is stable within the judgment threshold set according to the expert experience method. The specific implementation process is as follows: After the master station sends the preliminary load shedding control instruction, set a short-period feedback detection window according to the expert experience method, such as setting the feedback period to , that is, after the preliminary load shedding control instruction is sent time, every interval of the feedback period , collect the frequency of the th feedback at the current moment again, the change rate of the grid frequency of the th feedback at the current moment, the load recovery rate of the th feedback at the current moment, and the power change amount of the adjacent area power grid of the th feedback at the current moment, to form a feedback input parameter set . Among them, , is obtained based on the elastic load modeling theory and is used to evaluate the rebound or transfer effect of the load, is the load recovery rate of the th feedback at the current moment; is the total load power of the th feedback at the current moment; is the total load power after load shedding at the moment; Based on the above set of feedback input parameters, the master station and the existing fuzzy control and incremental error correction method define the calculation formula of the feedback control error to evaluate whether the current frequency deviates from the target stable value , and the specific calculation formula is: , where is the th feedback control error, representing the comprehensive index of the deviation of the current frequency from the target stable value; is the target stable value, that is, the expected frequency of the power grid, preset according to the expert experience method, such as 50Hz / 60Hz; is the feedback regulation coefficient, used to balance the influence of the change rate of the power grid frequency, the current load recovery rate, and the power change amount of the adjacent regional power grid on the overall feedback control error, set according to the expert experience, such as .

[0032] Furthermore, based on the feedback control error, a discrimination threshold is preset according to the expert experience method, such as 0.05. The feedback control error is compared with the discrimination threshold, and it is judged whether the initial load shedding decision needs to be adjusted according to the size of the error function. If , it is considered that the current power grid frequency is basically stable, and the initial load shedding decision is not adjusted; if , that is, the power grid frequency is still lower than the target and the load shedding is insufficient, additional load shedding power needs to be added: , obtained based on the proportional feedback regulator (P-Control) model, where is the additional load shedding power, is the proportional coefficient of the additional load shedding power, determined according to the expert experience method, and the reference value range is 0.2 - 0.5; if , that is, the power grid frequency has risen too fast or the load shedding is excessive, and some loads need to be gradually restored: , obtained based on the proportional feedback regulator (P-Control) model, where is the restored load power, is the proportional coefficient of the restored load power, determined according to the expert experience method, and the reference value range is 0.2 - 0.5.

[0033] Furthermore, in the case of adjustment required, the additional load shedding power is obtained and the restored load power , based on the additional load shedding power and the restored load power, adjust the initial load shedding decision to obtain an optimized load shedding decision, and implement the main station control of under-frequency load shedding.

[0034] In summary, a main station control method for under-frequency load shedding in a high-proportion new energy power grid is completed.

[0035] The order of the invention embodiments is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0036] Each embodiment in this specification is described in a progressive manner. For the same or similar parts between the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments.

[0037] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments or perform equivalent replacements for some of the technical features. These modifications or replacements 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 in the protection scope 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, It includes the following steps: S1. Collect frequency data, perform anomaly detection on the frequency data. After detecting anomalies, introduce a dynamic load shedding decision algorithm based on multi-dimensional influence parameters to obtain a preliminary load shedding decision; based on the preliminary load shedding decision, generate a preliminary load shedding control instruction; S2. Based on the preliminary load shedding control instruction, introduce a dynamic feedback adjustment mechanism, and adjust the preliminary load shedding decision according to the feedback of the grid frequency and the load change situation to obtain an optimized load shedding decision.

2. The low-frequency load shedding master station control method for a high-proportion new energy power grid according to claim 1, characterized in that The specific content of S1 includes: Introduce an anomaly detection model with a sliding window to perform anomaly detection on the frequency data. During the anomaly detection process, set a sliding window, calculate the change rate of the grid frequency within the window, set the frequency mean and standard deviation within each window, and compare the absolute value of the difference between the frequency mean and the target frequency, the standard deviation, and the absolute value of the change rate of the grid frequency with the empirical thresholds respectively.

3. A low-frequency load shedding master station control method for a high-proportion new energy power grid according to claim 2, characterized in that, The specific content of S1 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 change rate of the grid frequency are respectively greater than the set empirical thresholds, it is determined as an anomaly.

4. A low-frequency load shedding master station control method for a high-proportion new energy power grid according to claim 1, characterized in that The specific content of S1 includes: During the implementation process of the dynamic load shedding decision algorithm based on multi-dimensional influence parameters, converge the multi-dimensional influence parameters and perform normalization processing on the multi-dimensional influence parameters; construct a power model that needs to be shed, obtain the total target load shedding power, use the total target load shedding power as the input for the next-stage load selection and priority scheduling, and correct the total target load shedding power to obtain the corrected target load shedding power.

5. A low-frequency load shedding master station control method for a high-proportion new energy power grid according to claim 4, characterized in that, The specific content of S1 includes: During the implementation process of the dynamic load shedding decision algorithm based on multi-dimensional influence parameters, use the corrected target load shedding power as the input, enter the load priority scheduling process to obtain a preliminary load shedding decision; based on the preliminary load shedding decision, generate a preliminary load shedding control instruction, and the master station sends the preliminary load shedding control instruction to the slave station and the load-side equipment.

6. A low-frequency load shedding master station control method for a high-proportion new energy power grid according to claim 1, characterized in that, The specific content of S2 includes: During the specific implementation process of the dynamic feedback adjustment mechanism, set a short-period feedback detection window. After the preliminary load shedding control instruction is sent, every feedback period, collect the current frequency, the change rate of the grid frequency, the load recovery rate, and the power change amount of the adjacent regional grid, and form a feedback input parameter set.

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

8. A low-frequency load shedding master station control method for a high-proportion new energy power grid according to claim 7, characterized in that, The specific content of S2 includes: During the specific implementation process of the dynamic feedback adjustment mechanism, based on the feedback control error, set a discrimination threshold, compare the feedback control error with the discrimination threshold, and judge whether it is necessary to adjust the initial load shedding decision according to the magnitude of the error function.

9. A low-frequency load shedding master station control method for a high-proportion new energy power grid according to claim 8, characterized in that, The specific content of S2 includes: In the case of adjustment required, obtain the additional load shedding power and the restored load power, and based on the additional load shedding power and the restored load power, adjust the initial load shedding decision to obtain an optimized load shedding decision.

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