A microgrid stability control method and system based on output characteristic simulation

Abstract

The present invention discloses a microgrid stability control method and system based on output characteristic simulation, the method comprising acquiring fluctuation characteristic data, meteorological condition data, operation data, load demand data and environmental parameter data; separating the fluctuation characteristic data to obtain periodic trajectory and random disturbance component; performing correlation analysis based on the periodic trajectory, random disturbance component and meteorological condition data to obtain potential impact data; obtaining an adaptability index based on the potential impact data and the operation data; if the adaptability index is lower than a preset index threshold, adjusting the energy capacity and frequency range based on the random disturbance component, operation data and load demand data to obtain a dynamic response configuration; generating an initial adjustment instruction template based on the dynamic response configuration and in combination with a preset state switching loss parameter and a preset environmental adaptability parameter; and adjusting the frequency and power parameters based on the initial adjustment instruction template and in combination with the environmental parameter data to generate a control instruction set.

Description

Technical Field

[0001] The present invention relates to the field of microgrid control technology, and in particular to a microgrid stability control method and system based on output characteristic simulation. Background Art

[0002] With the large-scale integration of renewable energy and the continuous evolution of distributed energy technologies, microgrids, as key platforms for achieving autonomous regional energy management, distributed energy coordination, and supply-demand balance, have been widely deployed in scenarios such as industrial parks, islands, and remote power grids. In particular, in areas rich in wind and solar resources, microgrids help improve local energy utilization and alleviate pressure on the main grid. However, due to the intermittent and uncertain nature of renewable energy and the dynamic nature of load demand, microgrid systems often face operational challenges such as severe power output fluctuations, large voltage and frequency deviations, and frequent sudden changes in load regulation. These characteristics require the system to possess fast and accurate response mechanisms and robust control strategies to maintain overall operational stability and economic efficiency.

[0003] Currently, some microgrid control research has employed methods such as model predictive control (MPC), fuzzy controllers, and PID with static parameter tuning to improve the system's dynamic response performance. A common technique involves establishing a fixed state model and, after real-time detection of frequency and voltage deviations, generating regulation commands for the energy storage system to promptly perform active power compensation and power allocation. Within a certain range, this approach can enhance the system's ability to handle small disturbances through short-term correction strategies. However, this type of control approach typically relies on static or preset rules and lacks the ability to analyze the source of disturbances and changing environmental conditions, making it difficult to effectively handle system disturbances driven by large-scale or complex meteorological factors.

[0004] Existing technologies mainly rely on fixed models for regulation, and lack a fusion response mechanism for dynamic feature recognition of output disturbances and environmental changes, resulting in delayed or mismatched regulation instructions of the energy storage system, making it impossible to ensure the stable operation of the microgrid under strong disturbance conditions. Summary of the Invention

[0005] The present invention provides a microgrid stability control method and system based on output characteristic simulation to achieve rapid response and precise regulation under disturbance conditions.

[0006] In a first aspect, in order to solve the above technical problems, the present invention provides a microgrid stability control method based on output characteristic simulation, comprising:

[0007] Obtain historical output fluctuation characteristic data, meteorological condition data, energy storage system operation data, load demand data and environmental parameter data;

[0008] Separating the period and amplitude of the fluctuation characteristic data to obtain the periodic trajectory and random disturbance component of the output fluctuation;

[0009] performing correlation analysis based on the periodic trajectory, the random disturbance component, and the meteorological condition data to obtain data on potential impact of fluctuations on system voltage deviation and frequency fluctuations;

[0010] Calculating, based on the potential impact data and the operating data, the degree to which the response time delay and power regulation accuracy of the energy storage system match dynamic load changes to obtain an adaptability index;

[0011] If the adaptability index is lower than a preset index threshold, decomposing disturbance characteristics according to the random disturbance component, the operation data, and the load demand data, adjusting energy capacity and frequency range, and obtaining a dynamic response configuration;

[0012] According to the dynamic response configuration, combined with preset state switching loss parameters and preset environmental adaptability parameters, a storage response behavior simulation framework is constructed to generate an initial adjustment instruction template;

[0013] Based on the initial adjustment instruction template and in combination with the environmental parameter data, the frequency and power parameters are adjusted to generate a control instruction set adapted to the current disturbance conditions.

[0014] Preferably, the separation of period and amplitude of the fluctuation characteristic data to obtain the periodic trajectory and random disturbance component of the output fluctuation includes:

[0015] constructing a time series for the fluctuation characteristic data to obtain output data in a time series form;

[0016] Perform time series decomposition based on the output data to separate the periodic term and the residual term;

[0017] According to the periodic term, frequency extraction and period length calculation are performed to obtain the periodic trajectory of output fluctuation;

[0018] According to the residual term, sliding window smoothing and amplitude threshold judgment are performed to obtain the random disturbance component of the output fluctuation.

[0019] Preferably, performing correlation analysis based on the periodic trajectory, the random disturbance component and the meteorological condition data to obtain data on potential impact of fluctuations on system voltage deviation and frequency fluctuations includes:

[0020] Extracting the fluctuation amplitude data at the corresponding time point according to the time index of the periodic trajectory and the random disturbance component;

[0021] constructing a meteorological time series based on the time index of the periodic trajectory and the random disturbance component in combination with the meteorological condition data;

[0022] constructing a correlation data set of environmental impacts based on the fluctuation amplitude data and the meteorological time series;

[0023] Performing correlation analysis on the associated data set to generate a mapping relationship matrix between the fluctuation amplitude and the meteorological variables;

[0024] If the correlation coefficient in the mapping relationship matrix exceeds a preset coefficient threshold, a simulation analysis is performed on the voltage deviation and frequency fluctuation to obtain data on the potential impact of the fluctuation on the system's real-time voltage deviation and frequency fluctuation amplitude.

[0025] Preferably, the adaptability index is obtained by calculating the degree of matching between the response time delay and the power regulation accuracy of the energy storage system to the dynamic load change based on the potential impact data and the operating data, including:

[0026] extracting a response time delay value and a power regulation output value within a corresponding time period from the operation data according to the voltage deviation and frequency fluctuation time period indicated in the potential impact data;

[0027] Comparing the response time delay value with a preset response delay threshold to calculate a response time deviation;

[0028] Comparing the power regulation output value with a preset regulation accuracy threshold to calculate a regulation accuracy deviation;

[0029] A weighted fusion process is performed according to the response time deviation and the adjustment accuracy deviation to obtain an adaptability index reflecting the degree of matching.

[0030] Preferably, if the adaptability index is lower than a preset index threshold, decomposing disturbance characteristics according to the random disturbance component, the operation data and the load demand data, adjusting energy capacity and frequency range, and obtaining a dynamic response configuration includes:

[0031] Extracting periodic fluctuations and transient disturbances according to the random disturbance component and the operating data to obtain a disturbance characteristic set;

[0032] Calculating the upper and lower limits of the current available energy capacity of the energy storage system based on the disturbance characteristic set and the operating data to obtain a capacity constraint range;

[0033] evaluating a required response bandwidth according to the disturbance characteristic set and the load demand data to obtain an adjustment frequency range;

[0034] Based on the capacity constraint range and the regulation frequency range, a dynamic response configuration matching current disturbance characteristics is generated.

[0035] Preferably, the dynamic response configuration is combined with preset state switching loss parameters and preset environmental adaptability parameters to construct an energy storage response behavior simulation framework and generate an initial adjustment instruction template, including:

[0036] extracting target output variation characteristics based on the power adjustment amplitude and response time requirements included in the dynamic response configuration;

[0037] Based on the target output variation characteristics, combined with preset state switching loss parameters and preset environmental adaptability parameters, the response behavior of the energy storage device under different output ranges and environmental conditions is simulated to generate a simulated data set of the response behavior;

[0038] In the simulation data set, identifying an abnormal operating condition in which a state switching loss value exceeds a preset loss threshold;

[0039] In response to the abnormal operating condition, an initial adjustment instruction template adapted to the current environmental conditions is generated.

[0040] Preferably, the adjusting frequency and power parameters based on the initial adjustment instruction template and in combination with the environmental parameter data to generate a control instruction set adapted to the current disturbance conditions includes:

[0041] Based on the initial adjustment instruction template, the disturbance type is identified on the environmental parameter data to determine the disturbance category and disturbance intensity of the current disturbance;

[0042] Generate an updated frequency control instruction according to the frequency adjustment rule corresponding to the disturbance category matching and the modified frequency parameter corresponding to the disturbance intensity;

[0043] Based on the frequency control instruction and in combination with the disturbance category, the power output parameter is dynamically adjusted to generate a control instruction set adapted to the current disturbance condition.

[0044] In a second aspect, the present invention provides a microgrid stability control system based on output characteristic simulation, comprising:

[0045] Data acquisition module, used to obtain historical output fluctuation characteristic data, meteorological condition data, energy storage system operation data, load demand data and environmental parameter data;

[0046] A fluctuation separation module is used to separate the period and amplitude of the fluctuation characteristic data to obtain the periodic trajectory and random disturbance component of the output fluctuation;

[0047] a potential analysis module, configured to perform a correlation analysis based on the periodic trajectory, the random disturbance component, and the meteorological condition data to obtain data on the potential impact of the fluctuation on the system voltage deviation and frequency fluctuation;

[0048] an adaptive adjustment module, configured to calculate, based on the potential impact data and the operating data, a degree of matching of the response time delay and power regulation accuracy of the energy storage system to dynamic load changes, and obtain an adaptability index;

[0049] a response configuration module, configured to decompose disturbance characteristics, adjust energy capacity and frequency range based on the random disturbance component, the operating data, and the load demand data to obtain a dynamic response configuration if the adaptability index is lower than a preset index threshold;

[0050] An adjustment instruction module is used to construct an energy storage response behavior simulation framework and generate an initial adjustment instruction template based on the dynamic response configuration, combined with preset state switching loss parameters and preset environmental adaptability parameters;

[0051] The control instruction module is used to adjust the frequency and power parameters based on the initial adjustment instruction template and in combination with the environmental parameter data to generate a control instruction set that adapts to the current disturbance conditions.

[0052] In a third aspect, the present invention also provides an electronic device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the microgrid stability control method based on output characteristic simulation as described above is implemented.

[0053] In a fourth aspect, the present invention also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned microgrid stability control methods based on output characteristic simulation.

[0054] Compared with the prior art, the present invention has the following beneficial effects:

[0055] (1) This invention achieves accurate identification of output fluctuation characteristics by constructing a separation model of periodic trajectories and disturbance components, and integrates meteorological data to establish a correlation analysis mechanism between disturbances and grid parameters. This method can effectively identify the potential impact of fluctuations on system voltage deviations and frequency fluctuations, giving the control strategy pre-perception capabilities. Through time series modeling and correlation derivation, system risks can be predicted in advance before control instructions are issued, providing a high-confidence data foundation for subsequent energy storage response decisions, thereby improving the foresight of responses to microgrid disturbances and the proactiveness of regulation.

[0056] (2) Based on the determination of fluctuation impact, the present invention further introduces the operating data of the energy storage system, establishes a deviation evaluation mechanism for response time delay and power regulation accuracy, and integrates load demand to perform multi-dimensional matching analysis. Compared with traditional static setting control, this method adopts a weighted fusion algorithm to comprehensively consider system capabilities and load dynamics, realizes the quantitative calculation of adaptability indicators, and triggers parameter reconstruction of disturbance response accordingly. This mechanism not only improves control accuracy, but also avoids resource mismatch and response lag, effectively enhancing the operational stability and energy efficiency synergy of the energy storage system under complex disturbances.

[0057] (3) For the generation and optimization of control instructions, the present invention constructs a storage behavior simulation framework based on dynamic response configuration, introduces a state switching loss and environmental adaptability parameter evaluation mechanism, and iteratively updates the adjustment template. During the simulation process, high-loss working conditions can be identified and targeted initial instruction templates can be generated. Then, disturbance classification identification and dynamic adjustment of frequency and power parameters are performed in combination with real-time environmental parameters. This method realizes the adaptive optimization of control instructions to the current disturbance working conditions, which not only improves the efficiency of instruction execution, but also ensures the control accuracy under environmental changes such as temperature fluctuations and humidity disturbances, and enhances the robustness and adjustment flexibility of the overall microgrid system. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 1 is a flow chart of a microgrid stability control method based on output characteristic simulation provided by the first embodiment of the present invention;

[0059] Figure 2 2 is a schematic structural diagram of a microgrid stability control system based on output characteristic simulation provided by the second embodiment of the present invention. DETAILED DESCRIPTION

[0060] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0061] Reference Figure 1 The first embodiment of the present invention provides a microgrid stability control method based on output characteristic simulation, comprising the following steps:

[0062] S11, obtaining historical output fluctuation characteristic data, meteorological condition data, energy storage system operation data, load demand data, and environmental parameter data;

[0063] S12, separating the period and amplitude of the fluctuation characteristic data to obtain the periodic trajectory and random disturbance component of the output fluctuation;

[0064] S13, performing correlation analysis based on the periodic trajectory, the random disturbance component, and the meteorological condition data to obtain data on potential impact of fluctuations on system voltage deviation and frequency fluctuations;

[0065] S14, calculating, based on the potential impact data and the operating data, the degree to which the response time delay and power regulation accuracy of the energy storage system match the dynamic load change to obtain an adaptability index;

[0066] S15, if the adaptability index is lower than a preset index threshold, decomposing disturbance characteristics according to the random disturbance component, the operation data, and the load demand data, adjusting energy capacity and frequency range, and obtaining a dynamic response configuration;

[0067] S16, constructing an energy storage response behavior simulation framework based on the dynamic response configuration, combined with preset state switching loss parameters and preset environmental adaptability parameters, and generating an initial adjustment instruction template;

[0068] S17, based on the initial adjustment instruction template and in combination with the environmental parameter data, adjusting the frequency and power parameters to generate a control instruction set adapted to the current disturbance condition.

[0069] In step S11, it is necessary to obtain historical output fluctuation characteristic data, meteorological condition data, energy storage system operation data, load demand data, and environmental parameter data, including:

[0070] In one specific embodiment, historical output fluctuation characteristic data is obtained by connecting to the operation logs of various renewable energy devices in the microgrid. This mainly includes records of solar radiation intensity and output current of photovoltaic modules, and the speed and power generation trends of wind turbines. The data source can be an on-site energy management system (EMS) or an edge measurement and control terminal, with a sampling period of 1 to 5 minutes. This type of data is used to reflect the output fluctuation trends of renewable energy in different time periods, providing basic data support for subsequent time series analysis and disturbance decomposition.

[0071] Specifically, meteorological condition data can be collected through meteorological sensor nodes deployed within the microgrid's operating area. Data items include external temperature, humidity, wind speed, light intensity, and rainfall. Wind speed and light intensity are direct drivers of photovoltaic and wind power volatility, while temperature and humidity have indirect effects on the energy storage system and its response efficiency. For example, by accessing historical data from corresponding sites through the National Meteorological Data Sharing Platform or deploying local automatic weather stations, a series of meteorological changes recorded at 10-minute intervals over a year can be obtained to support subsequent correlation analysis.

[0072] For example, the energy storage system's operating data can be recorded in real time by the battery management system (BMS) and power conversion system (PCS). This data includes indicators such as SOC (state of charge), battery output power, charge-discharge conversion efficiency, current response time, and historical regulation records. For example, a lithium-ion energy storage system's BMS reports voltage, current, and SOC curves every 30 seconds. This data can be used to assess the energy storage system's current energy reserves and regulation capabilities, providing a basis for determining whether the system can meet fluctuating demand.

[0073] It's important to note that load demand data relies on load monitoring devices or smart meters in the distribution network. These data include the load current, voltage, energy, and active / reactive power demand at each node. For example, in a factory microgrid, the load distribution includes lighting systems, air conditioners, and motor equipment. This data is reported every minute via the building energy management platform, with historical data dating back nearly a month. This data is used to model load fluctuations and predict future regulation targets.

[0074] Finally, in an optional embodiment, environmental parameter data, including information such as temperature, humidity, wind direction, and air pressure in the space where the equipment is located, is used to analyze the impact of the external environment on the performance of the energy storage device. This data is often acquired by environmental sensors within the energy storage compartment and uploaded to the main control platform via Modbus or CAN bus. For example, in high-temperature summer scenarios, when the temperature reaches 40°C, the response time of some energy storage units will be 20% longer than under standard operating conditions. This type of data has a direct impact on the generation of adjustment templates.

[0075] In summary, the above-mentioned types of data not only have clear data sources and collection structures, but also undertake different functions in the design of energy storage control strategies, such as basic modeling, disturbance factor identification, equipment capability assessment, load-side adaptability analysis, and environmental dynamic compensation. Together, they constitute the input basis for data-driven modeling and optimization control in the method of the present invention.

[0076] In step S12, the period and amplitude of the fluctuation characteristic data need to be separated to obtain the periodic trajectory and random disturbance component of the output fluctuation, including:

[0077] constructing a time series for the fluctuation characteristic data to obtain output data in a time series form;

[0078] Perform time series decomposition based on the output data to separate the periodic term and the residual term;

[0079] According to the periodic term, frequency extraction and period length calculation are performed to obtain the periodic trajectory of output fluctuation;

[0080] According to the residual term, sliding window smoothing and amplitude threshold judgment are performed to obtain the random disturbance component of the output fluctuation.

[0081] In one specific embodiment, to extract the microgrid's fluctuation structure characteristics from historical output data, the raw fluctuation characteristic data must first be constructed into a continuous time series with a time index. Specifically, output power values ​​collected at 5-minute intervals over the past 30 days from wind or photovoltaic power plants can be selected and arranged chronologically to form a data series. This time series serves as the basis for subsequent cycle and disturbance analysis, reflecting the continuity and trend changes of equipment operation.

[0082] Specifically, based on this time series output data, a time series decomposition method is used to structurally decompose it into two components: a periodic trajectory of output fluctuations and a random disturbance component. The periodic trajectory reflects output trends driven by natural laws, such as the sunshine cycle and seasonal changes in wind speed; the random disturbance component refers to sudden, irregular fluctuations in a short period of time, such as sudden weather changes or equipment fluctuations. Commonly used decomposition tools include STL (Seasonal-Trend decomposition using Loess) and empirical mode decomposition (EMD), which can effectively split the original data into a trend term containing the main period and a residual term containing non-main period, respectively used to describe the regularity and sudden fluctuation behavior of the system.

[0083] For example, frequency extraction and cycle length calculation are performed on the decomposed periodic trajectory to assess its rhythm of variation and repetition patterns. In one real-world case, the output sequence of a photovoltaic system exhibited a stable daily peak variation from 8:00 AM to 5:00 PM. Peak-to-trough distance statistics revealed a dominant period of approximately 24 hours, with a daily frequency, indicating typical diurnal fluctuations. This information will be used in subsequent fluctuation attribution analysis and disturbance identification benchmarks.

[0084] It should be noted that for the residual term obtained after time series decomposition—that is, the non-periodic portion after the main periodic term has been removed—the system further uses a sliding window smoothing method and an amplitude threshold judgment mechanism to extract the disturbance component. In specific operations, the system uses a one-hour sliding window to calculate the residual mean and fluctuation amplitude within that window. When the offset of a data point exceeds a set threshold (for example, ±200kW) and the fluctuation does not recur within a short period of time, it is determined to be a random disturbance event. This processing method can effectively identify short-term abnormal fluctuations caused by sudden wind speed increases, cloud cover, thunderstorm interference, etc., thereby extracting a set of disturbance features that are irregular and sudden.

[0085] Through the above process, the system successfully distinguished the stable periodic behavior from the transient disturbance factors in the microgrid output, established the periodic trajectory and random disturbance components for subsequent analysis, and laid a data foundation for the identification of disturbance sources and the generation of control strategies.

[0086] In step S13, it is necessary to perform correlation analysis based on the periodic trajectory, the random disturbance component, and the meteorological condition data to obtain data on the potential impact of fluctuations on system voltage deviation and frequency fluctuations, including:

[0087] Extracting the fluctuation amplitude data at the corresponding time point according to the time index of the periodic trajectory and the random disturbance component;

[0088] constructing a meteorological time series based on the time index of the periodic trajectory and the random disturbance component in combination with the meteorological condition data;

[0089] constructing a correlation data set of environmental impacts based on the fluctuation amplitude data and the meteorological time series;

[0090] Performing correlation analysis on the associated data set to generate a mapping relationship matrix between the fluctuation amplitude and the meteorological variables;

[0091] If the correlation coefficient in the mapping relationship matrix exceeds a preset coefficient threshold, a simulation analysis is performed on the voltage deviation and frequency fluctuation to obtain data on the potential impact of the fluctuation on the system's real-time voltage deviation and frequency fluctuation amplitude.

[0092] In a specific embodiment, to study the connection between power output fluctuations and grid operating conditions, it is first necessary to extract the fluctuation amplitude data at the corresponding time point based on the time index marked in the periodic trajectory and random disturbance component. The so-called time index refers to the time label corresponding to each data point in the original output time series, for example, "June 1, 2025, 12:30." In actual application, if a wind farm's output fluctuates at 12:30 on a certain day due to wind speed changes, the system will record the fluctuation value at that time point (such as the change amplitude +180kW) as the amplitude data, forming a fluctuation amplitude sequence in minutes. This sequence is then used as the target object for meteorological data comparison.

[0093] Specifically, based on the aforementioned time index, a meteorological time series must be constructed synchronously with meteorological condition data. This data comes from on-site meteorological sensors deployed at wind farms or photovoltaic sites or from third-party meteorological API services, and includes multiple indicators such as wind speed, wind direction, temperature, humidity, and irradiance. During the construction process, the output fluctuation time point is used as the alignment reference, and the meteorological variables corresponding to the same time are extracted from the meteorological data. For example, the meteorological time series items corresponding to 12:30 are "wind speed 8.4 m / s, wind direction 200°, temperature 31°C, relative humidity 65%, irradiance 850 W / m²." By processing multiple time points, a complete meteorological time series can be constructed.

[0094] In an exemplary embodiment, to further clarify the relationship between environmental variables and output fluctuations, it is necessary to match the aforementioned fluctuation amplitude data with meteorological time series to construct a correlation dataset of environmental impacts. The structure of this dataset resembles a two-dimensional data table, with each row representing a sample at a time point and each column representing a different variable at that moment, including fields such as "fluctuation amplitude, wind speed, wind direction, temperature, humidity, and irradiance." For example, the 101st sample record is "+240kW, 9.2m / s, 190°, 33°C, 70%, 860W / m²," which is used for subsequent correlation statistical analysis.

[0095] It should be noted that in order to identify regular trends between fluctuations and environmental factors in the above data set, the system performs a correlation analysis process. The results of this analysis are represented as a mapping relationship matrix, where each row corresponds to a fluctuation variable (such as output amplitude), and each column corresponds to an environmental variable (such as wind speed, temperature, etc.). Each value in the matrix is ​​the correlation coefficient between the two. For example, the value corresponding to "fluctuation amplitude - wind speed" in the mapping relationship matrix is ​​0.84, indicating a strong positive correlation between the two, while the value corresponding to "fluctuation amplitude - temperature" is 0.12, indicating a weak relationship between temperature and output changes. This matrix is ​​used to determine which meteorological factors have a significant impact on fluctuations and serves as input for subsequent simulations.

[0096] In a specific embodiment, to improve the accuracy of judgment, the system sets a correlation threshold as a screening basis. If the correlation coefficient in the mapping relationship matrix is ​​greater than the preset threshold, it is considered that the variable has a significant impact on the fluctuation. The threshold is set between 0.6 and 0.7 based on historical experience. For example, if "fluctuation amplitude - wind speed" is 0.84, it exceeds the 0.65 threshold. The system includes wind speed in the key analysis range, while "fluctuation amplitude - humidity" is 0.22 and is discarded.

[0097] In a specific embodiment, if there are significant correlation items in the mapping relationship matrix, the system enters the simulation analysis phase and uses a professional power system simulation platform to build a disturbance response model. In this embodiment, DIgSILENT PowerFactory is used as the main simulation platform, which supports fine-grained frequency response modeling and dynamic stability analysis. The system first imports the meteorological variables (such as wind speed and irradiance) identified in the correlation analysis as input disturbance sources into the microgrid model in the PowerFactory environment, and sets the disturbance action point and action time range.

[0098] Specifically, taking the wind speed variable as an example, if its correlation coefficient with output fluctuation reaches 0.84, the system will generate a wind speed disturbance curve (for example, a linear change from 6.5 m / s to 9.0 m / s over a 5-minute period) and bind it to the wind turbine's input power control module. The automatic frequency control (AFC) function is activated during the simulation, and the dynamic response of the system frequency is monitored. The results show that under the influence of this disturbance, the system frequency drops from the rated 50.00 Hz to 49.82 Hz, exceeding the preset limit for stable operation. The system then records the frequency offset corresponding to this disturbance variable.

[0099] Similarly, to address the impact of voltage deviation, the system activates the voltage control module (AVR) in PowerFactory, sets the observation node to the load center bus, and records voltage changes through simulation. If the node voltage drops from 0.98 pu to 0.93 pu due to wind speed disturbance, the system incorporates this result into the voltage deviation impact assessment results. Ultimately, the frequency response trajectory and voltage deviation data obtained through simulation are mapped one-to-one with the disturbance variable, forming a "potential impact dataset" containing fields such as disturbance name, disturbance amplitude, frequency response amplitude, and voltage deviation.

[0100] In step S14, it is necessary to calculate the degree of matching of the response time delay and power regulation accuracy of the energy storage system to dynamic load changes based on the potential impact data and the operating data to obtain an adaptability index, including:

[0101] extracting a response time delay value and a power regulation output value within a corresponding time period from the operation data according to the voltage deviation and frequency fluctuation time period indicated in the potential impact data;

[0102] Comparing the response time delay value with a preset response delay threshold to calculate a response time deviation;

[0103] Comparing the power regulation output value with a preset regulation accuracy threshold to calculate a regulation accuracy deviation;

[0104] A weighted fusion process is performed according to the response time deviation and the adjustment accuracy deviation to obtain an adaptability index reflecting the degree of matching.

[0105] In one specific embodiment, to evaluate the energy storage system's ability to regulate and respond to voltage and frequency disturbances, the system first extracts time periods with significant voltage deviations and frequency fluctuations based on the potential impact data obtained in the previous step. For example, in one disturbance event, the frequency fluctuation occurred between the 120th and 150th seconds of the simulation. Based on this, the system captures the energy storage system's real-time response data for that period from the operating data, including the time the system issues a power regulation command and the time the actual power change begins. By comparing the time difference between the two, the response time delay value is obtained.

[0106] Specifically, if the control system issues a command at 121 seconds and the actual power output changes at 123.5 seconds, the response time delay is 2.5 seconds. To determine whether this meets the regulation requirements, it is necessary to compare it with a preset response delay threshold. In this embodiment, the response time delay threshold is set to 3 seconds. Exceeding this value will prevent the frequency offset from being suppressed in a timely manner, so a 2.5-second response is considered acceptable.

[0107] Next, the system extracts the power regulation output value for that time period, representing the deviation of the energy storage system's actual output power from the target value. In this example, the system's expected output is +250kW, but the device only achieves +220kW, resulting in a regulation accuracy deviation of 30kW. This value is then compared with a preset regulation accuracy threshold. In this example, the regulation accuracy threshold is set at ±20kW, meaning any deviation exceeding 20kW is considered insufficient response accuracy.

[0108] It should be noted that when evaluating the overall adaptability, the two deviation values ​​are not simply superimposed, but a weighted fusion process is performed based on the different weights of delay and accuracy in actual operation. Specifically, in this embodiment, the response time deviation weight is 0.6 and the adjustment accuracy deviation weight is 0.4, that is:

[0109] Adaptability index = 0.6 × normalized response time deviation + 0.4 × normalized power deviation.

[0110] To make the two deviation values ​​comparable, the system first normalizes them, using their respective thresholds as the standard. Continuing with the above data as an example: the response time deviation is 2.5s / 3s = 0.83, and the power deviation is 30kW / 20kW = 1.5. The final adaptability index is:

[0111] Adaptability index = 0.6 × 0.83 + 0.4 × 1.5 = 0.498 + 0.6 = 1.098.

[0112] If the final adaptability index exceeds a set comprehensive threshold (e.g., 1.0), the system determines that the current energy storage response is insufficient to support dynamic load changes and must enter the subsequent capacity adjustment and frequency response range optimization processes. This approach ensures a dual evaluation of the energy storage system's response performance while also allowing the weight to be flexibly adjusted based on system sensitivity.

[0113] For example, for urban low-voltage microgrid systems, which are primarily deployed in office buildings, residential communities, and light industrial parks, the operating load has significant fluctuation characteristics and high requirements for frequency and voltage stability. To ensure that the energy storage system can adjust promptly when rapid disturbances occur, the preset response delay threshold is set to 2.0 seconds. That is, when a frequency offset occurs, the energy storage system must complete the power output adjustment action within 2 seconds; otherwise, it will be considered a response timeout. This threshold is derived from a combined assessment of the control system sampling period, master-slave communication delay, and the response inertia of the energy storage itself. At the same time, the preset threshold for power regulation accuracy is set to ±5%. That is, when the system load changes, the actual power output of the energy storage system must deviate from the expected power value by no more than 5%. Within this range, the microgrid system can maintain frequency closed-loop stability under typical operating conditions without inducing inverter linkage jitter or frequent adjustments of multiple source nodes, and has strong adaptability to actual engineering projects.

[0114] For example, for independent microgrid systems in remote mountainous areas, which have limited scheduling redundancy and primarily consist of basic lighting and low-speed machinery, the system allows for a higher tolerance for response delays. In this scenario, the response delay threshold can be relaxed to 5.0 seconds, allowing the energy storage system to gradually complete output regulation within 5 seconds, in exchange for lower energy loss and system wear. Furthermore, the regulation accuracy threshold can be adjusted to ±10% to accommodate low-precision power supply demand scenarios, reduce unnecessary frequent scheduling, and improve the overall system lifespan and economic efficiency.

[0115] In step S15, if the adaptability index is lower than a preset index threshold, the disturbance characteristics are decomposed according to the random disturbance component, the operation data, and the load demand data, and the energy capacity and frequency range are adjusted to obtain a dynamic response configuration, including:

[0116] Extracting periodic fluctuations and transient disturbances according to the random disturbance component and the operating data to obtain a disturbance characteristic set;

[0117] Calculating the upper and lower limits of the current available energy capacity of the energy storage system based on the disturbance characteristic set and the operating data to obtain a capacity constraint range;

[0118] evaluating a required response bandwidth according to the disturbance characteristic set and the load demand data to obtain an adjustment frequency range;

[0119] Based on the capacity constraint range and the regulation frequency range, a dynamic response configuration matching current disturbance characteristics is generated.

[0120] In a specific embodiment, when the adaptability index calculated by the system is lower than the preset index threshold, it means that the current energy storage system has a response delay or a power deviation exceeding the limit when dealing with load disturbances. At this time, the system needs to further use the extracted random disturbance components and energy storage operation data to finely classify the nature of the disturbance. Specifically, the system first identifies the periodic fluctuations and non-periodic transient mutations of the disturbance. For example, in a microgrid in an industrial park, two types of disturbances were identified through analysis of the load change curve within 30 minutes: one is a low-frequency periodic fluctuation caused by the peak power consumption during the day, and the other is an instantaneous power jump caused by the startup of large equipment.

[0121] In a specific embodiment, the system calculates the maximum available energy capacity and minimum safe operating capacity of the energy storage system based on the aforementioned disturbance characteristics, combined with operating data such as the energy storage system's current SOC (state of charge), discharge power curve, and remaining continuous discharge capacity, to form a capacity constraint range. For example, in one analysis, the energy storage battery's current SOC is 65%, and the set safety minimum is 20%, so the current available capacity is 45%. At the same time, considering the current 30kW discharge power limit, the upper limit of energy that can be released per unit time is determined.

[0122] In one specific embodiment, the system combines a set of disturbance characteristics with actual load demand data to deduce the required system regulation response bandwidth under different disturbance intensities. Regulation response bandwidth is defined here as the frequency response range that the system needs to regulate per unit time. For example, in one case, load forecast data indicates a maximum surge demand of 80kW within the next 10 minutes. Combining the disturbance frequency and response time, the system calculates the required frequency regulation range to be 0.2Hz to 0.5Hz to ensure that the system frequency remains within the stability boundary.

[0123] In one specific embodiment, the system maps and integrates the capacity constraint range with the regulation frequency range to construct a dynamic response configuration for the current conditions. This configuration clearly defines control parameters such as the maximum and minimum output ranges of the energy storage system in the next cycle, the allowable slope of the frequency response curve, and the duration of the regulation. This provides physical boundaries and scheduling basis for subsequent behavioral simulation and control command generation, thereby achieving stable adaptation to complex disturbances.

[0124] In step S16, it is necessary to construct an energy storage response behavior simulation framework based on the dynamic response configuration, combined with preset state switching loss parameters and preset environmental adaptability parameters, and generate an initial adjustment instruction template, including:

[0125] extracting target output variation characteristics based on the power adjustment amplitude and response time requirements included in the dynamic response configuration;

[0126] Based on the target output variation characteristics, combined with preset state switching loss parameters and preset environmental adaptability parameters, the response behavior of the energy storage device under different output ranges and environmental conditions is simulated to generate a simulated data set of the response behavior;

[0127] In the simulation data set, identifying an abnormal operating condition in which a state switching loss value exceeds a preset loss threshold;

[0128] In response to the abnormal operating condition, an initial adjustment instruction template adapted to the current environmental conditions is generated.

[0129] In one specific embodiment, after acquiring and confirming a dynamic response configuration, the system first extracts a target output change characteristic based on the power regulation amplitude and response time requirements contained in the configuration. This characteristic describes the trend, time span, and rate of change of the energy storage system's power from the initial state to the target state during the regulation process. For example, if a dynamic response configuration sets a target power increase amplitude of 40kW and a response time of 3 seconds, the system will establish an ideal output trajectory for the regulation process, which serves as a benchmark input for subsequent simulations.

[0130] Specifically, to analyze the execution performance of control commands under real-world operating conditions, the system inputs the target output variation characteristics into a response behavior simulation model. This model incorporates two preset parameter sets: state switching loss parameters, which simulate the energy loss and efficiency fluctuations caused by the energy storage device switching between different operating states (such as switching between charge and discharge modes); and environmental adaptability parameters, such as the temperature coefficient and humidity impact factor, which assess the impact of environmental conditions on the device's response speed and output stability. For example, if the preset temperature adaptability range is 10–35°C and the current operating condition is 38°C, the system will simulate the downward trend in energy storage power output caused by high temperature.

[0131] For example, during the simulation process, the system batch-simulated the device's behavior under different output ranges (e.g., 0–20kW, 20–40kW, and 40–60kW) and environmental conditions, generating a set of simulated data on the device's behavior. Each data set includes metrics such as regulation start delay, output fluctuation range, state transition efficiency, and the corresponding environmental coupling factor, characterizing the device's true performance under that combination of conditions.

[0132] The system then screens this simulated data set and identifies anomalies, focusing on conditions where energy loss during state transitions exceeds a preset threshold. For example, if energy loss due to state transitions within a certain interval exceeds a preset 10% tolerance, this is considered an abnormal condition. Such conditions can lead to inefficiencies, insufficient control accuracy, or energy waste in actual operation and must be corrected.

[0133] For identified abnormal operating conditions, the system develops differentiated control strategies based on current environmental conditions (such as high temperature and high humidity) and simulation outputs, ultimately generating an initial adjustment instruction template adapted to the current disturbance environment. This template not only includes power curves and response time settings, but also incorporates environmental adaptability coefficients and tolerance adjustment parameters, providing the foundational input for subsequent real-time control instruction generation. This ensures that the energy storage system achieves higher steady-state efficiency and environmental robustness when performing dynamic response tasks.

[0134] It should be noted that the preset state switching loss parameter is used to describe the energy loss incurred when the energy storage system switches from charging to discharging, or from standby to operating. This parameter is set based on the equipment's technical specifications and historical operating data. For example, for a lithium iron phosphate battery-based energy storage system, the average energy loss during the charge-to-discharge transition is set to 3%, and the additional energy consumption during the standby-to-operation transition is set to 1.5%. These values ​​are determined by a combination of manufacturer experimental data and field calibration results, and are regularly updated as the equipment ages to ensure that simulation is consistent with actual conditions.

[0135] It should be noted that preset environmental adaptability parameters are used to quantify the impact of environmental variables (such as temperature and humidity) on the energy storage system's responsiveness. For example, when the ambient temperature exceeds 35°C, the output capacity of lithium batteries generally decreases. The system sets a temperature adjustment coefficient of -0.8% / °C, meaning that for every degree above the ambient temperature, the system allows the output capacity to decrease by 0.8%. When the humidity exceeds 90%, considering insulation safety and heat dissipation efficiency, the humidity compensation coefficient is set at -0.5% / 10%. These parameters are obtained through laboratory environmental chamber testing and regression analysis of actual site data, and are applicable to different energy storage system types and application environments.

[0136] It should be noted that the preset loss threshold is used to determine whether a certain energy storage response condition is an abnormal state. In a specific embodiment, the system sets the total state switching energy loss threshold at 8%. That is, if the energy loss simulated during a certain adjustment process accounts for more than 8% of the total energy, the condition is considered to have an efficiency abnormality and is not recommended for subsequent actual scheduling. In addition, for critical conditions (such as continuous fluctuation response tasks), the threshold can be lowered to 6% to ensure that the system still has energy utilization efficiency and equipment protection capabilities during long-term high-frequency switching. This type of threshold setting is formulated in conjunction with the national standard GB / T36547-2018.

[0137] In step S17, it is necessary to adjust the frequency and power parameters based on the initial adjustment instruction template and in combination with the environmental parameter data to generate a control instruction set adapted to the current disturbance conditions, including:

[0138] Based on the initial adjustment instruction template, the disturbance type is identified on the environmental parameter data to determine the disturbance category and disturbance intensity of the current disturbance;

[0139] Generate an updated frequency control instruction according to the frequency adjustment rule corresponding to the disturbance category matching and the modified frequency parameter corresponding to the disturbance intensity;

[0140] Based on the frequency control instruction and in combination with the disturbance category, the power output parameter is dynamically adjusted to generate a control instruction set adapted to the current disturbance condition.

[0141] In one specific embodiment, the microgrid's operating environment is first identified based on the initial adjustment instruction template and the currently collected environmental parameter data. This identification process does not rely on manual judgment, but instead utilizes real-time data collected by on-site sensors for temperature, humidity, wind speed, voltage, and current. The system then classifies and processes the data using preset disturbance judgment rules. Taking wind speed changes as an example, if the wind speed increase exceeds 3m / s within a continuous sampling period and the change lasts for more than 2 minutes, the system identifies this phenomenon as a "wind disturbance." If the system voltage deviation exceeds ±5% of the rated value during the same period and is accompanied by frequency fluctuations, it is identified as a "voltage-frequency combined disturbance." The disturbance intensity is identified by statistically analyzing whether the disturbance amplitude exceeds various operating limits. For example, if the frequency fluctuation exceeds 0.5Hz, the voltage drop exceeds 10%, or the temperature and humidity changes exceed the set threshold, the system classifies it as a high-intensity disturbance, which serves as a basis for subsequent instruction correction.

[0142] Specifically, after identifying the disturbance type and intensity, the system searches the preset frequency adjustment rule table according to the disturbance type, and selects appropriate correction parameters in combination with the disturbance intensity level to generate an updated frequency control instruction. For example, for the "high-intensity wind disturbance" scenario, the system will give priority to matching the frequency control strategy under the "wind source-dominated disturbance", and expand the frequency response range set in the original instruction by 10% to 15% to improve adaptability to sudden changes in wind power. At the same time, with reference to the wind speed change rate, the frequency setting value is corrected at the boundaries to ensure that the frequency regulation action of the energy storage equipment is coordinated with the development trend of the disturbance, avoiding response fatigue or control lag caused by frequent scheduling. The instruction update process does not involve changing the main control structure, but dynamically adjusts some adjustment parameters based on the original instruction template to facilitate rapid issuance and hardware compatibility during execution.

[0143] For example, after obtaining the updated frequency control instruction, the system will further combine the power regulation strategy that matches the disturbance category to dynamically correct the output power parameters of the energy storage system. Taking the "high temperature and high humidity disturbance" environment as an example, the system determines that this type of environment has a risk of inhibiting the discharge capacity of the energy storage battery cell, so it lowers the original power output upper limit to 85% of the rated power and lowers the power change slope to avoid thermal shock; at the same time, it combines the current SOC state of the energy storage with the load change trend in the past 10 minutes to fine-tune the adjustment slope. The final generated control instruction set contains complete parameters such as the updated frequency adjustment range, power change rate, maximum allowed output power, and adjustment response time, providing comprehensive support for the controller to issue. Through the dynamic correction of this control instruction set, the system can quickly adapt to different disturbance types and intensities, ensuring the stable operation of the microgrid, accurate energy storage response, and extending the service life of the equipment.

[0144] In summary, the present invention provides a microgrid stability control method and system based on output characteristic simulation to achieve rapid response and precise regulation under disturbance conditions.

[0145] Reference Figure 2 The second embodiment of the present invention provides a microgrid stability control system based on output characteristic simulation, including:

[0146] Data acquisition module, used to obtain historical output fluctuation characteristic data, meteorological condition data, energy storage system operation data, load demand data and environmental parameter data;

[0147] A fluctuation separation module is used to separate the period and amplitude of the fluctuation characteristic data to obtain the periodic trajectory and random disturbance component of the output fluctuation;

[0148] a potential analysis module, configured to perform a correlation analysis based on the periodic trajectory, the random disturbance component, and the meteorological condition data to obtain data on the potential impact of the fluctuation on the system voltage deviation and frequency fluctuation;

[0149] an adaptive adjustment module, configured to calculate, based on the potential impact data and the operating data, a degree of matching of the response time delay and power regulation accuracy of the energy storage system to dynamic load changes, and obtain an adaptability index;

[0150] a response configuration module, configured to decompose disturbance characteristics, adjust energy capacity and frequency range based on the random disturbance component, the operating data, and the load demand data to obtain a dynamic response configuration if the adaptability index is lower than a preset index threshold;

[0151] An adjustment instruction module is used to construct an energy storage response behavior simulation framework and generate an initial adjustment instruction template based on the dynamic response configuration, combined with preset state switching loss parameters and preset environmental adaptability parameters;

[0152] The control instruction module is used to adjust the frequency and power parameters based on the initial adjustment instruction template and in combination with the environmental parameter data to generate a control instruction set that adapts to the current disturbance conditions.

[0153] It should be noted that the microgrid stability control system based on output characteristic simulation provided in an embodiment of the present invention is used to execute all the process steps of the microgrid stability control method based on output characteristic simulation in the above embodiment. The working principles and beneficial effects of the two correspond one to one, so they will not be repeated here.

[0154] An embodiment of the present invention further provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a fluctuation separation program. When the processor executes the computer program, the steps in the above-mentioned embodiments of the microgrid stability control method based on output characteristic simulation are implemented, such as Figure 1 Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are realized, such as the adjustment instruction module.

[0155] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, and the instruction segments are used to describe the execution process of the computer program in the electronic device.

[0156] The electronic device may be a computing device such as a desktop computer, notebook, PDA, or smart tablet. The electronic device may include, but is not limited to, a processor and memory. Those skilled in the art will appreciate that the aforementioned components are merely examples of electronic devices and do not constitute a limitation of the electronic device. The electronic device may include more or fewer components than those described above, or a combination of certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, and the like.

[0157] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the electronic device, connecting various parts of the entire electronic device using various interfaces and lines.

[0158] The memory can be used to store the computer programs and / or modules. The processor implements the various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory and accessing the data stored in the memory. The memory may primarily include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function (such as a sound playback function or an image playback function); the data storage area may store data generated based on the use of the mobile phone (such as audio data, a phone book, etc.). Furthermore, the memory may include high-speed random access memory and non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0159] If the module / unit integrated into the electronic device is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention can implement all or part of the process steps in the above-mentioned method embodiments by using a computer program to instruct the relevant hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal, and software distribution medium. It should be noted that the content of the computer-readable medium can be appropriately increased or decreased based on the requirements of legislation and patent practice in a jurisdiction. For example, in some jurisdictions, based on legislation and patent practice, computer-readable media does not include electric carrier signals and telecommunication signals.

[0160] It should be noted that the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed across multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement the present invention without inventive effort.

[0161] The specific embodiments described above further illustrate the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A microgrid stability control method based on output characteristic simulation, characterized in that include: Obtain historical output fluctuation characteristic data, meteorological condition data, energy storage system operation data, load demand data and environmental parameter data; Separating the period and amplitude of the fluctuation characteristic data to obtain the periodic trajectory and random disturbance component of the output fluctuation; performing correlation analysis based on the periodic trajectory, the random disturbance component, and the meteorological condition data to obtain data on potential impact of fluctuations on system voltage deviation and frequency fluctuations; Calculating, based on the potential impact data and the operating data, the degree to which the response time delay and power regulation accuracy of the energy storage system match dynamic load changes to obtain an adaptability index; If the adaptability index is lower than a preset index threshold, decomposing disturbance characteristics according to the random disturbance component, the operation data, and the load demand data, adjusting energy capacity and frequency range, and obtaining a dynamic response configuration; According to the dynamic response configuration, combined with preset state switching loss parameters and preset environmental adaptability parameters, a storage response behavior simulation framework is constructed to generate an initial adjustment instruction template; Based on the initial adjustment instruction template and in combination with the environmental parameter data, the frequency and power parameters are adjusted to generate a control instruction set adapted to the current disturbance conditions; The step of performing correlation analysis based on the periodic trajectory, the random disturbance component, and the meteorological condition data to obtain data on potential impacts of fluctuations on system voltage deviation and frequency fluctuations includes: Extracting the fluctuation amplitude data at the corresponding time point according to the time index of the periodic trajectory and the random disturbance component; constructing a meteorological time series based on the time index of the periodic trajectory and the random disturbance component in combination with the meteorological condition data; constructing a correlation data set of environmental impacts based on the fluctuation amplitude data and the meteorological time series; Performing correlation analysis on the associated data set to generate a mapping relationship matrix between the fluctuation amplitude and the meteorological variables; If the correlation coefficient in the mapping relationship matrix exceeds a preset coefficient threshold, a simulation analysis is performed on the voltage deviation and frequency fluctuation to obtain data on the potential impact of the fluctuation on the system's real-time voltage deviation and frequency fluctuation amplitude; The adaptability index is obtained by calculating the degree of matching between the response time delay and the power regulation accuracy of the energy storage system to the dynamic load change based on the potential impact data and the operating data, including: extracting a response time delay value and a power regulation output value within a corresponding time period from the operation data according to the voltage deviation and frequency fluctuation time period indicated in the potential impact data; Comparing the response time delay value with a preset response delay threshold to calculate a response time deviation; Comparing the power regulation output value with a preset regulation accuracy threshold to calculate a regulation accuracy deviation; Performing weighted fusion processing according to the response time deviation and the adjustment accuracy deviation to obtain an adaptability index reflecting the degree of matching; The process of constructing an energy storage response behavior simulation framework based on the dynamic response configuration, combining preset state switching loss parameters and preset environmental adaptability parameters, and generating an initial adjustment instruction template includes: extracting target output variation characteristics based on the power adjustment amplitude and response time requirements included in the dynamic response configuration; Based on the target output variation characteristics, combined with preset state switching loss parameters and preset environmental adaptability parameters, the response behavior of the energy storage device under different output ranges and environmental conditions is simulated to generate a simulated data set of the response behavior; In the simulation data set, identifying an abnormal operating condition in which a state switching loss value exceeds a preset loss threshold; In response to the abnormal operating condition, an initial adjustment instruction template adapted to the current environmental conditions is generated.

2. The microgrid stability control method based on output characteristic simulation according to claim 1 is characterized in that: The separation of the period and amplitude of the fluctuation characteristic data to obtain the periodic trajectory and random disturbance component of the output fluctuation includes: constructing a time series for the fluctuation characteristic data to obtain output data in a time series form; Perform time series decomposition based on the output data to separate the periodic term and the residual term; According to the periodic term, frequency extraction and period length calculation are performed to obtain the periodic trajectory of output fluctuation; According to the residual term, sliding window smoothing and amplitude threshold judgment are performed to obtain the random disturbance component of the output fluctuation.

3. The microgrid stability control method based on output characteristic simulation according to claim 1 is characterized in that: If the adaptability index is lower than a preset index threshold, decomposing disturbance characteristics according to the random disturbance component, the operation data, and the load demand data, adjusting energy capacity and frequency range, and obtaining a dynamic response configuration, including: Extracting periodic fluctuations and transient disturbances according to the random disturbance component and the operating data to obtain a disturbance characteristic set; Calculating the upper and lower limits of the current available energy capacity of the energy storage system based on the disturbance characteristic set and the operating data to obtain a capacity constraint range; evaluating a required response bandwidth according to the disturbance characteristic set and the load demand data to obtain an adjustment frequency range; Based on the capacity constraint range and the regulation frequency range, a dynamic response configuration matching current disturbance characteristics is generated.

4. The microgrid stability control method based on output characteristic simulation according to claim 1 is characterized in that: The step of adjusting the frequency and power parameters based on the initial adjustment instruction template and combining the environmental parameter data to generate a control instruction set adapted to the current disturbance conditions includes: Based on the initial adjustment instruction template, the disturbance type is identified on the environmental parameter data to determine the disturbance category and disturbance intensity of the current disturbance; Generate an updated frequency control instruction according to the frequency adjustment rule corresponding to the disturbance category matching and the modified frequency parameter corresponding to the disturbance intensity; Based on the frequency control instruction and in combination with the disturbance category, the power output parameter is dynamically adjusted to generate a control instruction set adapted to the current disturbance condition.

5. A microgrid stability control system based on output characteristic simulation, characterized in that: A microgrid stability control method based on output characteristic simulation according to any one of claims 1 to 4 is implemented, comprising: Data acquisition module, used to obtain historical output fluctuation characteristic data, meteorological condition data, energy storage system operation data, load demand data and environmental parameter data; A fluctuation separation module is used to separate the period and amplitude of the fluctuation characteristic data to obtain the periodic trajectory and random disturbance component of the output fluctuation; a potential analysis module, configured to perform a correlation analysis based on the periodic trajectory, the random disturbance component, and the meteorological condition data to obtain data on the potential impact of the fluctuation on the system voltage deviation and frequency fluctuation; an adaptive adjustment module, configured to calculate, based on the potential impact data and the operating data, a degree of matching of the response time delay and power regulation accuracy of the energy storage system to dynamic load changes, and obtain an adaptability index; a response configuration module, configured to decompose disturbance characteristics, adjust energy capacity and frequency range based on the random disturbance component, the operating data, and the load demand data to obtain a dynamic response configuration if the adaptability index is lower than a preset index threshold; An adjustment instruction module is used to construct an energy storage response behavior simulation framework and generate an initial adjustment instruction template based on the dynamic response configuration, combined with preset state switching loss parameters and preset environmental adaptability parameters; The control instruction module is used to adjust the frequency and power parameters based on the initial adjustment instruction template and in combination with the environmental parameter data to generate a control instruction set that adapts to the current disturbance conditions.

6. An electronic device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the microgrid stability control method based on output characteristic simulation according to any one of claims 1 to 4 is implemented.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the microgrid stability control method based on output characteristic simulation according to any one of claims 1 to 4.

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