Quantitative analysis method and device for control performance by source network uncertainty and medium

Abstract

The invention relates to the technical field of power system control and stability analysis, in particular to a quantitative analysis method and device for control performance by source network uncertainty and a medium, and the method comprises the steps: generating a plurality of random parameter combinations based on a plurality of probability distribution models; automatically configuring the random parameter combination to a pre-established power system simulation model, driving the power system simulation model to complete simulation operation of corresponding times, and automatically extracting at least one controlled quantity data; and calculating at least two performance indexes on the basis of the extracted controlled quantity data, and establishing a mapping relation between the random parameter combination and the performance indexes on the basis of the data of each simulation frequency so as to quantify the influence degree of different uncertainty factors on each control performance index. Through the setting, the influence of the bilateral uncertainty of the source network on the control performance of the power system can be systematically quantified, and the problem that the coupling effect of the bilateral uncertainty cannot be comprehensively analyzed by a traditional method is effectively solved.

Description

Technical Field

[0001] This application relates to the field of power system control and stability analysis technology, and in particular to a method, device and medium for quantitative analysis of the impact of source-grid uncertainty on control performance. Background Technology

[0002] As the penetration rate of renewable energy sources such as wind power and photovoltaics in the power system continues to increase, the operating characteristics of the power system are undergoing profound changes. On the source side, the natural fluctuations and intermittency of wind and solar power output constitute the main source of uncertainty. Their power generation is strongly affected by weather conditions, exhibiting significant randomness and difficulty in accurate prediction. On the grid side, dynamic changes in load demand and fault disturbances also introduce significant uncertainty. These uncertainties on both the source and grid sides are intertwined and coupled, jointly constituting the core challenges affecting the safe, stable, and economical operation of modern power systems.

[0003] Faced with this new situation, traditional power system control strategy design and performance analysis methods are mostly based on simplified deterministic models, or focus only on analyzing the uncertainty of a single aspect on the source side or grid side, lacking a systematic consideration of the dynamic coupling effect of uncertainty on both the source and grid sides.

[0004] Currently, in academic research and engineering practice, there is still a lack of effective methods and models that can systematically quantify the impact mechanism of uncertainties on both the power source and grid sides on a series of key control performance indicators. This lack of theoretical methods and analytical tools directly restricts the refined design of advanced control strategies for power systems, the adaptive tuning of controller parameters, and the effective improvement of the overall robustness and adaptive operation capability of the system under the background of high proportion of renewable energy access. Summary of the Invention

[0005] To address the aforementioned technical problems, this application provides a method, device, and medium for quantitative analysis of the impact of source-grid uncertainties on control performance. This method can systematically quantify the impact of source-grid uncertainties on power system control performance, effectively solving the problem that traditional methods cannot comprehensively analyze the coupling effect of two-sided uncertainties.

[0006] In a first aspect, embodiments of this application provide a method for quantitatively analyzing the impact of source-network uncertainty on control performance, the method comprising: Based on multiple probability distribution models, multiple sets of random parameter combinations are generated to simulate uncertainties on the source side and the network side; The random parameter combination is automatically configured into a pre-established power system simulation model, and the power system simulation model is driven to complete the corresponding number of simulation runs, wherein each simulation run outputs the time domain data of the system dynamic response; Automatically extract at least one controlled variable from the time-domain data, including system frequency, node voltage amplitude, and frequency change rate. Based on the extracted controlled variable data, at least two performance indicators are calculated from steady-state error, overshoot, control energy, and control error integral. Based on the data from each simulation, a mapping relationship between the random parameter combination and the performance indicators is established to quantify the influence of different uncertainty factors on each control performance indicator.

[0007] According to some embodiments of the first aspect of this application, the generation of multiple sets of random parameter combinations for simulating uncertainties at the source and network sides based on multiple probability distribution models includes: Multiple sets of active power reference values ​​are generated using the Weibull distribution to simulate the uncertainty of the output of the photovoltaic power generation unit in the power system simulation model. Multiple sets of governor speed droop coefficients are generated by uniform distribution to simulate the parameter uncertainty of the virtual synchronous generator control unit in the power system simulation model. Multiple sets of active power values ​​of the load are generated by normal distribution to simulate the power fluctuation uncertainty of the grid-side load unit in the power system simulation model; The random parameter combination includes at least one of the active power reference value, the speed governor speed droop coefficient, and the load active power value.

[0008] According to some embodiments of the first aspect of this application, the power system simulation model is a three-phase grid-connected photovoltaic-storage load model based on a virtual synchronous generator (VSG). The power system simulation model includes: a power generation unit, an energy storage and grid connection control unit, and a grid-side unit. The DC output terminal of the power generation unit is connected to the DC input terminal of the energy storage and grid connection control unit, and the AC output terminal of the energy storage and grid connection control unit is connected to the grid-side unit through line impedance.

[0009] According to some embodiments of the first aspect of this application, the power generation unit includes a photovoltaic array and a boost circuit, wherein the boost circuit uses the incremental conductance method for maximum power point tracking control. The energy storage and grid connection control unit includes a battery, a bidirectional DC-DC converter, a three-phase inverter bridge, and a VSG control system; The VSG control system is sequentially connected to a phase pre-synchronization circuit, a rotor mechanical equation circuit, an excitation electromotive force generation circuit, a voltage and current dual closed-loop control circuit, and a virtual impedance circuit. The outputs of the phase pre-synchronization circuit and the excitation electromotive force generation circuit serve as the inputs of the rotor mechanical equation circuit and the voltage and current dual closed-loop control circuit, respectively. The output of the voltage and current dual closed-loop control circuit is used to generate a PWM signal to drive the three-phase inverter bridge. The grid-side unit includes a three-phase parallel RLC load.

[0010] According to some embodiments of the first aspect of this application, the rotor mechanical equation circuit is configured as follows: Based on the real-time operating state variables of the power system simulation model, the values ​​of the moment of inertia and damping coefficient in the rotor mechanical equation circuit are dynamically adjusted, where the real-time operating state variables are the system frequency deviation or frequency change rate.

[0011] According to some embodiments of the first aspect of this application, the step of automatically extracting at least one controlled variable data from the time-domain data, including system frequency, node voltage amplitude, and frequency change rate, includes: The controlled quantity data is attempted to be obtained through multiple data interfaces in sequence; wherein, the subsequent data interface is activated when the previous data interface fails to obtain data.

[0012] According to some embodiments of the first aspect of this application, after automatically extracting at least one controlled variable data among system frequency, node voltage amplitude, and frequency change rate from the time-domain data, the process includes: Verify the consistency of the lengths of the time vector and the numerical vector of the controlled variable data, remove non-numerical and infinite values ​​from the controlled variable data, and convert the processed controlled variable data into a column vector in a standard format.

[0013] According to some embodiments of the first aspect of this application, multiple typical fault conditions are set in a power system simulation model. The step of establishing a mapping relationship between the random parameter combinations and the performance indicators to quantify the influence of different uncertainty factors on various control performance indicators includes: By comparing and analyzing the mapping relationship between the random parameter combinations and the performance indicators under various typical fault conditions, a control performance impact assessment report is generated to guide the design of the control system. The typical fault conditions include simulated sudden drop in wind and solar power generation, simulated continuous power imbalance, simulated short-circuit fault followed by disconnection, simulated load surge, and simulated sudden drop in new energy output.

[0014] In a second aspect, embodiments of this application provide an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement: the method for quantitative analysis of the impact of source-network uncertainty on control performance as described in the first aspect above.

[0015] Thirdly, embodiments of this application also provide a computer-readable storage medium storing computer-executable instructions for executing the quantitative analysis method for the impact of source-network uncertainty on control performance as described in the first aspect above.

[0016] The beneficial effects of this application are reflected in the following aspects: Based on multiple probability distribution models, multiple sets of random parameter combinations are generated to simulate uncertainties on both the source and grid sides; these random parameter combinations are automatically configured into a pre-established power system simulation model, driving the model to complete a corresponding number of simulation runs. Each simulation run outputs time-domain data of the system's dynamic response; from the time-domain data, at least one controlled variable data among system frequency, node voltage amplitude, and frequency change rate is automatically extracted; based on the extracted controlled variable data, at least two performance indicators among steady-state error, overshoot, control energy, and control error integral are calculated; and based on the data from each simulation run, a mapping relationship between the random parameter combinations and performance indicators is established to quantify the influence of different uncertainty factors on various control performance indicators. Through this setup, this application can systematically quantify the impact of source-grid dual-side uncertainties on power system control performance, effectively solving the problem that traditional methods cannot comprehensively analyze the coupling effect of dual-side uncertainties. Attached Figure Description

[0017] Figure 1 A flowchart illustrating the method for quantifying the impact of source-network uncertainty on control performance provided in the first aspect of this application. Figure 2 This is a schematic diagram of the process provided in the first aspect of the present application for generating multiple sets of random parameter combinations for simulating uncertainties on the source side and the network side based on multiple probability distribution models. Figure 3 This is a schematic diagram of the configuration of the rotor mechanical equation circuit provided in the first aspect embodiment of this application; Figure 4 This is a schematic diagram of the process for automatically extracting at least one controlled variable data among system frequency, node voltage amplitude, and frequency change rate provided in the first aspect embodiment of this application; Figure 5 This is a flowchart illustrating the process after automatically extracting at least one controlled variable data among system frequency, node voltage amplitude, and frequency change rate, as provided in the first aspect embodiment of this application. Figure 6 This is a schematic diagram of the process for establishing a mapping relationship between random parameter combinations and performance indicators for various typical fault conditions, provided in the first aspect embodiment of this application. Figure 7 A flowchart illustrating the overall architecture of the power system simulation model provided in the first aspect embodiment of this application; Figure 8This is a circuit topology diagram of a photovoltaic power generation unit provided in the first aspect embodiment of this application; Figure 9 This is a topology diagram of the energy storage and power conversion unit provided in the first aspect embodiment of this application; Figure 10 This is a schematic diagram of the phase pre-synchronization control principle provided in the first aspect embodiment of this application; Figure 11 This is a schematic diagram of the voltage pre-synchronization control principle provided in the first aspect embodiment of this application; Figure 12 This is a structural diagram illustrating the implementation of the rotor mechanical equations provided in the first aspect embodiment of this application; Figure 13 This is a topology diagram of the excitation electromotive force generation circuit provided in the first aspect embodiment of this application; Figure 14 This is a voltage and current dual closed-loop control circuit diagram provided in the first aspect embodiment of this application; Figure 15 This is a schematic diagram of the structure of the electronic device provided in the second aspect of this application. Detailed Implementation

[0018] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.

[0019] In the description of this application, it should be understood that the orientation descriptions, such as up, down, front, back, left, right, etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0020] In the description of this application, the use of "first" and "second" is for the purpose of distinguishing technical features only, and should not be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated or the order of the technical features indicated.

[0021] In the description of this application, unless otherwise expressly defined, terms such as "setup," "installation," and "connection" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this application in conjunction with the specific content of the technical solution.

[0022] As the penetration rate of renewable energy sources such as wind power and photovoltaics in the power system continues to increase, the operating characteristics of the power system are undergoing profound changes. On the source side, the natural fluctuations and intermittency of wind and solar power output constitute the main source of uncertainty. Their power generation is strongly affected by weather conditions, exhibiting significant randomness and difficulty in accurate prediction. On the grid side, dynamic changes in load demand and fault disturbances also introduce significant uncertainty. These uncertainties on both the source and grid sides are intertwined and coupled, jointly constituting the core challenges affecting the safe, stable, and economical operation of modern power systems.

[0023] Faced with this new situation, traditional power system control strategy design and performance analysis methods are mostly based on simplified deterministic models, or focus only on analyzing the uncertainty of a single aspect on the source side or grid side, lacking a systematic consideration of the dynamic coupling effect of uncertainty on both the source and grid sides.

[0024] Currently, in academic research and engineering practice, there is still a lack of effective methods and models that can systematically quantify the impact mechanism of uncertainties on both the power source and grid sides on a series of key control performance indicators. This lack of theoretical methods and analytical tools directly restricts the refined design of advanced control strategies for power systems, the adaptive tuning of controller parameters, and the effective improvement of the overall robustness and adaptive operation capability of the system under the background of high proportion of renewable energy access.

[0025] To address the aforementioned issues, this application proposes a method, device, and medium for quantitative analysis of the impact of source-network uncertainty on control performance. The embodiments of this application will be further described below with reference to the accompanying drawings.

[0026] Reference Figure 1 , Figure 1 This illustration shows a method for quantitatively analyzing the impact of source-network uncertainty on control performance, provided by an embodiment of the first aspect of this application. This method is also applied to and executed by an electronic device. In other words, the method can be executed by software or hardware installed in the device, and includes the following steps: Step S100: Based on multiple probability distribution models, generate multiple sets of random parameter combinations to simulate uncertainties on the source side and the network side.

[0027] In this step, based on three probability distribution models—Weibull distribution, uniform distribution, and normal distribution—random parameter combinations are generated to simulate the uncertainties of photovoltaic power output on the source side, the uncertainties of control system parameters, and the uncertainties of load fluctuations on the grid side. Each parameter combination includes three parameter types: active power reference value, speed governor speed droop coefficient, and load active power value.

[0028] It should be noted that various probability distribution models, including Weibull distribution, uniform distribution, and normal distribution, accurately characterize the stochastic characteristics of source-side output, control parameters, and grid-side load, respectively. This achieves a realistic simulation of the uncertain coupling between the source and grid sides from the source of the model, so that the analysis results no longer depend on a few typical working conditions selected by a few people, but are based on the statistical laws of a large number of random scenarios. The conclusions are more comprehensive and reliable, effectively overcoming the distortion problem of traditional deterministic models.

[0029] Step S200: The random parameter combination is automatically configured into the pre-established power system simulation model, and the power system simulation model is driven to complete the corresponding number of simulation runs.

[0030] In this step, the time-domain data of the system's dynamic response is output for each simulation run. Random parameter combinations are sequentially configured into the three-phase grid-connected simulation model of photovoltaic-storage load based on virtual synchronous generators through an automated script. The model is driven to complete batch simulation runs corresponding to the number of parameter combinations. Each simulation run lasts for 10 seconds, with a sampling interval of 0.001 seconds, and outputs time-domain data including system frequency, node voltage, and power waveforms.

[0031] Step S300: Automatically extract at least one controlled variable data from the time domain data, including system frequency, node voltage amplitude, and frequency change rate.

[0032] In this step, a multi-path data extraction mechanism is used to automatically collect three controlled variable data—system frequency, node voltage amplitude, and frequency change rate—from the time-domain data output by the simulation. The multi-path includes three methods: directly reading the simulation data object, accessing data structure fields, and obtaining data from workspace variables.

[0033] In this step, by setting parameters, running simulation, and recording data, automatic batch configuration of parameters, automatic continuous execution of simulation, and automatic data collection are achieved, which greatly improves the efficiency of analysis and makes it possible to perform simulation analysis on hundreds or thousands of random scenarios, providing a data foundation for subsequent quantitative statistics.

[0034] Step S400: Based on the extracted controlled variable data, calculate at least two performance indicators from steady-state error, overshoot, control energy, and control error integral. Based on the data from each simulation, establish a mapping relationship between random parameter combinations and performance indicators to quantify the influence of different uncertainty factors on various control performance indicators.

[0035] In this step, based on the collected controlled variable data, four performance indicators of the system are calculated: steady-state error, overshoot, control energy, and control error integral. A quantitative mapping relationship between random parameter combinations and performance indicators is established through statistical analysis methods and machine learning algorithms. Specifically, multiple regression analysis and sensitivity analysis methods are used to quantify the degree of influence of different uncertainty factors on each control performance indicator.

[0036] It should be noted that the quantitative analysis results are saved and archived through the data management module. This module uses a timestamp naming mechanism to generate unique file identifiers and saves the processed controlled variable data, calculated performance indicators, and established mapping relationships to a structured MAT data file. At the same time, it automatically saves the analysis graphs of voltage and frequency and generates a control performance impact assessment report containing key findings and statistical conclusions. The data management module supports batch export and format conversion of data to ensure the traceability and reusability of the analysis results, providing a complete quantitative analysis basis for control system design.

[0037] It should be noted that expanding the analytical perspective from a single indicator (such as focusing only on frequency stability) to a multi-dimensional and comprehensive indicator system that covers steady-state performance (steady-state error), dynamic performance (overshoot), control cost (control energy), and overall performance (control error integral) enables the evaluation results to comprehensively reflect the performance of the control system in different aspects, avoids one-sidedness, and provides accurate evaluation criteria for the refined design of control strategies and multi-objective optimization.

[0038] It should be noted that by using massive amounts of simulation data and employing data analysis methods (such as regression analysis and sensitivity analysis), a clear and quantifiable mapping relationship is established between random inputs (parameter combinations) and system outputs (performance indicators), transforming the originally vague concept of "impact" into a precise "degree of impact." For example, it can quantitatively answer key questions such as "How much will a 10% increase in the active power reference value lead to an average increase in the steady-state error of the system frequency?" This provides direct and reliable data support and theoretical guidance for understanding the system's internal mechanisms and for targeted improvements to the controller.

[0039] It should be noted that the power system simulation model is built on the Matlab / Simulink platform. The parameter control code is written in M ​​language to generate and automatically configure random parameter combinations. The system data acquisition module is deeply integrated with the power system circuit through Simulink signal routing to form a closed-loop monitoring system. The parameter control code dynamically modifies the module parameters in the power system simulation model through the set_param function and the interaction mechanism of model workspace variables, realizing the automatic injection and batch configuration of uncertain parameters.

[0040] The beneficial effects of this application are reflected in the following aspects: Based on multiple probability distribution models, multiple sets of random parameter combinations are generated to simulate uncertainties on both the source and grid sides; these random parameter combinations are automatically configured into a pre-established power system simulation model, driving the model to complete a corresponding number of simulation runs. Each simulation run outputs time-domain data of the system's dynamic response; from the time-domain data, at least one controlled variable data among system frequency, node voltage amplitude, and frequency change rate is automatically extracted; based on the extracted controlled variable data, at least two performance indicators among steady-state error, overshoot, control energy, and control error integral are calculated; and based on the data from each simulation run, a mapping relationship between the random parameter combinations and performance indicators is established to quantify the influence of different uncertainty factors on various control performance indicators. Through this setup, this application can systematically quantify the impact of source-grid dual-side uncertainties on power system control performance, effectively solving the problem that traditional methods cannot comprehensively analyze the coupling effect of dual-side uncertainties.

[0041] Understandably, referring to Figure 2 Step S100 includes, but is not limited to, the following steps: Step S110: Generate multiple sets of active power reference values ​​through Weibull distribution to simulate the uncertainty of the output of photovoltaic power generation units in the power system simulation model.

[0042] In this step, a two-parameter Weibull distribution is used to generate 200 active power reference values. The scale parameter is set to 50,000 and the shape parameter is set to 2.5. The generated values ​​are limited to the range of 10,000W to 100,000W, which is used to accurately simulate the probability distribution characteristics of the output of photovoltaic power generation units.

[0043] Step S120: Generate multiple sets of governor speed droop coefficients by uniform distribution to simulate the parameter uncertainty of the virtual synchronous generator control unit in the power system simulation model.

[0044] In this step, 50 governor speed droop coefficients are generated using a uniform distribution, with a distribution range of [0,1] and a step size of 0.02, to cover all possible tuning ranges of the system control parameters.

[0045] Step S130: Generate multiple sets of load active power values ​​through normal distribution to simulate the power fluctuation uncertainty of grid-side load units in the power system simulation model.

[0046] In this step, 200 active power values ​​of the load are generated using a normal distribution, with a mean of 50,000W, a standard deviation of 15,000W, and a value range limited to 10,000W to 100,000W, to simulate the random characteristics of grid-side load fluctuations.

[0047] In this step, the random parameter combination includes at least one of the following: active power reference value, governor speed droop coefficient, and load active power value.

[0048] It should be noted that the random parameter combinations form test cases through a parameter pairing algorithm. Each test case contains a specific combination of an active power reference value, a governor speed droop coefficient, and a load active power value. Through the model workspace variable interaction interface, the generated random parameter combinations are automatically written into the corresponding parameter nodes of the power system simulation model, including the active power parameters of the Three-phase parallel RLC Load module, the reference value parameters of the Constant module, and the irradiance parameters of the photovoltaic module.

[0049] It should be noted that the key operating parameters of the power system simulation model include: active power reference value, governor speed droop coefficient, and load active power value as input uncertainty parameters; system frequency, node voltage amplitude, and frequency change rate as output controlled variable data; among them, the system frequency is monitored in real time by the frequency measurement module, the node voltage amplitude is collected by the three-phase voltage measurement module, and the frequency change rate is calculated by the differential element. All controlled variable data are exported to the MATLAB workspace through the ToWorkspace module for subsequent performance index calculations.

[0050] It should be noted that, based on the characteristics of photovoltaic power output being random, intermittent, and conforming to a specific probability distribution, the Weibull distribution can accurately reflect the actual statistical laws of photovoltaic power generation, solving the problem that traditional deterministic models cannot truly simulate the random characteristics of new energy sources. Considering that the speed droop coefficient of the speed governor usually takes values ​​uniformly within a certain range during engineering tuning, a uniform distribution can comprehensively cover all possible values ​​of the control parameters, avoiding the one-sidedness of analysis results caused by parameter fixation. Utilizing the statistical characteristic that load power fluctuations usually follow a normal distribution, the random variation law of grid-side load can be realistically reproduced, providing accurate input for analyzing the impact of grid-side uncertainties on system performance.

[0051] It is understandable that the power system simulation model is a three-phase grid-connected photovoltaic-storage load model based on a virtual synchronous generator (VSG). The power system simulation model includes: a generation unit, an energy storage and grid connection control unit, and a grid-side unit. The DC output terminal of the generation unit is connected to the DC input terminal of the energy storage and grid connection control unit, and the AC output terminal of the energy storage and grid connection control unit is connected to the grid-side unit through the line impedance.

[0052] It should be noted that the VSG-based three-phase grid-connected photovoltaic-storage load model structure can accurately simulate the interaction characteristics of source-grid-load under a high proportion of new energy access environment, providing a realistic test environment for analyzing two-sided uncertainties. By limiting the electrical connection methods between the power generation unit, energy storage and grid-connection control unit and grid-side unit, it is ensured that the simulation model can accurately reflect the energy transmission path and interaction mechanism in the actual system.

[0053] Understandably, the power generation unit includes a photovoltaic array and a boost converter circuit, with the boost converter circuit employing the incremental conductance method for maximum power point tracking control. The energy storage and grid-connected control unit includes a battery, a bidirectional DC-DC converter, a three-phase inverter bridge, and a VSG control system. The VSG control system is sequentially connected to a phase pre-synchronization circuit, a rotor mechanical equation circuit, an excitation electromotive force generation circuit, a voltage and current dual closed-loop control circuit, and a virtual impedance circuit. The outputs of the phase pre-synchronization circuit and the excitation electromotive force generation circuit serve as inputs to the rotor mechanical equation circuit and the voltage and current dual closed-loop control circuit, respectively. The output of the voltage and current dual closed-loop control circuit is used to generate a PWM signal to drive the three-phase inverter bridge. The grid-side unit includes a three-phase parallel RLC load.

[0054] It should be noted that the power generation unit adopts a photovoltaic (PV) power generation architecture, whose core components include a PV array and a boost converter circuit. The boost converter circuit achieves maximum power point tracking (MPPT) control through the incremental conductance method. By dynamically adjusting the duty cycle of the power switching transistors, it boosts the output voltage of the PV array to the level required by the DC bus, maximizing the utilization of PV power output. The output characteristics of the PV power generation unit are simulated by changing the irradiance parameter to reflect the uncertainty characteristics of the source-side PV power output, providing configurable input conditions for quantitative analysis. The energy storage and grid connection control unit adopts a battery system architecture based on a bidirectional DC-DC converter. This architecture can autonomously switch between Boost and Buck operating modes to achieve bidirectional energy flow control. Through a dual closed-loop control strategy of voltage outer loop and current inner loop, this unit maintains the stability of the DC bus voltage while achieving smooth control of the battery charging and discharging current. The energy storage unit works in conjunction with the virtual synchronous generator control system to provide necessary inertial support and power buffering for the system, effectively suppressing power fluctuations caused by uncertainties on both the source and grid sides. The DC output terminal of the power generation unit is directly connected to the DC input terminal of the energy storage unit to form a DC power transmission channel; the AC output terminal of the energy storage and grid-connected control unit is connected to the grid-side unit through the line impedance to form an AC grid-connected channel. This architecture can realistically simulate the energy interaction process between the source, grid and load in the actual power system, and provide an accurate simulation platform for analyzing the impact of two-sided uncertainties on the system control performance.

[0055] It should be noted that the maximum power point tracking control of the power generation unit ensures efficient energy capture on the source side, the power balance control of the energy storage unit maintains the instantaneous power balance of the system, and the virtual synchronous generator control provides grid support functions. The various control links achieve coordinated operation through signal interaction. Among them, the rotor mechanical equation circuit dynamically adjusts the control parameters according to the real-time operating status of the system to ensure that the system can maintain excellent control performance under different operating conditions.

[0056] It should be noted that the phase pre-synchronization circuit in the VSG control system adopts a discrete PI controller with a proportional coefficient of 2 and an integral coefficient of 20; the voltage pre-synchronization circuit adopts a discrete PI controller with a proportional coefficient of 0.0001 and an integral coefficient of 0.02; the rotor mechanical equation circuit adopts an adaptive parameter control strategy, dynamically adjusting the moment of inertia and damping coefficient according to the real-time operating status of the system; the excitation electromotive force generation circuit adopts an integral regulation mechanism with an integral coefficient of 1.1547e-5; and the voltage and current dual closed-loop control circuit adopts decoupled control in the dq coordinate system, outputting six PWM signals to drive the three-phase inverter bridge.

[0057] It should be noted that the initialization process of the power system simulation model includes: opening and loading the pre-established three-phase grid-connected model of photovoltaic-storage load based on virtual synchronous generator (VSG), fully loading the model into the memory workspace, performing model parameter verification and component connection status checks, and outputting a ready signal after completing model initialization; the process of driving the power system simulation model to complete the corresponding number of simulation runs is implemented through the simulation control module. This module adopts a progress monitoring mechanism, pre-allocates storage arrays for voltage and frequency results, creates a simulation progress indicator, and automatically obtains the current simulation parameter combination and updates the progress display before each simulation run; during the simulation, the operating status is monitored in real time, and simulation anomalies are captured and handled; after the simulation is completed, the output data is automatically saved, and the data acquisition quality is verified through the debugging information interface.

[0058] In some embodiments, the battery has a rated capacity of 100Ah and the DC bus voltage is stable at 800V; the grid-side unit adopts a three-phase parallel RLC load structure, and the load impedance parameter can be dynamically adjusted to simulate different types of grid faults and load changes; the power generation unit is connected to the energy storage and grid connection control unit through the DC bus, and the energy storage and grid connection control unit is connected to the grid through an LCL filter with a filter inductance of 2mH and a filter capacitor of 50μF.

[0059] Understandably, referring to Figure 3 The rotor mechanical equation circuit is configured as follows: Step S201: Based on the real-time operating state variables of the power system simulation model, dynamically adjust the values ​​of the moment of inertia and damping coefficient in the rotor mechanical equation circuit.

[0060] In this step, the real-time operating status variable is the system frequency deviation or frequency change rate.

[0061] In this step, the system frequency deviation and frequency change rate are monitored in real time, and a frequency deviation threshold and a frequency change rate threshold are set. When the system frequency deviation exceeds the threshold, the rotational inertia and damping coefficient are dynamically adjusted using a fuzzy logic control algorithm.

[0062] It should be noted that, based on the two key operating indicators of system frequency deviation or frequency change rate, the system can promptly detect changes in system state; by adaptively adjusting the moment of inertia and damping coefficient, the control system can better cope with disturbances caused by uncertainties on both the source and network sides, thereby improving the system's stability and adaptability.

[0063] Understandably, referring to Figure 4 Step S300 includes, but is not limited to, the following steps: Step S310: Try to obtain the controlled variable data through various data interfaces in sequence.

[0064] In this step, the subsequent data interface is enabled when the previous data interface fails to retrieve data.

[0065] It should be noted that by setting data interfaces with different priorities, key data can be successfully acquired in various simulation environments; when one interface fails, a backup interface is automatically activated, which improves the robustness and automation of the data analysis process.

[0066] It should be noted that the process of automatically extracting controlled variable data from time-domain data employs a multiple backup path mechanism, executed in priority order: the primary path accesses signal data such as system frequency, node voltage amplitude, and frequency change rate through the `get` method of `Simulink.SimulationData.DataSet`; the secondary path directly accesses the structure fields of the `Simulink.SimulationOutput` object; the backup path obtains data from time-series variables exported from the `ToWorkspace` module in the MATLAB workspace; the data extraction process is equipped with exception handling functionality, automatically switching to the next path when one path fails, and outputting warning messages when all paths fail. The extraction process automatically adapts to the parameter naming of the actual module, supporting dynamic identification and matching of variable names.

[0067] For example, specific implementations of the multi-path data extraction mechanism include: First priority path: Use Simulink's get_param function and Simulink.SimulationData.BlockData.get method to directly extract system frequency, voltage amplitude, and frequency change rate signals from the simulation data log; Second priority path: Read the time-domain signal data stored in the data structure by accessing the find function and getElement method of the Simulink.SimulationOutput object; Third priority path: Obtain data from time series variables exported from the To Workspace module in the MATLAB base workspace; The multi-path system employs an exception handling mechanism. When data retrieval fails on a certain path, an error log is automatically recorded and the system switches to the next priority path.

[0068] For example, refer to Figures 7 to 14 In the diagram, Voltage pre-synchronization represents the voltage pre-synchronization circuit, Phase pre-synchronization represents the phase pre-synchronization circuit, Rotor Mechanical Equation represents the rotor mechanical equation circuit, Excitation Electromotive Force Equation represents the excitation electromotive force generation circuit, dq-frame decomposition represents the dq coordinate transformation circuit, Dual-loop control of voltage and current represents the voltage and current dual closed-loop control circuit, and energy storage represents the energy storage circuit. Battery represents the energy storage unit, PV represents the photovoltaic power generation unit, signals represents the signal generation unit, Lf1 represents the source-side filter inductor, pcc1 represents the source-side monitoring point, pcc represents the grid-connected monitoring point, Cf2 represents the grid-side filter capacitor, Lf2 represents the grid-side filter inductor, grid represents the grid-side monitoring point, vg represents the grid-side voltage, Vabc_pcc represents the grid-connected three-phase voltage, Iabc_pcc represents the grid-connected three-phase current, wt represents the phase angle, Pref represents the active power reference value, Pref1 represents the active power standard value, Pe represents the grid active power, Qe represents the grid reactive power, Qref represents the reactive power reference value, and f represents the system frequency.

[0069] Understandably, referring to Figure 5 After step S300, the following steps are included, but are not limited to: Step S301: Verify the consistency of the lengths of the time vector and the numerical vector of the controlled variable data, remove non-numerical and infinite values ​​from the controlled variable data, and convert the processed controlled variable data into a column vector in a standard format.

[0070] It should be noted that by performing length consistency checks and outlier removal, the impact of data quality issues on the analysis results has been eliminated; the unified column vector format provides a standardized data foundation for performance index calculation and mapping relationship establishment.

[0071] It should be noted that the standardization processing of the controlled variable data includes: verifying the consistency of the lengths of the time vector and the numerical vector, and using linear interpolation to solve the length mismatch problem; using data cleaning algorithms to remove non-numerical and infinite values, and using the neighborhood mean method to replace outlier data points; converting the processed data into an N×2 standard column vector format, and performing data integrity verification. The standardization process is equipped with data quality monitoring functions, performing range verification and consistency checks on the processed data to ensure that the data meets the input requirements for subsequent performance index calculations. Valid time points and valid values ​​are retained during the processing, and a complete anomaly capture and handling mechanism is in place.

[0072] Understandably, referring to Figure 6 In the power system simulation model, various typical fault conditions are set up. In step S400, a mapping relationship between random parameter combinations and performance indicators is established to quantify the impact of different uncertainties on various control performance indicators, including but not limited to the following steps: Step S410: By comparing and analyzing the mapping relationship between random parameter combinations and performance indicators under various typical fault conditions, a control performance impact assessment report is generated to guide the design of the control system.

[0073] In this step, typical fault conditions include simulated sudden drop in wind and solar power generation, simulated continuous power imbalance, simulated short-circuit fault disconnection, simulated load surge, and simulated sudden drop in renewable energy output.

[0074] It should be noted that the five typical fault conditions basically cover the main types of disturbances that the power system may face, ensuring the comprehensiveness of the analysis results; by generating a control performance impact assessment report, the analysis results are transformed into technical documents that can directly guide engineering practice.

[0075] For example, the performance analysis process includes a results visualization module. This module employs an automated plotting mechanism to create multiple subplot display windows, plotting dynamic response curves for system frequency, node voltage amplitude, and frequency change rate, respectively. The plotting process automatically calculates reasonable coordinate axis ranges, sets differentiated line types, colors, and line width attributes, and generates legends containing simulation parameter information. The visualization module supports vertical stitching and comparative analysis of curve data, can automatically identify invalid data and provide warning prompts, and includes automatically adjusted X-axis and Y-axis ranges, a 10% display margin, and optimized display position and size of the graph window, ultimately generating professional-grade analysis charts.

[0076] It should be noted that various typical fault conditions are set in the power system simulation model to verify the effectiveness of the method: the sudden drop in wind and solar power generation is simulated by a step change in irradiance; the continuous power imbalance is simulated by a sudden load surge; the short-circuit fault clearing is simulated by a three-phase short-circuit-to-ground fault; the load surge is simulated by a step change in load; and the sudden drop in renewable energy output is simulated by a rapid decrease in wind turbine output. Based on the simulation data under these typical conditions, a quantitative mapping relationship between random parameter combinations and performance indicators is established. The influence weight of each uncertainty factor is determined through correlation analysis and regression analysis. Finally, a control performance impact assessment report containing sensitivity analysis results and optimization suggestions is generated.

[0077] It should be noted that VSG stands for Virtual Synchronous Generator; PWM stands for Pulse Width Modulation; LCL stands for Inductor-Capacitor-Inductor, which is a filter topology. In this application, it specifically refers to the specific circuit form used in the topology sub-circuit of the grid-connected control unit, which consists of two inductors and one capacitor, used to efficiently filter out high-frequency harmonics generated by the inverter and ensure grid-connected power quality; DC-DC stands for Direct Current to Direct Current, which is a power conversion circuit; and PI stands for Proportional-Integral, which is a simplified form of the PID controller (with the derivative term removed).

[0078] Optionally, such as Figure 15As shown, the second aspect of this application also provides an electronic device 10, including a processor 11 and a memory 12. The memory 12 stores a program or instructions that can run on the processor 11. When the program or instructions are executed by the processor 11, they implement the various processes of the first aspect of the quantitative analysis method for source-network uncertainty on control performance and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0079] It should be noted that the devices in the embodiments of this application include: servers, terminals, or other devices besides terminals.

[0080] The above device structure does not constitute a limitation on the device. The device may include more or fewer components than illustrated, or combine certain components, or arrange different components. For example, the input unit may include a graphics processing unit (GPU) and a microphone, and the display unit may use a liquid crystal display, organic light-emitting diode, or other forms to configure the display panel. The user input unit includes at least one of a touch panel and other input devices. A touch panel is also called a touch screen. Other input devices may include, but are not limited to, physical keyboards, function keys (such as volume control buttons, power buttons, etc.), trackballs, mice, and joysticks, which will not be described in detail here.

[0081] Memory can be used to store software programs and various data. Memory can primarily include a first storage area for storing programs or instructions and a second storage area for storing data. The first storage area can store the operating system, application programs or instructions required for at least one function (such as sound playback, image playback, etc.). Furthermore, memory can include volatile memory or non-volatile memory, or both. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (Synchlink DRAM, SLDRAM), and direct memory bus RAM (DRRAM).

[0082] The processor may include one or more processing units; optionally, the processor integrates an application processor and a modem processor, wherein the application processor mainly handles operations related to the operating system, user interface, and applications, while the modem processor mainly handles wireless communication signals, such as a baseband processor. It is understood that the aforementioned modem processor may also not be integrated into the processor.

[0083] This application also provides a readable storage medium storing a program or instructions. When executed by a processor, the program or instructions implement the various processes of the above-described first aspect of the quantitative analysis method for source-network uncertainty on control performance, and achieve the same technical effect. To avoid repetition, these will not be described again here. The processor is the processor in the device described above. The readable storage medium includes computer-readable storage media such as ROM, RAM, magnetic disk, or optical disk. It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatus in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in an order different from that described. In addition, features described with reference to certain examples may be combined in other examples.

[0084] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of this application.

[0085] In the description of the embodiments of this application, the terms "first," "second," "third," and "fourth" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first," "second," "third," or "fourth" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0086] In the description of the embodiments of this application, specific features, structures, materials, or characteristics may be combined in any suitable manner in one or more embodiments or examples.

[0087] Although embodiments of this application have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of this application, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for quantitatively analyzing the impact of source-network uncertainty on control performance, characterized in that, include: Based on multiple probability distribution models, multiple sets of random parameter combinations are generated to simulate uncertainties on the source side and the network side; The random parameter combination is automatically configured into a pre-established power system simulation model, and the power system simulation model is driven to complete the corresponding number of simulation runs, wherein each simulation run outputs the time domain data of the system dynamic response; Automatically extract at least one controlled variable from the time-domain data, including system frequency, node voltage amplitude, and frequency change rate. Based on the extracted controlled variable data, at least two performance indicators are calculated from steady-state error, overshoot, control energy, and control error integral. Based on the data from each simulation, a mapping relationship between the random parameter combination and the performance indicators is established to quantify the influence of different uncertainty factors on each control performance indicator.

2. The method for quantitatively analyzing the impact of source-network uncertainty on control performance according to claim 1, characterized in that, The method generates multiple sets of random parameter combinations based on various probability distribution models to simulate uncertainties at the source and network sides, including: Multiple sets of active power reference values ​​are generated using the Weibull distribution to simulate the uncertainty of the output of the photovoltaic power generation unit in the power system simulation model. Multiple sets of governor speed droop coefficients are generated by uniform distribution to simulate the parameter uncertainty of the virtual synchronous generator control unit in the power system simulation model. Multiple sets of active power values ​​of the load are generated by normal distribution to simulate the power fluctuation uncertainty of the grid-side load unit in the power system simulation model; The random parameter combination includes at least one of the active power reference value, the speed governor speed droop coefficient, and the load active power value.

3. The method for quantitatively analyzing the impact of source-network uncertainty on control performance according to claim 1, characterized in that, The power system simulation model is a three-phase grid-connected photovoltaic-storage load model based on a virtual synchronous generator (VSG). The power system simulation model includes a generation unit, an energy storage and grid connection control unit, and a grid-side unit. The DC output terminal of the generation unit is connected to the DC input terminal of the energy storage and grid connection control unit, and the AC output terminal of the energy storage and grid connection control unit is connected to the grid-side unit through line impedance.

4. The method for quantitatively analyzing the impact of source-network uncertainty on control performance according to claim 3, characterized in that, The power generation unit includes a photovoltaic array and a boost circuit, wherein the boost circuit uses the incremental conductance method for maximum power point tracking control. The energy storage and grid connection control unit includes a battery, a bidirectional DC-DC converter, a three-phase inverter bridge, and a VSG control system; The VSG control system is sequentially connected to a phase pre-synchronization circuit, a rotor mechanical equation circuit, an excitation electromotive force generation circuit, a voltage and current dual closed-loop control circuit, and a virtual impedance circuit. The outputs of the phase pre-synchronization circuit and the excitation electromotive force generation circuit serve as the inputs of the rotor mechanical equation circuit and the voltage and current dual closed-loop control circuit, respectively. The output of the voltage and current dual closed-loop control circuit is used to generate a PWM signal to drive the three-phase inverter bridge. The grid-side unit includes a three-phase parallel RLC load.

5. The method for quantitatively analyzing the impact of source-network uncertainty on control performance according to claim 4, characterized in that, The rotor mechanical equation circuit is configured as follows: Based on the real-time operating state variables of the power system simulation model, the values ​​of the moment of inertia and damping coefficient in the rotor mechanical equation circuit are dynamically adjusted, where the real-time operating state variables are the system frequency deviation or frequency change rate.

6. The method for quantitative analysis of the impact of source-network uncertainty on control performance according to claim 1, characterized in that, The step of automatically extracting at least one controlled variable data from the time-domain data, including system frequency, node voltage amplitude, and frequency change rate, includes: The controlled quantity data is attempted to be obtained through multiple data interfaces in sequence; wherein, the subsequent data interface is activated when the previous data interface fails to obtain data.

7. The method for quantitatively analyzing the impact of source-network uncertainty on control performance according to any one of claims 1 or 6, characterized in that, After automatically extracting at least one controlled variable data from the time-domain data, including system frequency, node voltage amplitude, and frequency change rate, the process includes: Verify the consistency of the lengths of the time vector and the numerical vector of the controlled variable data, remove non-numerical and infinite values ​​from the controlled variable data, and convert the processed controlled variable data into a column vector in a standard format.

8. The method for quantitative analysis of the impact of source-network uncertainty on control performance according to claim 1, characterized in that, In a power system simulation model, various typical fault conditions are set up. The mapping relationship between the random parameter combinations and the performance indicators is established to quantify the impact of different uncertainties on various control performance indicators, including: By comparing and analyzing the mapping relationship between the random parameter combinations and the performance indicators under various typical fault conditions, a control performance impact assessment report is generated to guide the design of the control system. The typical fault conditions include simulated sudden drop in wind and solar power generation, simulated continuous power imbalance, simulated short-circuit fault followed by disconnection, simulated load surge, and simulated sudden drop in new energy output.

9. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements: a method for quantitative analysis of the impact of source-network uncertainty on control performance as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions for: a method for quantitative analysis of the impact of source-network uncertainty on control performance as described in any one of claims 1 to 8.

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