A high-proportion new energy power system frequency safety domain analysis method and system

Abstract

The application provides a high-proportion new energy power system frequency safety domain analysis method and system, which comprises the following steps: firstly, a power system frequency dynamic equivalent model considering the frequency modulation characteristics of new energy and the influence of low voltage ride through is constructed, thereby forming a model basis for frequency safety analysis under different disturbances and system compositions; then, a frequency safety correlation variable scanning space generation process based on equidistant network division is designed, and the frequency dynamic change process simulation under active impact is carried out in combination with the constructed frequency dynamic equivalent model, so as to obtain a system frequency response curve; finally, a parameter safety domain for power grid frequency safety is constructed. Based on the method, a corresponding system is also provided. The application constructs an aggregated frequency response model considering the differences between new energy control modes and new energy low voltage ride through, so that the new energy frequency modulation configuration under the limitation of frequency safety indexes and the system equivalent parameter safety domain boundary can be obtained through simulation under certain fault scenes.

Description

Technical Field

[0001] This invention belongs to the field of power system stability control, and specifically relates to a frequency security domain analysis method and system for high-proportion new energy power systems. Background Technology

[0002] Power system frequency is a crucial indicator of the overall operational safety level of the system. After a large-scale disturbance, the power system frequency undergoes a transient change process and eventually tends towards a steady state. The amplitude, rate of change, and steady-state deviation of frequency throughout the dynamic response process directly affect the safe and stable operation of the power system. Generally, the power system frequency should be maintained within a safe range, meaning that after a disturbance, the system frequency remains within the grid's permissible operating range throughout the entire dynamic response process and the final steady-state process, without exhibiting instability phenomena that could jeopardize the safe operation of generating units, grid disconnection, or large-scale load losses.

[0003] Under conditions of large disturbances, the frequency security and stability of the power system depends on various frequency regulation resources available for frequency regulation within the system. These include the inherent inertia and primary frequency regulation capability of traditional synchronous generator units, as well as the frequency support capability provided by renewable energy sources through additional control. The latter typically connects to the grid via power electronic converters, which have relatively weak inertia support and primary frequency regulation capability. Currently, the power industry is accelerating its evolution from a structure dominated by traditional synchronous generator units to a clean and low-carbon energy structure dominated by renewable energy sources such as wind power and photovoltaics. The large-scale replacement of synchronous generator units by renewable energy sources leads to a significant decrease in system inertia and damping levels, making it prone to problems such as drastic frequency changes and increased frequency deviations under conditions of large disturbances. On the other hand, although some renewable energy sources can provide a certain frequency support capability through additional control, in scenarios of severe voltage fluctuations caused by large-scale power deficits, some renewable energy units may enter a low-voltage ride-through state, weakening their active power output and frequency regulation capability. This further amplifies the equivalent power deficit and reduces the system's frequency regulation capability, adversely affecting system frequency security. The combination of the above factors increases the risk of system frequency exceeding safety boundaries, making it difficult to guarantee the frequency safety and stability of the power system. Therefore, the design of frequency regulation systems that take into account the dynamic characteristics of new energy sources and the impact of low voltage ride-through has become an important issue that high-proportion new energy power systems need to address. Constructing a power system frequency safety domain that takes into account the impact of new energy sources is one of the important tasks to solve the above problems.

[0004] Existing methods for configuring system frequency regulation capacity in power systems considering the penetration of renewable energy typically rely on low-order aggregation models such as traditional system frequency response models and average system frequency models. These models combine renewable energy-related control to construct a power system frequency response model with synchronous generator frequency response as the primary component and renewable energy participation. Power system stability indices are then solved using simulation or analytical methods, and their correlation with power system frequency regulation configuration is analyzed to guide frequency regulation capacity configuration. However, these modeling methods suffer from several drawbacks. Firstly, they are often based on the assumption of equivalent aggregation, making it difficult to simultaneously characterize the differentiated dynamic characteristics of multiple frequency regulation resources. Furthermore, they fail to incorporate short-term power deficits and weakened frequency regulation capacity resulting from low-voltage ride-through of renewable energy into a unified modeling framework. This leads to frequency response models that fail to accurately reflect the true dynamic behavior of the system under high-proportion renewable energy conditions, thus affecting the accuracy of frequency security boundary characterization. Secondly, these analytical methods often rely on sensitivity analysis based on single frequency regulation capacity indices or single-variable parameter changes. This relatively singular analytical dimension makes it difficult to systematically reveal the characteristics of the frequency security boundary from a high-dimensional perspective of multivariate coupling relationships, limiting the accuracy of frequency regulation capacity configuration decisions. The key challenge of current research is to balance the characteristics of multivariate frequency modulation subjects with the impact of low voltage ride-through within a unified modeling framework, and to guide the configuration of system frequency modulation capabilities under multivariable coupling dimensions. Summary of the Invention

[0005] To address the aforementioned technical issues, this invention proposes a frequency security domain analysis method and system for high-proportion renewable energy power systems. By constructing an aggregated frequency response model that considers both the differences in renewable energy control methods and low-voltage ride-through of renewable energy, this method can, under certain fault scenarios, obtain the renewable energy frequency regulation configuration and the system's equivalent parameter security domain boundary through simulation, provided guidance for the planning and design of frequency regulation capabilities and safe operation of new power systems.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a frequency security domain analysis method for high-proportion renewable energy power systems, comprising the following steps: Frequency response sub-models for synchronous generating units and renewable energy generating units are established separately. The renewable energy generating units include grid-connected renewable energy generating units and grid-linked renewable energy generating units. A power deficit time-domain model for renewable energy generating units under low voltage ride-through operation is also established. The transfer function corresponding to the frequency response sub-model of the synchronous generating unit is fused with the transfer function corresponding to the frequency response sub-model of the grid-connected renewable energy generating unit to obtain a fused transfer function. The transfer function corresponding to the frequency response sub-model of the grid-linked renewable energy generating unit is used as a feedback loop to construct a closed-loop transfer function. The power deficit time-domain model is superimposed with a preset long-term power deficit and used together as the power deficit input, which is applied to the closed-loop transfer function to form a converged frequency response model. The proportion of grid-connected renewable energy to the total renewable energy capacity is taken as the first independent variable, and the proportion of grid-connected renewable energy with droop control is taken as the second independent variable. The first and second independent variables are discretized to obtain the first and second independent variable sequences respectively. Each element in the first independent variable sequence is cross-combined with each element in the second independent variable sequence to form a two-dimensional parameter space. Based on the aggregated frequency response model, frequency dynamic simulation is performed on each set of parameter combinations in the two-dimensional parameter space to obtain the frequency response curve. A multidimensional frequency safety index threshold is set, and a multidimensional evaluation of frequency safety is performed based on the frequency response curve to identify the safety boundaries of parameter combinations under different safety dimensions. Within the design parameter space of the frequency modulation system and the equivalent parameter space of the system, the safety point set region is fitted and the intersection is taken to form a parameter region that meets the frequency safety requirements.

[0007] Secondly, the present invention provides a frequency security domain analysis system for high-proportion new energy power systems, comprising: The model building module is used to establish frequency response sub-models for synchronous generator units and new energy generator units, including grid-connected and grid-linked new energy generator units; and to establish a power deficit time-domain model for new energy generator units under low voltage ride-through operation; to fuse the transfer function corresponding to the frequency response sub-model of the synchronous generator unit with the transfer function corresponding to the frequency response sub-model of the grid-connected new energy generator unit to obtain a fused transfer function; to use the transfer function corresponding to the frequency response sub-model of the grid-linked new energy generator unit as a feedback loop to construct a closed-loop transfer function; and to superimpose the power deficit time-domain model with a preset long-term power deficit, using them together as the power deficit input, which is applied to the closed-loop transfer function to form a converged frequency response model. The frequency simulation module uses the proportion of grid-connected renewable energy to the total renewable energy capacity as the first independent variable and the proportion of grid-connected renewable energy with droop control as the second independent variable. It discretizes the first and second independent variables to obtain first and second independent variable sequences. Each element in the first independent variable sequence is cross-combined with each element in the second independent variable sequence to form a two-dimensional parameter space. Based on the aggregated frequency response model, it performs frequency dynamic simulation on each parameter combination in the two-dimensional parameter space to obtain the frequency response curve. The security domain output module is used to set multi-dimensional frequency security index thresholds, perform multi-dimensional frequency security evaluation based on the frequency response curve, identify the security boundaries of parameter combinations under different security dimensions, and fit and intersect the security point set region within the frequency modulation system design parameter space and system equivalent parameter space to form a parameter region that meets frequency security requirements.

[0008] The effects described in the invention are merely those of the embodiments, and not all the effects of the invention. One of the above technical solutions has the following advantages or beneficial effects: This invention proposes a frequency security domain analysis method and system for high-proportion renewable energy power systems. The method involves: first, constructing a dynamic equivalent model of the power system frequency considering the frequency regulation characteristics of renewable energy and the impact of low-voltage ride-through, forming the model foundation for frequency security analysis under different disturbances and system configurations; then, designing a process for generating a frequency security correlation variable scanning space based on an equally spaced network, and simulating the dynamic frequency change process under active power impact using the constructed dynamic equivalent model to obtain the system frequency response curve; finally, setting multi-dimensional frequency security index thresholds, performing multi-dimensional frequency security evaluation based on the frequency response curve, determining the security boundaries of parameter combinations under different security dimensions, dividing the parameter space into safe and unsafe regions, and fitting and intersecting the safe point set regions within the frequency regulation system design parameter space and the system equivalent parameter space to form a parameter region that meets frequency security requirements. Based on this frequency security domain analysis method for high-proportion renewable energy power systems, a frequency security domain analysis system for high-proportion renewable energy power systems is also proposed. This invention constructs an aggregated frequency response model that takes into account the differences in new energy control methods and the low-voltage ride-through of new energy. It can obtain the frequency regulation configuration of new energy and the safety domain boundary of the system equivalent parameters under frequency safety index constraints through simulation under certain fault scenarios, and provide guidance for the planning and design of frequency regulation capabilities and safe operation of new power systems.

[0009] This invention overcomes the problem that traditional aggregated frequency response models cannot simultaneously reflect the coupling effects of frequency regulation capability degradation and transient power deficit by constructing a reduced-order aggregated dynamic model that considers the frequency regulation characteristics of new energy sources and the impact of low-voltage ride-through. It also breaks through the limitations of traditional analytical methods that only conduct sensitivity analysis based on single variables or low-dimensional parameter spaces, enabling the systematic characterization of frequency safety and stability boundaries within a high-dimensional parameter space composed of multiple frequency safety-related variables. Given the system's energy structure and anticipated low-voltage ride-through amplitude, it comprehensively considers frequency safety requirements to form the operational safety domain of new energy frequency regulation configuration parameters and system equivalent parameters. This is applicable to the parameter configuration planning and verification of frequency regulation systems in high-proportion new energy power systems, guiding the frequency regulation configuration of grid-connected and grid-linked new energy power sources from a frequency safety perspective. Attached Figure Description

[0010] Figure 1 This is a flowchart of a frequency security domain analysis method for a high-proportion new energy power system proposed in Embodiment 1 of the present invention; Figure 2 This refers to the synchronous machine frequency response model under new energy penetration proposed in Embodiment 1 of the present invention; Figure 3This refers to the frequency response model of a grid-type new energy source using virtual synchronous machine control proposed in Embodiment 1 of the present invention; Figure 4 This refers to the frequency response model of grid-connected new energy based on droop control proposed in Embodiment 1 of the present invention. Figure 5 This is the low-voltage ride-through power time-domain curve proposed in Embodiment 1 of the present invention; Figure 6 This refers to the power system frequency response model that considers the frequency regulation characteristics and operating status of new energy sources, as proposed in Embodiment 1 of the present invention. Figure 7 This is a flowchart of the simulation and data recording process based on parameter space traversal proposed in Embodiment 1 of the present invention; Figure 8 This is a flowchart of the parameter security domain construction process for power grid frequency security proposed in Embodiment 1 of the present invention; Figure 9 This is an application example model of the power system frequency response considering the frequency regulation characteristics and operating status of new energy sources, as proposed in Embodiment 1 of the present invention. Figure 10 This refers to the new energy frequency regulation configuration security domain proposed in Embodiment 1 of the present invention; Figure 11 This refers to the system equivalent parameter security domain proposed in Embodiment 1 of the present invention; Figure 12 This is a schematic diagram of a frequency security domain analysis system for a high-proportion new energy power system proposed in Embodiment 2 of the present invention. Detailed Implementation

[0011] To clearly illustrate the technical features of this solution, the invention will be described in detail below through specific embodiments and in conjunction with the accompanying drawings. The following disclosure provides many different embodiments or examples for implementing different structures of the invention. To simplify the disclosure of the invention, components and arrangements of specific examples are described below. Furthermore, reference numerals and / or letters may be repeated in different examples. This repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed. It should be noted that the components illustrated in the drawings are not necessarily drawn to scale. Descriptions of well-known components, processing techniques, and processes are omitted in this invention to avoid unnecessarily limiting the invention.

[0012] Example 1 Embodiment 1 of this invention proposes a frequency security domain analysis method for high-proportion renewable energy power systems. This method addresses the problem that the complexity of frequency regulation characteristics of renewable energy power sources and the low-voltage ride-through power surge significantly increase the complexity of frequency characteristics in high-proportion renewable energy power systems and make frequency regulation system design difficult.

[0013] Embodiment 1 of this invention proposes a frequency security domain analysis method for power systems with a high proportion of new energy sources. This method is applicable to frequency security analysis and frequency stability control of power systems with a high proportion of new energy sources. It mainly includes three steps: constructing a power system frequency response model that considers the influence of new energy frequency regulation characteristics and operating status; simulation and data recording based on parameter space traversal; and constructing a parameter security domain for grid frequency security. The first part obtains typical parameters of the power system through surveys, aggregates and models different types of renewable energy participating in frequency response models, and combines them to form a power system frequency response model that takes into account different energy types and operating states. The second part sets the frequency regulation configuration of renewable energy sources as independent variables, specifically including the proportion of grid-connected renewable energy sources using virtual synchronous machine control and the configuration proportion of grid-connected renewable energy sources using droop control. These are discretized with a certain step size and combined to form a parameter space. For each set of parameters within the parameter space, the frequency response curve is obtained through time-domain simulation and recorded. The third part sets multi-dimensional frequency security index thresholds, performs multi-dimensional frequency security evaluation based on the frequency response curves, finds the frequency security boundary through interpolation and linear fitting methods, and then divides the parameter space combination into safe and unsafe parts. The parameter space of safe parameter combination distributions under different security dimensions is fitted and the intersection is taken to finally obtain the frequency security domain boundary.

[0014] Figure 1 This is a flowchart of a frequency security domain analysis method for a high-proportion new energy power system proposed in Embodiment 1 of the present invention; In step 1, a power system aggregated frequency response model considering the frequency regulation characteristics of new energy sources and the impact of low-voltage ride-through is constructed. The detailed process is as follows: frequency response sub-models for synchronous generating units and new energy generating units are established separately. The new energy generating units include grid-connected new energy generating units and grid-linked new energy generating units. A power deficit time-domain model of the new energy generating units under low-voltage ride-through operation is also established. The transfer function corresponding to the frequency response sub-model of the synchronous generating units is fused with the transfer function corresponding to the frequency response sub-model of the grid-connected new energy generating units to obtain the fused transfer function. The transfer function corresponding to the frequency response sub-model of the grid-linked new energy generating units is used as a feedback loop to construct a closed-loop transfer function. The power deficit time-domain model is superimposed with a preset long-term power deficit, and the two are used together as the power deficit input, which is applied to the closed-loop transfer function to form the aggregated frequency response model. First, the target system is investigated to obtain typical parameters of various types of energy in the power system, and these parameters are aggregated to form aggregated parameters, including: Synchronous generating units: The total system capacity obtained from the actual system survey is denoted as... The capacity of each synchronous generator unit is denoted as , ...; the inertia of each synchronous generator unit is denoted as... , ...; Damping coefficients of each synchronous generator unit , ...; reheat time constants of each synchronous unit , ...; Power ratio of each synchronous high-pressure cylinder , ...; droop coefficients of each synchronous generator unit , , ...

[0015] The parameters are aggregated to obtain aggregated parameters for modeling, including: the proportion of synchronous unit capacity. Equivalent inertia of synchronous generator units Damping coefficient of synchronous generator unit Synchronous unit reheat time constant The power ratio of the high-pressure cylinder of the synchronous machine Synchronous generator droop coefficient The solution method is shown in equation (1). (1) in, This indicates the number of synchronous generators in the system.

[0016] New energy units: Virtual inertia of grid-connected new energy units obtained from actual system surveys. Virtual damping coefficient of grid-type new energy Grid-type new energy simulation gain coefficient , and the droop control coefficient of grid-type new energy .

[0017] Typical failure scenario: time of failure Long-term power deficit Low voltage ride-through power valley Low voltage ride-through minimum power duration Low voltage ride-through power recovery time Low voltage ride-through ratio under anticipated fault conditions .

[0018] Using the acquired parameters, system frequency response models and low-voltage ride-through power curves were constructed for synchronous machines, grid-connected renewable energy sources, and grid-connected renewable energy sources, respectively, including: Using the power deficit of the input synchronous generator as input and the frequency deviation as output, a frequency response model of the synchronous generator under the penetration of new energy sources is constructed. Figure 2 The frequency response model of the synchronous machine under the penetration of new energy sources proposed in Embodiment 1 of this invention has the following frequency deviation frequency domain expression: (2) in, Indicates frequency deviation; Indicates the power deficit of the input system; The transfer function representing the frequency deviation and power deficit of a synchronous generator unit; This represents the droop coefficient of the synchronous generator unit; Indicates the natural angular frequency; Indicates the damping coefficient of the synchronous generator unit; This indicates the proportion of synchronous generator unit capacity; This represents the reheat time constant of the synchronous generator unit; Indicates the damping ratio; This represents the equivalent inertia of a synchronous generator unit; This indicates the percentage of power supplied by the high-pressure cylinder of the synchronous machine.

[0019] Using the power deficit of grid-connected renewable energy units as input and frequency deviation as output, a frequency response model for grid-connected renewable energy units is obtained. Figure 3 This is the frequency response model of a grid-type new energy source using virtual synchronous machine control proposed in Embodiment 1 of the present invention; its frequency deviation frequency domain expression is: (3) ; in, The transfer function representing the frequency deviation and power deficit of grid-connected new energy generating units; Represents the virtual inertia of grid-type new energy sources; This represents the virtual damping coefficient of grid-type new energy sources; This represents the analog gain coefficient of grid-connected new energy sources; The equivalent virtual damping parameter represents the frequency support characteristics of grid-connected new energy generating units.

[0020] Using the system frequency deviation as input and the frequency regulation output of grid-connected renewable energy units as output, a frequency response model for grid-connected renewable energy is obtained. Figure 4 The frequency response model of grid-connected new energy based on droop control proposed in Embodiment 1 of this invention; the frequency domain expression of the active power output participating in frequency regulation is as follows: (4) in, This indicates that the grid-connected new energy frequency regulation system is contributing power. This represents the transfer function between the active power output of grid-connected renewable energy generating units participating in frequency regulation and the system frequency deviation; This indicates the droop control coefficient for grid-type new energy sources; This indicates the low-voltage ride-through ratio.

[0021] A time-domain model of the power deficit of new energy generating units under low-voltage ride-through operation is established, specifically as follows: (5) (6) in, express The short-term power deficit caused by low voltage ride-through; Indicates the low voltage crossing power valley value Indicates the time when the fault occurred; This indicates the moment when low-voltage ride-through power begins to recover; This indicates the moment when the low-voltage ride-through power is fully restored; This indicates the low-voltage ride-through power recovery rate.

[0022] The proportion of grid-connected renewable energy units to total renewable energy is set as the independent variable. The first independent variable is the frequency response transfer function of synchronous generator units and grid-connected new energy generator units. and Merging , (7) ; ; in, Indicates the transfer function after fusion; Represents the equivalent inertia; This indicates equivalent damping.

[0023] The transfer function corresponding to the frequency response sub-model of the grid-connected new energy power unit is used as a feedback loop to construct a closed-loop transfer function, specifically: (8) ; in, Represents the transfer function of the drooping loop; Indicates the percentage of total capacity; This represents the proportion of grid-connected new energy generating units equipped with droop control, i.e., the second independent variable; The transfer function between frequency deviation and power deficit, i.e., the closed-loop transfer function, is expressed as: (9) Short-term power deficit Compared with the preset long-term power deficit The components are superimposed and used together as power deficit input to form an aggregated frequency response model that takes into account the frequency regulation characteristics and operating status of new energy sources. ; in, This represents the total power deficit in the input aggregate frequency response model.

[0024] Based on the above factors, a power system frequency response model is formed that takes into account the frequency regulation characteristics and operating conditions of new energy sources. Figure 6 The power system frequency response model that considers the frequency regulation characteristics and operating status of new energy sources, as proposed in Embodiment 1 of the present invention, is the aggregated frequency response model. As the first independent variable parameter, As the second independent variable parameter.

[0025] In step 2, a two-dimensional scanning space for the frequency regulation configuration parameters of new energy sources is generated, and frequency dynamic simulation is performed based on the aggregated frequency response model. The detailed process is as follows: the proportion of grid-connected new energy sources to the total new energy capacity is taken as the first independent variable, and the proportion of grid-connected new energy sources with droop control is taken as the second independent variable; the first and second independent variables are discretized to obtain the first and second independent variable sequences respectively; each element in the first independent variable sequence is cross-combined with each element in the second independent variable sequence to form a two-dimensional parameter space; based on the aggregated frequency response model, frequency dynamic simulation is performed on each set of parameter combinations in the two-dimensional parameter space to obtain the frequency response curve. Figure 7 This is a flowchart of the simulation and data recording process based on parameter space traversal proposed in Embodiment 1 of the present invention.

[0026] In step2.1, limit and Both are within the interval [0, 1]. Discretizing the first and second independent variables respectively yields the first and second independent variable sequences, i.e., respectively... and According to step size and Discretize to form the sequence of the first independent variable. Second independent variable sequence ; (10) and Includes One element; Includes One element; in, Indicates rounding down; Indicates the first The value of the ratio of each network structure type; Indicates the first The droop control ratio can be set to a specific value. Indicates the network structure ratio index number; This indicates the droop control ratio index number.

[0027] By combining each element of the first independent variable sequence with each element of the second independent variable sequence, a two-dimensional parameter space is formed as follows: (11) in, The CCP contains Each of the following elements is distinct and represents a... Parameter combinations.

[0028] In step 2.2, based on the aggregated frequency response model, frequency dynamic simulation is performed on each set of parameters in the two-dimensional parameter space to obtain the frequency response curve; specifically: From two-dimensional parameter space In the middle, select a set of parameter combinations that have not yet been simulated. ; The selected parameter combination is used as the variable parameter of the aggregate frequency response model to configure the aggregate frequency response model; Set the total simulation time according to the preset fault scenarios. and time step Dynamic simulation calculations were performed on the system model to obtain the frequency deviation curve of the system frequency relative to the rated frequency. : (12) This is a one-dimensional time series vector that increments by the number of time steps, where the elements are... Indicates the number of times the fault started. The system frequency deviation value at time , the interval between two adjacent time points is . , This represents the total number of simulation moments since the fault began.

[0029] Check if there are any remaining parameter combinations that were not included in the simulation; if so, return from the two-dimensional parameter space. Then select another set of parameter combinations that have not yet been simulated; otherwise, proceed to the next step until the simulation of all parameter combinations in the two-dimensional parameter space is completed.

[0030] In step 3, frequency security multidimensional evaluation and security domain construction are performed based on simulation results. The detailed process includes: setting multidimensional frequency security index thresholds, performing multidimensional frequency security evaluation based on the frequency response curve, identifying the security boundaries of parameter combinations under different security dimensions, and fitting and intersecting the security point set region in the frequency modulation system design parameter space and system equivalent parameter space to form a parameter region that meets frequency security requirements.

[0031] First, referring to the current frequency security standards, a multi-dimensional criterion for power system frequency security is constructed. Frequency security indicators are extracted from simulation results to determine the security boundaries of frequency regulation configuration parameter combinations under different security dimension criteria. The security boundaries under each dimension criterion are then fitted, dividing the frequency regulation configuration parameter space into a safe domain and an unsafe domain. The intersection of the safe domains is then taken to form the system frequency regulation configuration security domain under the multi-dimensional security criteria. Finally, the system energy system frequency regulation configuration parameters are mapped to system equivalent parameters. The security boundaries under each dimension criterion are then fitted, dividing the system equivalent parameter space into a safe domain and an unsafe domain. The intersection of the safe domains is then taken to form the system equivalent parameter security domain under the multi-dimensional security criteria. Figure 8 This is a flowchart of the parameter security domain construction process for power grid frequency security proposed in Embodiment 1 of the present invention.

[0032] Step 3.1 Refer to the current power system frequency security standards to determine the power system frequency security indicators and corresponding thresholds, and extract and record the key indicators characterizing the system frequency security from the simulation results. The specific steps include: multi-dimensional frequency security indicators, including the maximum rate of change of frequency, the extreme value of frequency deviation, and the steady-state frequency deviation. The values ​​of the maximum rate of change of frequency, the extreme value of frequency deviation, and the steady-state frequency deviation are extracted from each frequency response curve obtained from the simulation. The extracted index values ​​are compared with the corresponding security thresholds to determine whether each set of parameters meets the security requirements under each security dimension. Based on the judgment results, the boundary positions between safe and unsafe points are identified in the two-dimensional parameter space for each safety dimension. The boundary points under each safety dimension are determined by interpolation method, and the safety boundary curves of each safety dimension are obtained by fitting. Based on the safety boundary curves of each safety dimension, the parameter regions that meet the requirements of each safety dimension are determined respectively. The intersection of the parameter regions that simultaneously meet the requirements of all safety dimensions is taken to form the new energy frequency regulation configuration safety domain.

[0033] (1) The maximum rate of change of frequency, the extreme value of frequency deviation, and the steady-state frequency deviation are determined as three safety dimensions. The specific values ​​of each threshold are determined according to relevant standards and system operation requirements, and are denoted as follows: , , The security criteria for each dimension are as follows: (13) in, Indicates parameter combination Maximum rate of change of system frequency; Parameter combination The extreme values ​​of the lower system frequency deviation; Indicates parameter combination The system steady-state frequency deviation; when all three conditions of the above safety criteria are met, the parameter combination is determined. Safety.

[0034] (2) Extract the maximum rate of change of frequency Calculate the rate of change of frequency for each simulation time step of all frequency deviation curves and record it as a one-dimensional time-series vector that increments by the time step number. : (14) in, Middle elements Represents the frequency deviation curve In the The rate of change of frequency over a given time interval is calculated using the following formula: (15) in, express Frequency deviation at any given moment; express Frequency deviation at any given moment; Each frequency deviation curve The maximum rate of change of the system frequency is denoted as As shown in equation (16); (16).

[0035] (3) Maximum deviation of extraction frequency Record the value that deviates furthest from the reference value of 0 in each frequency deviation curve, as shown in equation (17); (17) in, satisfy , Represents the frequency deviation curve The maximum deviation of the system frequency.

[0036] (4) Extracting frequency steady-state deviation Set a steady-state criterion threshold ε and record the steady-state frequency deviation: (18) in, , Represents the frequency deviation curve The steady-state deviation of the system frequency.

[0037] The maximum rate of change of frequency, the lowest frequency point, and the steady-state frequency deviation extracted in the above steps are recorded uniformly as follows: (19) in, , , These represent two-dimensional matrices in parameter space representing the maximum rate of change of frequency, the minimum frequency point, and the steady-state frequency deviation, respectively, all of which are of size 1. The element subscripts correspond to the frequency deviation curves. and parameter combinations .

[0038] Step 3.2 Under the multidimensional safety threshold constraint, an interpolation method is used to traverse each point, determine the nearest boundary points of each point, and add them to the boundary point set under the multidimensional safety threshold constraint. Specifically: Initially take Begin the traversal; like , will point Nearby boundary points Add to boundary point set ; At the same time if , will point Nearby boundary points Also add to the boundary point set ; like , will point Nearby boundary points Add to boundary point set ; At the same time if , will point Nearby boundary points Also add to the boundary point set ; like , will point Nearby boundary points Add to boundary point set ; At the same time if , will point Nearby boundary points Also add to the boundary point set ; (5) Parameters If the value is increased by 1, The value equals Proceed to the next step; otherwise, return to step (2). (6) Parameters If the value is increased by 1, The value equals Proceed to the next step, otherwise... The value is 1 and return to step (2).

[0039] Step 3.3 For the set of boundary points of the security domain under the three security dimensions, the boundary lines are obtained by fitting their distribution boundaries through a function. The security domains and non-security domains separated by the boundary points are identified by sampling. The intersection of the security domains under the constraints of the three security dimensions is taken to obtain the complete system composition parameter security domain. The specific steps are as follows: The elements in are denoted as follows: ; The elements in are denoted as follows: ; The elements in are denoted as follows: its subscript , and They respectively represent the first in their respective sets. , and Each element.

[0040] assumed , , The parameter distribution boundary under safety constraints conforms to the form shown in equation (20); (20) Take respectively For the mapping sequence, the first parameter to be identified is calculated using the least squares parameter identification method. Second parameter to be identified ; Similarly, and For the mapping sequence, calculate the third parameter to be identified. Fourth parameter to be identified and the fifth parameter to be identified The sixth parameter to be identified ; Arbitrary parameter space Not on the dividing line , , Points on ; If the discriminant Then it represents The enclosed area is Security domain corresponding to the indicator Otherwise, it is a non-secure domain; Similarly, through the discriminant and choose , Security domain under indicator constraints , ; Filter out separately , , Security domain under constraints , , Then, the intersection of the three is taken to obtain the frequency modulation configuration security domain. As shown in equation (21); (twenty one).

[0041] Step 3.4 Maps each element in the set of security domain boundary points under the three security dimensions from the frequency modulation configuration parameter space to the system equivalent parameter distribution space. The boundary line is obtained by fitting the distribution using a function. The security domains separated by this boundary line are then identified through sampling. The intersection of the security domains under the constraints of the three security dimensions is taken to obtain the complete system equivalent parameter security domain. The detailed steps are as follows: The boundary points in the safety domain of new energy frequency regulation configuration are mapped from the parameter space consisting of the first independent variable and the second independent variable to the equivalent parameter space consisting of the system equivalent inertia and the system equivalent damping. The safety boundary curves for each safety dimension are obtained by fitting them in the equivalent parameter space. Based on the safety boundary curves of each safety dimension, determine the equivalent parameter regions that meet the requirements of each safety dimension. The intersection of the equivalent parameter regions that simultaneously meet all safety dimension requirements is used to form the system equivalent parameter safety domain. Among them, the mapping method calculates the corresponding system equivalent inertia and system equivalent damping through affine transformation based on the capacity ratio of synchronous units, the virtual inertia parameters of grid-type new energy, the droop control parameters of grid-connected new energy, and the values ​​of the first and second independent variables.

[0042] The specific steps are as follows: (1) To Perform an affine transformation on all elements in the set, for the i-th element... element ,Pick , The transformation rules are shown in equation (22); ;(twenty two) After mapping , Recorded as ; Similarly, and Perform an affine transformation on all elements in the array, and take the values ​​of each element. , and , After mapping, retrieve , and , Recorded as and ; assumed , , The distribution boundary of the system's equivalent parameters under safety constraints conforms to the form of equation (23): ;(twenty three) Take respectively As a mapping sequence, the seventh parameter to be identified is calculated using the least squares parameter identification method. and the eighth parameter to be identified Similarly, and For the mapping sequence, calculate the ninth parameter to be identified. The tenth parameter to be identified And the eleventh parameter to be identified and the twelfth parameter to be identified .

[0043] Arbitrary parameter space Not on the dividing line , , Points on ,Pick , After transformation by equation (22), let , Get points ; like Then it represents The enclosed area is Security domain corresponding to the indicator Otherwise, it is a non-secure domain; Similarly, through and choose , Security domain under indicator constraints , ; (6) Select separately , , After determining the safety region under constraints, the intersection of the three factors yields the system's equivalent parameter safety region. As shown in equation (24).

[0044] ;(twenty four) The security domains for renewable energy frequency regulation configuration parameters and system equivalent parameters were constructed. This led to the determination of the proportion of grid-connected renewable energy capacity participating in frequency response using virtual synchronous machine control, in order to ensure frequency security. The proportion of grid-connected renewable energy capacity participating in frequency regulation via vertical control and below. The point formed by the two parameters It should be distributed in Within the designated area, configure the equivalent parameters of the system after frequency regulation by new energy sources. It should be distributed in Within the designated area.

[0045] The frequency security domain analysis method for high-proportion renewable energy power systems proposed in Embodiment 1 of this invention constructs an aggregated frequency response model that takes into account the differences in renewable energy control methods and the low-voltage ride-through of renewable energy. Under certain fault scenarios, it can obtain the renewable energy frequency regulation configuration and the system equivalent parameter security domain boundary through simulation, providing guidance for the planning and design of frequency regulation capabilities and safe operation of new power systems.

[0046] To fully illustrate the implementation process of the frequency security domain analysis method for a high-proportion new energy power system proposed in Embodiment 1 of this invention, its accuracy is verified through simulation. The application steps of the patented method are as follows: Based on the method in step 1, the target system was investigated, and the modeling parameters for power system synchronous generators, new energy sources, and low voltage ride-through scenarios were obtained and aggregated as shown in Tables 1, 2, and 3. Table 1: Typical parameters of synchronous generator units

[0047] Table 2: Typical Parameters of New Energy

[0048] Table 3: Parameters for Low Voltage Ride-Through Scenarios

[0049] Based on the parameters in the table, system frequency response models of synchronous machines, grid-connected renewable energy sources, and grid-following renewable energy sources were constructed on the Matlab / Simulink platform, and low-voltage ride-through power time-domain curves were modeled. The proportion of grid-connected renewable energy sources participating in the frequency response using virtual synchronous machine control was set. The proportion of grid-connected new energy generating units equipped with droop control As the independent variable, Figure 9 This is an application example model of the power system frequency response considering the frequency regulation characteristics and operating status of new energy sources, as proposed in Embodiment 1 of the present invention. Based on the method in step 2, the proportion of grid-connected new energy sources in total new energy sources is set. , and the droop control ratio of grid-type new energy configuration The range of values ​​is and according to step size Discretization forms discrete sequences , Traversing and combining these elements creates a two-dimensional parameter space of size 51×51. .

[0050] For parameter space The simulation iterates through all possible parameter combinations, inputs them into the model, and performs simulations at fixed time steps, recording the resulting frequency curves. It then iterates through all elements in the frequency modulation configuration parameter space, inputting them into the model as parameter configurations, and sets the total simulation time to [value missing]. =30s, simulation step size =0.001s, and a total of 2601 (51×51) frequency deviation curves were obtained from the simulation.

[0051] Based on the method in step 3.1, and referring to the national standard GB / T 40596—2021 "Technical Regulations for Automatic Low-Frequency Load Shedding in Power Systems" and international power system operation experience, the permissible limits for the maximum rate of change of system frequency, the extreme value of frequency deviation, and the steady-state frequency deviation are determined, without triggering serious safety accidents such as large-scale low-frequency load shedding and system instability. , and The above three indicators are used as system frequency security constraints. Key indicators characterizing system frequency security under each parameter combination are extracted from the simulation results and recorded. The steady-state frequency criterion threshold ε is set to 0.01. Two-dimensional matrices recording the maximum rate of frequency change, the lowest frequency point, and the steady-state frequency deviation are obtained. , , All are 51×51 in size, and each element is associated with a parameter space. The elements in the middle correspond one-to-one; Based on the method in step 3.2, in the multidimensional security threshold , , Under constraints, interpolation methods are used to determine the threshold equivalence locations and extract the safety region boundary points, which are denoted as follows: , , ; Based on the method in step 3.3, the security domain boundary point sets under the three security dimensions are respectively... , , By fitting its distribution using the least squares method, the boundary lines of the frequency security domain under each security dimension constraint are obtained as shown in Equation (25). (25) Select the point (0.9, 0.6) that is not on the boundary shown in equation (25), and its corresponding safety index , , Following Step 3.3 (3) and (4), the security domains are divided and their intersections are taken to obtain the complete system composition parameter security domains. As shown in equation (26); (26) Based on the method in step 3.4, the set of security domain boundary points under the three security dimensions is determined. , , Each element in the system is mapped from the frequency modulation configuration parameter space to the system equivalent parameter distribution space, and the mapping method is shown in (27). (27) After mapping, we obtain the set of security domain boundary points with equivalent parameters. , , By fitting its distribution using the least squares method, the boundary lines of the frequency security domain under each security dimension constraint are obtained as shown in equation (28). (28) According to Step 3.4 (4), select the point (0.9, 0.6) that is not on the boundary shown in Equation (28), and obtain the point (10.2, 9.26) after mapping by Equation (27). According to Step 3.4 (5) (6), divide the security domain and take the intersection of the security domains under the three security dimensions to obtain the complete system equivalent parameter security domain as shown in Equation (29). (29) This leads to the conclusion that in this power system, traditional synchronous generator units account for a significant proportion. Expected low voltage ride-through ratio In order to ensure frequency security, the proportion of grid-connected new energy capacity participating in frequency response using virtual synchronous machine control is [not specified]. The proportion of grid-connected renewable energy capacity participating in frequency regulation via vertical control methods. The point formed by the two parameters The system equivalent parameters after frequency regulation by new energy sources should be distributed within the range of equation (26). It should be distributed within the range of equation (29).

[0052] To visualize the security domain distribution, the security domains expressed by equations (26) and (29) are plotted as follows: Figure 10 and Figure 11 As shown. Figure 10 This refers to the new energy frequency regulation configuration security domain proposed in Embodiment 1 of the present invention; Figure 11 The system equivalent parameter security domain proposed in Embodiment 1 of the present invention is shown in the figure. The dashed line represents the security domain boundary line of each security index, and the adjacent colored area is the security domain under the constraint of the index. The gray part represents the new energy frequency regulation configuration security domain that simultaneously satisfies the constraints of three security indices.

[0053] To further verify the correctness of the method, take Figure 10 The point (0.82, 0.52) located within the security region and close to its boundary is... , and located in and Within the security domain, but located The point outside the security domain (0.63, 0.31), i.e. , The two sets of parameters were input into the frequency response model, and the frequency response curve was obtained through simulation. Then, the frequency safety index was obtained to verify the correctness of the method. The results are shown in Table 4.

[0054] Table 4: Simulation verification results of frequency security domain criterion at representative parameter points

[0055] The simulation results show that the parameter points (0.82, 0.52) distributed within the safe domain satisfy the safety constraints of the maximum rate of change of frequency, the extreme value of frequency offset, and the steady-state frequency offset, which is consistent with the distribution of the safe domain. The parameter points (0.63, 0.31) distributed outside the safe domain satisfy the safety constraints of the maximum rate of change of frequency and the steady-state frequency offset, but do not satisfy the safety constraint of the extreme value of frequency offset, which is consistent with the distribution of the safe domain.

[0056] Example 2 Based on the frequency security domain analysis method for high-proportion renewable energy power systems proposed in Embodiment 1 of this invention, Embodiment 2 of this invention also proposes a frequency security domain analysis system for high-proportion renewable energy power systems. Figure 12 This is a schematic diagram of a frequency security domain analysis system for a high-proportion new energy power system proposed in Embodiment 2 of the present invention. The system includes: The model building module is used to establish frequency response sub-models for synchronous generator units and new energy generator units, including grid-connected and grid-linked new energy generator units; and to establish a power deficit time-domain model for new energy generator units under low voltage ride-through operation; to fuse the transfer function corresponding to the frequency response sub-model of the synchronous generator unit with the transfer function corresponding to the frequency response sub-model of the grid-connected new energy generator unit to obtain a fused transfer function; to use the transfer function corresponding to the frequency response sub-model of the grid-linked new energy generator unit as a feedback loop to construct a closed-loop transfer function; and to superimpose the power deficit time-domain model with a preset long-term power deficit, using them together as the power deficit input, which is applied to the closed-loop transfer function to form a converged frequency response model. The frequency simulation module uses the proportion of grid-connected renewable energy to the total renewable energy capacity as the first independent variable and the proportion of grid-connected renewable energy with droop control as the second independent variable. It discretizes the first and second independent variables to obtain first and second independent variable sequences. Each element in the first independent variable sequence is cross-combined with each element in the second independent variable sequence to form a two-dimensional parameter space. Based on the aggregated frequency response model, it performs frequency dynamic simulation on each parameter combination in the two-dimensional parameter space to obtain the frequency response curve. The security domain output module is used to set multi-dimensional frequency security index thresholds, perform multi-dimensional frequency security evaluation based on the frequency response curve, identify the security boundaries of parameter combinations under different security dimensions, and fit and intersect the security point set region within the frequency modulation system design parameter space and system equivalent parameter space to form a parameter region that meets frequency security requirements.

[0057] The description of the relevant parts of the frequency security domain analysis system for a high proportion of new energy power system provided in Embodiment 2 of this application can be found in the detailed description of the corresponding parts of the frequency security domain analysis method for a high proportion of new energy power system provided in Embodiment 1 of this application, and will not be repeated here.

[0058] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that the elements inherent in a process, method, article, or apparatus that includes a list of elements are included. 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 said element. Additionally, portions of the technical solutions provided in the embodiments of this application that are consistent with the implementation principles of corresponding technical solutions in the prior art have not been described in detail to avoid excessive elaboration.

[0059] While specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art can make other modifications or variations based on the above description. It is neither necessary nor possible to exhaustively describe all embodiments here. Various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A high-proportion new energy power system frequency safety domain analysis method, characterized in that, Includes the following steps: Frequency response sub-models for synchronous generating units and new energy generating units are established separately. The new energy generating units include grid-connected new energy generating units and grid-linked new energy generating units. Furthermore, a time-domain model of the power deficit of new energy units under low-voltage ride-through operation is established; the transfer function corresponding to the frequency response sub-model of the synchronous unit is fused with the transfer function corresponding to the frequency response sub-model of the grid-connected new energy unit to obtain the fused transfer function; the transfer function corresponding to the frequency response sub-model of the grid-connected new energy unit is used as a feedback loop to construct a closed-loop transfer function; the power deficit time-domain model is superimposed with the preset long-term power deficit, and used together as the power deficit input, which is applied to the closed-loop transfer function to form a converged frequency response model; The transfer function corresponding to the frequency response sub-model of the synchronous unit is fused with the transfer function corresponding to the frequency response sub-model of the grid-connected new energy unit to obtain a fused transfer function, that is, the transfer functions of the synchronous unit and the grid-connected new energy unit are fused to obtain the fused transfer function and The fused transfer function is obtained by merging ; ； ； ； in, Indicates the transfer function after fusion; Represents the equivalent inertia; Indicates equivalent damping; This represents the proportion of grid-connected renewable energy units in the total renewable energy mix, i.e., the first independent variable; This indicates the proportion of synchronous generator unit capacity; This represents the equivalent inertia of a synchronous generator unit; Represents the virtual inertia of grid-type new energy sources; Indicates the damping coefficient of the synchronous generator unit; The equivalent virtual damping parameter represents the frequency support characteristics of grid-connected new energy generating units; Represents the equivalent inertia; Indicates equivalent damping; The transfer function corresponding to the frequency response sub-model of the grid-connected new energy power unit is used as a feedback loop to construct a closed-loop transfer function, specifically: ； ； in, Represents the transfer function of the drooping loop; Indicates the percentage of total capacity; This represents the proportion of grid-connected new energy generating units equipped with droop control, i.e., the second independent variable; This indicates the droop control coefficient for grid-type new energy sources; Indicates the low voltage ride-through ratio; The transfer function between frequency deviation and power deficit, i.e., the closed-loop transfer function, is expressed as: ； Short-term power deficit Compared with the preset long-term power deficit The components are superimposed and used together as power deficit input to form an aggregated frequency response model that takes into account the frequency regulation characteristics and operating status of new energy sources. ； in, Indicates the power deficit of the input system; The proportion of grid-connected renewable energy to the total renewable energy capacity is taken as the first independent variable, and the proportion of grid-connected renewable energy with droop control is taken as the second independent variable. The first and second independent variables are discretized to obtain the first and second independent variable sequences respectively. Each element in the first independent variable sequence is cross-combined with each element in the second independent variable sequence to form a two-dimensional parameter space. Based on the aggregated frequency response model, frequency dynamic simulation is performed on each set of parameter combinations in the two-dimensional parameter space to obtain the frequency response curve. A multidimensional frequency safety index threshold is set, and a multidimensional evaluation of frequency safety is performed based on the frequency response curve to identify the safety boundaries of parameter combinations under different safety dimensions. Within the design parameter space of the frequency modulation system and the equivalent parameter space of the system, the safety point set region is fitted and the intersection is taken to form a parameter region that meets the frequency safety requirements.

2. The frequency security domain analysis method for high-proportion new energy power systems according to claim 1, characterized in that, The process of constructing the frequency response sub-model of a synchronous generator unit includes: in, Indicates frequency deviation; Indicates the power deficit of the input system; The transfer function representing the frequency deviation and power deficit of a synchronous generator unit; This represents the droop coefficient of the synchronous generator unit; Indicates the natural angular frequency; Indicates the damping coefficient of the synchronous generator unit; This indicates the proportion of synchronous generator unit capacity; This represents the reheat time constant of the synchronous generator unit; Indicates the damping ratio; This represents the equivalent inertia of a synchronous generator unit; This indicates the percentage of power supplied by the high-pressure cylinder of the synchronous machine.

3. The frequency security domain analysis method for high-proportion new energy power systems according to claim 2, characterized in that, The process of constructing the frequency response sub-model for grid-connected new energy generating units includes: ； in, The transfer function representing the frequency deviation and power deficit of grid-connected new energy generating units; Represents the virtual inertia of grid-type new energy sources; This represents the virtual damping coefficient of grid-type new energy sources; This represents the analog gain coefficient of grid-connected new energy sources; The equivalent virtual damping parameter represents the frequency support characteristics of grid-connected new energy generating units; The process of constructing the frequency response sub-model of grid-connected new energy power units includes: ； in, This indicates that the grid-connected new energy frequency regulation system is contributing power. This represents the transfer function between the active power output of grid-connected renewable energy generating units participating in frequency regulation and the system frequency deviation; This indicates the droop control coefficient for grid-type new energy sources; This indicates the low-voltage ride-through ratio.

4. The frequency security domain analysis method for high-proportion new energy power systems according to claim 3, characterized in that, A time-domain model of the power deficit of new energy generating units under low-voltage ride-through operation is established, specifically as follows: ； ； in, express The short-term power deficit caused by low voltage ride-through; Indicates the low voltage crossing power valley value Indicates the time when the fault occurred; This indicates the moment when low-voltage ride-through power begins to recover; This indicates the moment when the low-voltage ride-through power is fully restored; This indicates the low-voltage ride-through power recovery rate.

5. The frequency security domain analysis method for high-proportion new energy power systems according to claim 4, characterized in that, Discretizing the first and second independent variables respectively yields the sequences of the first and second independent variables, i.e., discretizing the first and second independent variables respectively. and According to step size and Discretize to form the sequence of the first independent variable. Second independent variable sequence ; ； and Includes One element; Includes One element; in, Indicates rounding down; Indicates the first The value of the ratio of each network structure type; Indicates the first The droop control ratio can be set to a specific value. Indicates the network structure ratio index number; Indicates the droop control ratio index number; By combining each element of the first independent variable sequence with each element of the second independent variable sequence, a two-dimensional parameter space is formed as follows: ； in, The CCP contains Each of the following elements is distinct and represents a... Parameter combinations.

6. The frequency security domain analysis method for high-proportion new energy power systems according to claim 1, characterized in that, Based on the aggregated frequency response model, frequency response curves are obtained by performing dynamic frequency simulation on each set of parameter combinations in the two-dimensional parameter space. Select a set of parameters that have not yet been simulated from the two-dimensional parameter space in sequence; The selected parameter combination is used as the variable parameter of the aggregate frequency response model to configure the model; According to the preset fault scenario, an active power impact is applied to the configured aggregate frequency response model, and time-domain dynamic simulation calculation is performed. Record the curve of the system frequency deviation from the rated frequency over time during the simulation process, which will be used as the frequency response curve corresponding to this parameter combination. Repeat the above process until the simulation of all parameter combinations in the two-dimensional parameter space is completed.

7. The frequency security domain analysis method for high-proportion new energy power systems according to claim 1, characterized in that, A multi-dimensional frequency security index threshold is set, and a multi-dimensional frequency security evaluation is performed based on the frequency response curve to identify the security boundaries of parameter combinations under different security dimensions; specifically: Multidimensional frequency safety indicators include the maximum rate of frequency change, extreme values ​​of frequency deviation, and steady-state frequency deviation; The values ​​of the maximum rate of change of frequency, the extreme value of frequency deviation, and the steady-state frequency deviation are extracted from each frequency response curve obtained from the simulation. The extracted index values ​​are compared with the corresponding security thresholds to determine whether each parameter combination meets the security requirements under each security dimension. Based on the judgment results, the boundary positions between safe and unsafe points are identified in the two-dimensional parameter space for each safety dimension. The boundary points under each safety dimension are determined by interpolation method, and the safety boundary curves of each safety dimension are obtained by fitting. Based on the safety boundary curves of each safety dimension, the parameter regions that meet the requirements of each safety dimension are determined respectively. The intersection of the parameter regions that simultaneously meet the requirements of all safety dimensions is taken to form the new energy frequency regulation configuration safety domain.

8. The frequency security domain analysis method for high-proportion new energy power systems according to claim 1, characterized in that, Within the design parameter space and equivalent parameter space of the frequency modulation system, the safety point set region is fitted and its intersection is taken to form a parameter region that meets the frequency safety requirements, specifically: The boundary points in the safety domain of new energy frequency regulation configuration are mapped from the parameter space consisting of the first independent variable and the second independent variable to the equivalent parameter space consisting of the system equivalent inertia and the system equivalent damping. The safety boundary curves for each safety dimension are obtained by fitting them in the equivalent parameter space. Based on the safety boundary curves of each safety dimension, determine the equivalent parameter regions that meet the requirements of each safety dimension. The intersection of the equivalent parameter regions that simultaneously meet all safety dimension requirements is used to form the system equivalent parameter safety domain. Among them, the mapping method calculates the corresponding system equivalent inertia and system equivalent damping through affine transformation based on the capacity ratio of synchronous units, the virtual inertia parameters of grid-type new energy, the droop control parameters of grid-connected new energy, and the values ​​of the first and second independent variables.

9. A frequency security domain analysis system for a high-proportion renewable energy power system, used to execute the frequency security domain analysis method for a high-proportion renewable energy power system according to any one of claims 1 to 8, characterized in that, include: The model building module is used to establish frequency response sub-models for synchronous generator units and new energy generator units, respectively. The new energy generator units include grid-connected new energy generator units and grid-linked new energy generator units. Furthermore, a time-domain model of the power deficit of new energy units under low-voltage ride-through operation is established; the transfer function corresponding to the frequency response sub-model of the synchronous unit is fused with the transfer function corresponding to the frequency response sub-model of the grid-connected new energy unit to obtain the fused transfer function; the transfer function corresponding to the frequency response sub-model of the grid-connected new energy unit is used as a feedback loop to construct a closed-loop transfer function; the power deficit time-domain model is superimposed with the preset long-term power deficit, and used together as the power deficit input, which is applied to the closed-loop transfer function to form a converged frequency response model; The frequency simulation module is used to take the proportion of grid-type new energy to the total capacity of new energy as the first independent variable and the proportion of grid-type new energy with droop control as the second independent variable; the first and second independent variables are discretized to obtain the first and second independent variable sequences respectively; each element in the first independent variable sequence is cross-combined with each element in the second independent variable sequence to form a two-dimensional parameter space; Based on the aggregated frequency response model, frequency response curves are obtained by performing dynamic frequency simulation on each set of parameter combinations in the two-dimensional parameter space. The security domain output module is used to set multi-dimensional frequency security index thresholds, perform multi-dimensional frequency security evaluation based on the frequency response curve, identify the security boundaries of parameter combinations under different security dimensions, and fit and intersect the security point set region within the frequency modulation system design parameter space and system equivalent parameter space to form a parameter region that meets frequency security requirements.

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