Mode resonance instability probability evaluation method and system considering new energy output randomness

Abstract

The invention discloses a mode resonance instability probability evaluation method and system considering new energy output randomness, and the method comprises the steps: S1, building a linearization model of a new energy grid-connected system, determining a transfer function of two subsystems, and estimating the position of a closed-loop oscillation mode of the system according to an open-loop oscillation mode and residue information; s2, fitting parameters of a probability distribution function and a generator power curve according to measured data of new energy intensity and output power, and constructing a new energy output probability distribution model; and S3, calculating to obtain critical power by using the open-loop oscillation mode and the residue information, and substituting the critical power into the probability model of the instability of the new energy grid-connected system to calculate the instability probability. The adopted mode analysis method is suitable for a multiple-input-multiple-output system, the position of the oscillation mode can be accurately calculated, the calculated amount is relatively small, the probability of system mode resonance instability can be estimated by combining a new energy random output model, and a reference is provided for new energy grid-connected stability.

Description

Technical Field

[0001] The present invention belongs to the technical field of renewable energy power generation systems, and in particular relates to a method and system for evaluating the probability of mode resonance instability taking into account the randomness of renewable energy output. Background Art

[0002] The randomness of new energy output refers to the phenomenon that the output power of the new energy converter changes accordingly due to random changes in the intensity of new energy power generation resources such as wind speed (or light intensity). In the present invention, a probability density function is used in combination with the generator power curve to characterize it. Mode resonance refers to the phenomenon that the closed-loop oscillation mode may repel to the left and right when the open-loop oscillation modes of two subsystems are close to each other in the complex plane. This phenomenon will cause the subsystem mode damping corresponding to the right characteristic root to decrease. When the real part of the closed-loop characteristic root is greater than zero, the closed-loop system oscillation becomes unstable.

[0003] When a renewable energy generation system approaches the open-loop mode of the power system, grid connection can lead to strong dynamic interactions with the grid, reducing the system's stability margin. Because the intensity of this dynamic interaction is positively correlated with the output power of the renewable energy generation system, the renewable energy output power that causes critical oscillations in the system is known as the critical power. A random renewable energy output model is constructed based on the statistical patterns of renewable energy generation resource intensity and converter output power. This model is used to assess the probability that renewable energy generation power exceeds the critical power, effectively assessing the probability of resonant instability in the open-loop mode of the power system.

[0004] Existing research primarily analyzes the mechanisms and key influencing factors of renewable energy grid-connected oscillations through modeling. However, probabilistic assessment of renewable energy grid stability is limited. Common methods for analyzing renewable energy grid stability include time-domain simulation, frequency-domain analysis, and mode analysis. Time-domain simulation is limited by computing power and time constraints when applied to large-scale systems. Frequency-domain analysis primarily targets single-input, single-output systems and cannot accurately reflect the magnitude of mode damping. Summary of the Invention

[0005] In order to solve the technical problems existing in the background technology, the present invention aims to provide a method and system for evaluating the probability of mode resonance instability taking into account the randomness of new energy output. The mode analysis method adopted is suitable for multi-input and multi-output systems, and can accurately calculate the position of the oscillation mode with relatively small calculation amount. Combined with the new energy random output model, the probability of system mode resonance instability can be estimated, providing a reference for the stability of new energy grid connection.

[0006] In order to solve the technical problem, the technical solution of the present invention is:

[0007] A method for evaluating the probability of modal resonance instability considering the randomness of renewable energy output, the method comprising:

[0008] S1: Establish a linearized model of the renewable energy grid-connected system, determine the transfer functions of the two subsystems, and estimate the position of the closed-loop oscillation mode of the system based on the open-loop oscillation mode and residue information;

[0009] S2: Fit the probability distribution function and the parameters of the generator power curve based on the measured data of renewable energy intensity and output power to build a renewable energy output probability distribution model;

[0010] S3: The critical power is calculated using the open-loop oscillation mode and residue information, and is substituted into the probability model of instability of the renewable energy grid-connected system to calculate the instability probability.

[0011] Furthermore, the step S1 is to establish a linear model of the new energy grid-connected system, which specifically includes:

[0012] A state-space model for the closed-loop interconnection between the new energy subsystem and the power subsystem is established, where P1 is the active power injected from the new energy subsystem into the power subsystem, ΔV1 is the voltage at the output node of the new energy subsystem, and G(s) and H(s) are the transfer functions of the power subsystem and the new energy subsystem, respectively.

[0013] The transfer function can be written as:

[0014]

[0015] Where λ 2k (k=1,2,...,i,...,m) represents each open-loop oscillation mode of the power subsystem, λ 1k (k=1,2,...,j,...,n) represents the open-loop oscillation mode of the new energy subsystem; R 2k Represents the residual corresponding to each open-loop oscillation mode of the power subsystem, R' 1k It represents the residual corresponding to each open-loop oscillation mode when the new energy subsystem outputs rated power, and P represents the size of the new energy output;

[0016] When open-loop mode resonance occurs, a pair of open-loop oscillation modes of the two subsystems overlap (λ 2i ≈λ 1j ), whose corresponding closed-loop mode can be predicted by the open-loop mode and the residual;

[0017]

[0018] Furthermore, the step S2, determining the probability distribution model of new energy output, specifically includes:

[0019] The Weibull distribution is used to describe the probability characteristics of random changes in the intensity of new energy. Its probability density function is expressed as:

[0020]

[0021] Where v is the new energy intensity, k and c are the shape parameter and scale parameter respectively, which can characterize the size and distribution characteristics of the average intensity;

[0022] The formula for the change of converter output power with the intensity of new energy is as follows:

[0023]

[0024] Where η is the conversion rate of new energy to electric energy, ρ and A are the converter parameters, and P m is the maximum output power of the generator, v ci 、v r and v co are the generator cut-in, rated and cut-out strengths respectively;

[0025] Assuming that the converter output power is P0 when the new energy intensity is v0, the probability of the new energy output P>P0 can be obtained by combining equations (3) and (4):

[0026]

[0027] Furthermore, the step S3, evaluating the probability of instability of the new energy grid-connected system, specifically includes:

[0028] Assume that the open-loop oscillation mode of the power subsystem is on the right, that is, λ 2i >λ 1i , then the closed-loop oscillation mode of the power subsystem is expressed as:

[0029]

[0030] Where P (0~1p.u.) is the active power output of new energy, when Re (λ 2i )=0, the closed-loop system is critically unstable, that is:

[0031]

[0032] The critical power P0 of the new energy grid instability can be expressed as:

[0033]

[0034] Combining equations (5) and (9), the probability of open-loop mode resonant instability of the power system considering the randomness of renewable energy output is obtained as follows:

[0035]

[0036] A system for evaluating the probability of modal resonance instability taking into account the randomness of renewable energy output, the system being applied to any of the above-mentioned methods, the system comprising:

[0037] Linearization model building module: Builds a linear model of the new energy grid-connected system, determines the transfer functions of the two subsystems, and estimates the location of the system's closed-loop oscillation mode based on the open-loop oscillation mode and residue information;

[0038] Probability distribution model construction module: This module fits the probability distribution function and the parameters of the generator power curve based on the measured data of renewable energy intensity and output power to build a renewable energy output probability distribution model;

[0039] Instability probability assessment module: The critical power is calculated using the open-loop oscillation mode and residue information, and is substituted into the probability model of instability of the new energy grid-connected system to calculate the instability probability.

[0040] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements any one of the above-mentioned methods for evaluating the probability of mode resonance instability considering the randomness of new energy output.

[0041] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements any one of the above-mentioned methods for evaluating the probability of mode resonance instability considering the randomness of new energy output.

[0042] Compared with the prior art, the advantages of the present invention are:

[0043] Improving system stability: By establishing a linearized model of the renewable energy grid-connected system and accurately estimating the location of closed-loop oscillation modes, potential system instability risks can be effectively identified. This preventative analysis helps develop appropriate control strategies, enhancing system stability during design and operation, thereby ensuring reliable power supply.

[0044] Optimizing renewable energy utilization: By fitting measured data on renewable energy intensity and output power, we can better understand the stochastic characteristics of renewable energy. The resulting random output model for renewable energy will provide a basis for power dispatch and resource allocation, optimizing the utilization efficiency of renewable energy resources and reducing reliance on traditional energy sources.

[0045] Reducing instability risks: Using open-loop oscillation mode and residue information to calculate critical power, and combining the randomness of renewable energy output to assess instability probability, can provide a scientific basis for power system operation, significantly reduce the instability risk caused by renewable energy fluctuations, and thus improve the safety of the power system.

[0046] Support decision-making and policy-making: The data analysis and model building provided by this solution can provide decision-making support for power operators and policymakers, help formulate more reasonable power market policies, promote the integration and development of new energy, and promote the widespread application of renewable energy.

[0047] Promoting economic benefits: Improving the stability and reliability of renewable energy grid-connected systems can reduce economic losses caused by power system instability, thereby enhancing economic benefits. Furthermore, optimizing resource utilization and scheduling will help reduce operating costs, thereby achieving optimal economic benefits.

[0048] Enhance scientific research value: The implementation of this plan can not only achieve good results in practical applications, but also provide theoretical support and data accumulation for related research, provide a reference for subsequent research on the stability of new energy grid-connected systems, and promote in-depth exploration of this field in the academic community.

[0049] By achieving these effects, the solution will lay a solid foundation for the safe and stable operation of the new energy grid-connected system and promote the realization of sustainable development goals. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 , the closed-loop interconnection model of the present invention;

[0051] Figure 2 , a schematic diagram showing the effect of the new energy output on the closed-loop root locus of the present invention;

[0052] Figure 3 , flow chart of the technical solution of the present invention;

[0053] Figure 4 , schematic diagram of the simulation example of the present invention. DETAILED DESCRIPTION

[0054] The specific implementation of the present invention is described below in conjunction with embodiments:

[0055] It should be noted that the structures, proportions, sizes, etc. shown in this specification are only used to match the contents disclosed in the specification for people familiar with this technology to understand and read, and are not used to limit the conditions under which the present invention can be implemented. Any structural modification, change in proportional relationship or adjustment of size should still fall within the scope of the technical content disclosed in the present invention without affecting the efficacy and purpose that can be achieved by the present invention.

[0056] At the same time, the terms such as "upper", "lower", "left", "right", "middle" and "one" quoted in this specification are only for the convenience of description and are not used to limit the scope of implementation of the present invention. Changes or adjustments to their relative relationships should be regarded as the scope of implementation of the present invention without substantially changing the technical content.

[0057] Example 1:

[0058] The overall technical method flow chart of the method for evaluating the probability of mode resonance instability considering the randomness of new energy output is as follows: Figure 3 As shown, the following steps are included:

[0059] Step 1: Establish a linearized model of the renewable energy grid-connected system, write the transfer functions of the two subsystems, and estimate the position of the closed-loop oscillation mode of the system based on the open-loop oscillation mode and residue information.

[0060] Step 2: Fit the probability distribution function and the parameters of the generator power curve based on the measured data of new energy intensity and output power to build a new energy random output model.

[0061] Step 3: Calculate the critical power from the open-loop oscillation mode and residue information, and substitute it into the probability model of instability of the new energy grid-connected system to calculate the instability probability.

[0062] Specifically include:

[0063] 1. Establish a linear model of the new energy grid-connected system

[0064] Establish a state space model of closed-loop interconnection between the new energy subsystem and the power subsystem, such as Figure 1 As shown;

[0065] P1 is the active power injected by the new energy subsystem into the power subsystem, ΔV1 is the voltage at the output node of the new energy subsystem, and G(s) and H(s) are the transfer functions of the power subsystem and the new energy subsystem, respectively.

[0066] The transfer function can be written as:

[0067]

[0068] Where λ 2k (k=1,2,...,i,...,m) represents each open-loop oscillation mode of the power subsystem, λ 1k (k=1,2,...,j,...,n) represents the open-loop oscillation mode of the new energy subsystem; R 2k Represents the residual corresponding to each open-loop oscillation mode of the power subsystem, R' 1k It represents the residual corresponding to each open-loop oscillation mode when the new energy subsystem outputs rated power, and P represents the size of the new energy output;

[0069] When open-loop mode resonance occurs, a pair of open-loop oscillation modes of the two subsystems overlap (λ 2i ≈λ 1j ), whose corresponding closed-loop mode can be predicted by the open-loop mode and the residual;

[0070]

[0071] Where λ 2i and λ 1j They are open-loop mode λ2i and λ 1j The corresponding closed-loop oscillation mode.

[0072] 2. Determine the probability distribution model of new energy output

[0073] The Weibull distribution is used to describe the probability characteristics of random changes in new energy intensity. Its probability density function is expressed as:

[0074]

[0075] Where v is the new energy intensity, k and c are the shape parameter and scale parameter, respectively, which can characterize the size and distribution characteristics of the average intensity.

[0076] The formula for the change of converter output power with the intensity of new energy is as follows:

[0077]

[0078] Where η is the conversion rate of new energy to electric energy, ρ and A are the converter parameters, and P m is the maximum output power of the generator, v ci 、v r and v co are the generator cut-in, rated and cut-out intensities respectively.

[0079] Assuming that the converter output power is P0 when the new energy intensity is v0, the probability of the new energy output P>P0 can be obtained by combining equations (3) and (4):

[0080]

[0081] 3. Assessment of the probability of instability of new energy grid-connected systems

[0082] Increasing the output of new energy will lead to an increase in the interaction between the two open-loop subsystems. When the output of new energy is small, the distance at which the closed-loop characteristic root is repelled is small, its real part is far from the imaginary axis, and the system remains stable; when the output of new energy is equal to the critical power, the real part of the closed-loop characteristic root is exactly equal to zero, and the closed-loop system oscillates critically; when the output of new energy is greater than the critical power, the real part of the closed-loop characteristic root is greater than zero, and the closed-loop system becomes unstable. Figure 2 shown.

[0083] Assume that the open-loop oscillation mode of the power subsystem is on the right, that is, Re(λ 2i )>Re(λ 1j ), the closed-loop oscillation mode of the power subsystem is expressed as:

[0084]

[0085] Where P (0~1p.u.) is the active power output of new energy, when Re (λ2i )=0, the closed-loop system is critically unstable, that is:

[0086]

[0087] More generally,

[0088]

[0089] The critical power P0 of the new energy grid instability can be expressed as:

[0090]

[0091] Combining equations (5) and (9), we can derive the probability of open-loop mode resonant instability of the power system considering the randomness of renewable energy output as follows:

[0092]

[0093] Example 2:

[0094] Example 2 is applied to Example 1, as Figure 4 The figure shows the calculation model diagram of the new energy unit connected to the power system through the busbar;

[0095] G1(s) is the transfer function of the new energy subsystem 1, H(s) is the transfer function of the power system, and x L1 、x L They are the line impedances from the new energy subsystem 1 and the power system to the busbar respectively.

[0096] Step 1: First, write the transfer functions of the two new energy subsystems. The dominant open-loop mode of new energy subsystem 1 is λ 1j =-3.74+125.17i, the residue is R 1j =4-10i; an open-loop oscillation mode of the power subsystem is λ 2i =-3.71+123.59i, the corresponding residue is R 2i =P(6.65-16.92i). Therefore, the transfer function can be written as,

[0097]

[0098] The open-loop oscillation modes of the new energy subsystem and the power subsystem are very similar, resulting in open-loop mode resonance. The corresponding closed-loop characteristic roots are estimated to be distributed at:

[0099]

[0100] Step 2: Use Weibull distribution to fit the probability distribution function of new energy intensity and obtain parameters k = 3.45 and c = 6.873. Therefore, the probability density function of new energy intensity is written as:

[0101]

[0102] The parameters obtained by fitting the power curve are Generator cut-out strength v co =22, so the generator power curve is:

[0103] f p1 (v)=1.8648v 3 (14)

[0104] Combining the above two equations, the new energy random output model can be written as:

[0105]

[0106] Step 3: Let Re(λ 2i )=0, calculate critical power Substituting the above random output model, the probability of resonant instability in the open-loop mode of renewable energy grid connection is obtained as:

[0107]

[0108] Example 3:

[0109] This embodiment provides a terminal device, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of the mode resonance instability probability assessment method considering the randomness of new energy output, including the following steps:

[0110] S1: Establish a linearized model of the renewable energy grid-connected system, determine the transfer functions of the two subsystems, and estimate the position of the closed-loop oscillation mode of the system based on the open-loop oscillation mode and residue information;

[0111] S2: Fit the probability distribution function and the parameters of the generator power curve based on the measured data of renewable energy intensity and output power to build a renewable energy output probability distribution model;

[0112] S3: The critical power is calculated using the open-loop oscillation mode and residue information, and is substituted into the probability model of instability of the renewable energy grid-connected system to calculate the instability probability.

[0113] Example 4:

[0114] This embodiment provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. It is understandable that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and, of course, the extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space. These instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0115] The processor may load and execute one or more instructions stored in a computer-readable storage medium to implement the corresponding steps of the method for evaluating the probability of modal resonance instability considering the randomness of new energy output in the above embodiment; the processor may load and execute the following steps:

[0116] S1: Establish a linearized model of the renewable energy grid-connected system, determine the transfer functions of the two subsystems, and estimate the position of the closed-loop oscillation mode of the system based on the open-loop oscillation mode and residue information;

[0117] S2: Fit the probability distribution function and the parameters of the generator power curve based on the measured data of renewable energy intensity and output power to build a renewable energy output probability distribution model;

[0118] S3: The critical power is calculated using the open-loop oscillation mode and residue information, and is substituted into the probability model of instability of the renewable energy grid-connected system to calculate the instability probability.

[0119] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0120] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0121] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0122] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0123] The preferred embodiments of the present invention are described in detail above, but the present invention is not limited to the above embodiments. Various changes can be made within the knowledge of ordinary technicians in this field without departing from the scope of the present invention.

[0124] Many other changes and modifications can be made without departing from the spirit and scope of the present invention. It should be understood that the present invention is not limited to the specific embodiments, and the scope of the present invention is defined by the appended claims.

Claims

1. A method for evaluating the probability of modal resonance instability considering the randomness of renewable energy output, characterized in that: The method comprises: S1: Establish a linearized model of the renewable energy grid-connected system, determine the transfer functions of the two subsystems, and estimate the position of the closed-loop oscillation mode of the system based on the open-loop oscillation mode and residue information; S2: Fit the probability distribution function and the parameters of the generator power curve based on the measured data of renewable energy intensity and output power to build a renewable energy output probability distribution model; S3: The critical power is calculated using the open-loop oscillation mode and residue information, and is substituted into the probability model of instability of the renewable energy grid-connected system to calculate the instability probability.

2. The method for evaluating the probability of modal resonance instability considering the randomness of renewable energy output according to claim 1 is characterized in that: The step S1, establishing a linear model of the new energy grid-connected system, specifically includes: A state-space model for the closed-loop interconnection between the new energy subsystem and the power subsystem is established, where P1 is the active power injected from the new energy subsystem into the power subsystem, ΔV1 is the voltage at the output node of the new energy subsystem, and G(s) and H(s) are the transfer functions of the power subsystem and the new energy subsystem, respectively. The transfer function can be written as: Where λ 2k (k=1,2,...,i,...,m) represents each open-loop oscillation mode of the power subsystem, λ 1k (k=1,2,...,j,...,n) represents the open-loop oscillation mode of the new energy subsystem; R 2k Represents the residual corresponding to each open-loop oscillation mode of the power subsystem, R′ 1k It represents the residual corresponding to each open-loop oscillation mode when the new energy subsystem outputs rated power, and P represents the size of the new energy output; When open-loop mode resonance occurs, a pair of open-loop oscillation modes of the two subsystems overlap (λ 2i ≈λ 1j ), whose corresponding closed-loop mode can be predicted by the open-loop mode and the residual; Where λ 2i and λ 1j They are open-loop mode λ 2i and λ 1j The corresponding closed-loop oscillation mode.

3. The method for evaluating the probability of modal resonance instability considering the randomness of renewable energy output according to claim 1 is characterized in that: The step S2, determining the probability distribution model of new energy output, specifically includes: The Weibull distribution is used to describe the probability characteristics of random changes in the intensity of new energy. Its probability density function is expressed as: Where v is the new energy intensity, k and c are the shape parameter and scale parameter respectively, which can characterize the size and distribution characteristics of the average intensity; The formula for the change of converter output power with the intensity of new energy is as follows: Where η is the conversion rate of new energy to electric energy, ρ and A are the converter parameters, and P m is the maximum output power of the generator, v ci 、v r and v co are the generator cut-in, rated and cut-out strengths respectively; Assuming that the converter output power is P0 when the new energy intensity is v0, the probability of the new energy output P>P0 can be obtained by combining equations (3) and (4):

4. The method for evaluating the probability of modal resonance instability considering the randomness of renewable energy output according to claim 1 is characterized in that: The step S3, evaluating the probability of instability of the new energy grid-connected system, specifically includes: Assume that the open-loop oscillation mode of the power subsystem is on the right, that is, Re(λ 2i )>Re(λ 1j ), the closed-loop oscillation mode of the power subsystem is expressed as: Where P (0~1p.u.) is the active power output of new energy, when Re (λ 2i )=0, the closed-loop system is critically unstable, that is: The critical power P0 of the new energy grid instability can be expressed as: Combining equations (5) and (9), the probability of open-loop mode resonant instability of the power system considering the randomness of renewable energy output is obtained as follows:

5. A mode resonance instability probability assessment system considering the randomness of renewable energy output, characterized by: The system is applied to the method according to any one of claims 1 to 4, and the system includes: Linearization model building module: Builds a linear model of the new energy grid-connected system, determines the transfer functions of the two subsystems, and estimates the location of the system's closed-loop oscillation mode based on the open-loop oscillation mode and residue information; Probability distribution model construction module: This module fits the probability distribution function and the parameters of the generator power curve based on the measured data of renewable energy intensity and output power to build a renewable energy output probability distribution model; Instability probability assessment module: The critical power is calculated using the open-loop oscillation mode and residue information, and is substituted into the probability model of instability of the new energy grid-connected system to calculate the instability probability.

6. A computer device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, a method for evaluating the probability of mode resonance instability taking into account the randomness of new energy output according to any one of claims 1 to 4 is implemented.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed by a processor, implements a method for evaluating the probability of modal resonance instability considering the randomness of new energy output according to any one of claims 1 to 4.

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