A multi-scale dynamic evaluation method suitable for grid voltage stability of high proportion of new energy

Abstract

The application provides a kind of multi-scale dynamic evaluation method for grid voltage stability suitable for high proportion new energy, comprising: collecting output data of new energy station, load power data, grid topology structure data and each node voltage electrical quantity data;Based on the constructed electromagnetic transient model, the voltage stability margin index of each node under different time scales is calculated;Based on the voltage stability margin, the dynamic evaluation of grid voltage stability is carried out.The application can effectively capture the voltage stability risk caused by new energy fluctuation, overcome the defects of traditional static evaluation ignoring dynamic characteristics and large amount of time-domain simulation calculation, the evaluation result is accurate and reliable, the calculation efficiency meets the online application demand, and can be widely applied to voltage stability online monitoring and early warning control of high proportion new energy power system.

Description

Technical Field

[0001] This invention belongs to the field of power system stability analysis and control technology, specifically relating to a multi-scale dynamic evaluation method for grid voltage stability applicable to scenarios with a high proportion of new energy access. Background Technology

[0002] With the accelerated global energy transition, the installed capacity of new energy power generation, such as wind power and photovoltaic power, continues to rise in the power system. Following my country's "dual-carbon" target, the development of new energy has entered a fast track, and a high proportion of renewable energy grid connection has become a prominent feature of the new power system. However, the intermittency, volatility, and uncertainty of new energy output pose serious challenges to the safe and stable operation of the power system, especially the increasingly prominent issue of voltage stability.

[0003] The impact of large-scale integration of new energy sources on grid voltage stability is mainly reflected in the following aspects: First, new energy power generation units are mostly connected to the grid through power electronic converters, lacking the inertia and voltage support capabilities of traditional synchronous generators; second, random fluctuations in new energy output lead to frequent changes in power flow distribution, increasing the difficulty of voltage control; third, during periods of high new energy generation, power backfeed may occur, causing voltage over-limit problems at the grid end; finally, under extreme weather conditions, sudden changes in new energy output may induce voltage instability or even collapse.

[0004] Currently, voltage stability assessment methods are mainly divided into two categories: static assessment and dynamic assessment. Static assessment methods are based on power flow equations and calculate voltage stability margins through continuous power flow methods, sensitivity analysis, etc. They have the advantages of fast calculation speed and ease of online application, but they ignore the dynamic characteristics of the system and the random fluctuations of renewable energy sources, resulting in overly optimistic assessment results. Dynamic assessment methods are based on time-domain simulation and can accurately characterize the transient and dynamic processes of the system, but they involve large computational loads and long processing times, making it difficult to meet the needs of online assessment. Furthermore, they typically use deterministic fault scenario analysis, failing to fully consider the probabilistic characteristics of renewable energy output.

[0005] In existing technologies, some scholars have proposed voltage stability assessment methods based on probabilistic power flow, which consider the uncertainty of renewable energy output through Monte Carlo simulation. However, this method still falls within the static category and cannot reflect the dynamic response process of the system. Some studies have also attempted to apply machine learning methods to voltage stability assessment, achieving rapid stability determination by training deep neural networks. However, these methods rely on a large number of simulation samples and lack clear physical meaning, resulting in poor interpretability. Summary of the Invention

[0006] The purpose of this invention is to provide a multi-scale dynamic assessment method for grid voltage stability applicable to high-proportion renewable energy sources, overcoming the aforementioned shortcomings of existing technologies. This invention constructs a three-level linkage model of "equipment-station-grid," combines stochastic differential equations to describe the intermittency of renewable energy sources, employs time-domain-frequency domain hybrid simulation to quantify voltage stability margins at different time scales, and identifies weak links through data-driven sensitivity analysis. This achieves full-process risk perception from second-level transients to hour-level steady states, overcoming the limitations of traditional methods.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows: In a first aspect, the present invention provides a multi-scale dynamic evaluation method for grid voltage stability applicable to high-proportion renewable energy sources, comprising the following steps: Collect power output data, load power data, power grid topology data, and voltage and electrical quantity data of each node from new energy power plants; Based on the constructed electromagnetic transient model, the voltage stability margin index of each node at different time scales is calculated. Dynamically assess grid voltage stability based on voltage stability margin.

[0008] Preferably, based on the constructed electromagnetic transient model, the voltage stability margin index of each node is calculated by combining time-domain simulation and frequency-domain analysis.

[0009] Preferably, based on the constructed electromagnetic transient model, and combining time-domain simulation with frequency-domain analysis, the voltage stability margin index of each node at different time scales is calculated. Specifically, the method is as follows: The static voltage stability margin of each node is calculated using the continuous power flow method. Set up typical fault scenarios and perform time-domain simulation on the constructed electromagnetic transient model to obtain the transient voltage stability margin of each node after the fault. The dynamic voltage stability margin of each node is calculated using frequency domain impedance analysis. The obtained static voltage stability margin, transient voltage stability margin, and dynamic voltage stability margin are weighted and fused to obtain the voltage stability margin.

[0010] Preferably, the grid voltage stability is dynamically assessed based on the voltage stability margin. Specifically, the method is as follows: If the obtained voltage stability margin is less than or equal to the first threshold and greater than the second threshold, a level three warning will be issued to alert the user. If the obtained voltage stability margin is less than or equal to the second threshold and greater than the third threshold, a level two early warning will be issued, prompting the preparation of preventive and control measures. If the obtained voltage stability margin is less than or equal to the third threshold, a first-level warning will be issued, prompting immediate emergency control measures to be taken.

[0011] Preferably, the evaluation method further includes: A global sensitivity analysis method based on Sobol was used to identify key factors and weak points; Develop prevention and control strategies based on key factors and weak points.

[0012] Preferably, a global sensitivity analysis method based on Sobol is used to identify key factors and weak points. The specific method is as follows: Determine the set of input parameters that affect voltage stability; The Monte Carlo method is used to sample the set of input parameters to generate multiple running scenarios, each of which corresponds to a set of input parameter values. Calculate the voltage stability margin for each operating scenario; Calculate the first-order sensitivity exponent for each input parameter; Identify key influencing factors based on the first-order sensitivity index; Identifying weak nodes based on voltage stability margin.

[0013] Secondly, the present invention provides a multi-scale dynamic evaluation system for grid voltage stability suitable for high-proportion renewable energy sources, comprising: The data acquisition unit is used to collect power output data, load power data, power grid topology data, and voltage and electrical quantity data of each node from new energy power plants. The index calculation unit is used to calculate the voltage stability margin index of each node at different time scales based on the constructed electromagnetic transient model. The voltage stability assessment unit is used to dynamically assess the voltage stability of the power grid based on the voltage stability margin.

[0014] Thirdly, the present invention provides an electronic device including a processor and a memory, wherein the memory stores computer instructions, and when the computer instructions are executed by the processor, the electronic device performs the method described thereon.

[0015] Fourthly, the present invention provides a computer program product, the computer program product including computer-executable instructions, which, when executed, implement the method described.

[0016] Fifthly, the present invention provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the method described herein.

[0017] Compared with the prior art, the beneficial effects of the present invention are: This invention provides a multi-scale dynamic assessment method for grid voltage stability applicable to high-proportion renewable energy scenarios. By simultaneously collecting four types of data—renewable energy output, load power, grid topology, and node voltage electrical quantities—it achieves systematic coverage of all elements within the "source-grid-load" system. This avoids the assessment blind spots caused by the single data dimension in traditional methods, providing a comprehensive and reliable information foundation for voltage stability analysis in high-proportion renewable energy scenarios. Specifically designed for high-renewable energy scenarios, this method constructs an electromagnetic transient model and calculates voltage stability margins at multiple time scales. It effectively characterizes the dynamic characteristics of renewable energy converters, such as lack of inertia support and intermittent output fluctuations. This overcomes the shortcomings of traditional static assessments, which ignore dynamic processes, and the excessive computational burden and difficulty in online application of pure time-domain simulations. It significantly improves the ability to capture voltage risks caused by random fluctuations in renewable energy. Finally, this method quantifies the assessment results into clear voltage stability margin indicators with clear physical meaning and engineering operability. It can be directly linked to early warning levels, providing dispatching and operation personnel with an automated assessment tool from data acquisition to risk identification. This meets the timeliness requirements of online monitoring and prevention control, possessing good engineering deployment value and promising prospects for widespread application. Attached Figure Description

[0018] Figure 1 This is a structural block diagram of the system of the present invention; Figure 2 This is a flowchart of the method of the present invention; Figure 3 This is a schematic diagram illustrating the calculation of voltage stability margin at different time scales. Detailed Implementation

[0019] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0020] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0021] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0022] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0023] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0024] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0025] Example 1 This embodiment provides a multi-scale dynamic evaluation method for grid voltage stability applicable to high-proportion renewable energy sources, comprising the following steps: S1: Data Acquisition Real-time data collection includes output data, load power data, power grid topology data, and voltage and electrical quantity data of each node from new energy power plants.

[0026] In this embodiment, the power output data of the new energy power station is the wind speed-power conversion data of the wind farm or the irradiance-power conversion data of the photovoltaic power station, which is obtained through an ultra-short-term prediction model.

[0027] The power grid topology data is obtained from the power grid model of the dispatch center.

[0028] Node voltage electrical quantity data are collected in real time through SCADA system or PMU device.

[0029] The collected data serves as input to the electromagnetic transient model, used for model initialization and boundary condition setting.

[0030] S2: Model Building and Updating A three-level interconnected electromagnetic transient model, comprising new energy power generation units, transmission and distribution networks, and dynamic loads, is constructed. Stochastic differential equations are introduced into this electromagnetic transient model to describe the intermittent and fluctuating characteristics of new energy power output. Specifically: Equipment-level models: These include detailed electromagnetic transient models of wind turbines, photovoltaic arrays, energy storage devices, and their converters. Wind turbines utilize either doubly-fed induction generators or permanent magnet direct-drive synchronous generator models; photovoltaic arrays employ engineering-grade five-parameter models; and converters use either average value models or switching models. All of these models are conventional models known in the field and can be directly accessed from standard library models in PSCAD / EMTDC or MATLAB / Simulink, configured according to the actual equipment parameters.

[0031] Station-level model: Considering the topology of the power collection network, the configuration of reactive power compensation devices, and the coordinated control strategy within the station, multiple units are represented as an aggregated model or a detailed model is retained, with the modeling granularity flexibly selected according to the evaluation requirements. This model serves as an intermediate layer between the equipment-level model and the grid-level model, receiving electrical quantities (voltage, current) output from the equipment level, and outputting the grid connection point electrical parameters to the grid-level model after being aggregated through the power collection network.

[0032] The grid-level model covers the main transmission network, substations, and dynamic loads, retaining key transmission sections and tie lines. It employs electromechanical transient models to describe the dynamic characteristics of synchronous generators, excitation systems, and speed control systems. The connection between the grid-level model and the electromagnetic transient model is achieved through interface conversion: in an electromagnetic transient simulation platform (such as PSCAD / EMTDC), the new energy power plant area is represented by a detailed electromagnetic transient model, while the synchronous generator area is represented by an equivalent electromechanical transient model. The two exchange data through a power or current source equivalent interface, enabling hybrid simulation.

[0033] Embedding stochastic differential equations in the model: dP_renew = μ(P_renew, t)dt + σ(P_renew, t)dW_t Where P_renew represents the power output of the new energy source, μ is the drift function, σ is the diffusion function, and W_t is the standard Wiener process. This equation can describe the stochastic fluctuation characteristics of the new energy source's power output, making the model closer to actual operating conditions. μ and σ can be identified as parameters based on the statistical characteristics of historical data, for example, using the maximum likelihood estimation method.

[0034] S3: Multi-scale voltage stability margin calculation Based on the aforementioned electromagnetic transient model, a method combining time-domain simulation and frequency-domain analysis is used to calculate the voltage stability margin indices of each node at different time scales. These time scales include transient second-level, dynamic minute-level, and steady-state hour-level. Specifically, they include: S31: Static Voltage Stability Margin Calculation Based on the current operating point, the static voltage stability margin λ_static of each node is calculated using the continuous power flow method. The continuous power flow method introduces a load growth factor λ and a generation dispatch factor, gradually increasing the system load until the power flow fails to converge, tracking the PV curve to the nose point to obtain the static voltage stability margin. λ_static is defined as the distance from the current operating point to the nose point, and is usually expressed as an active power margin.

[0035] S32: Transient Voltage Stability Margin Calculation The electromagnetic transient model is simulated in the time domain, with typical fault scenarios (such as three-phase short circuit, single-phase grounding, load surge, etc.) set. The fault duration is usually 0.1-0.2 seconds, and the simulation duration is 5-10 seconds. The transient voltage response trajectory of each node after the fault is obtained, and the voltage drop depth V_nadir (the lowest voltage point) and voltage recovery speed (characterized by recovery time T_recovery, i.e., the time required for the voltage to recover from the lowest drop point to 0.9 pu) are extracted from it.

[0036] The formula for calculating the transient voltage stability margin λ_transient is: λ_transient = 1 - (V_nadir / V_0) - k·(T_recovery / T_max) Where V_0 is the initial voltage, T_max is the maximum allowable recovery time (e.g., 2 seconds), and k is a weighting coefficient (ranging from 0.1 to 0.3). This formula considers both drop depth and recovery speed, comprehensively reflecting the transient voltage stability level.

[0037] S33: Calculation of Dynamic Voltage Stability Margin The dynamic voltage stability margin λ_dynamic of each node is calculated using frequency domain impedance analysis. The specific steps are as follows: a small-amplitude, wide-bandwidth harmonic current is injected at the grid connection point, and the impedance-frequency characteristic curve of the system is measured; the frequency corresponding to the minimum amplitude of the impedance curve is used to determine whether there is a resonance risk (resonance risk indicates that the system may experience subsynchronous oscillations, and this information is used to generate a specific early warning in subsequent risk warning steps); the dynamic voltage stability margin is evaluated based on the distance between the Nyquist curve and the complex plane point (-1, j0). The Nyquist curve is obtained by frequency scanning of the system's open-loop transfer function. (-1, j0) is the critical point in the Nyquist stability criterion. The minimum damping ratio or gain margin of the system can be quantified as the distance from this point to the critical point; a larger distance indicates a higher dynamic stability margin. λ_dynamic is defined as the normalized value of this distance, ranging from [0,1].

[0038] S34: Multi-scale margin fusion The static voltage stability margin λ_static, the transient voltage stability margin λ_transient, and the dynamic voltage stability margin λ_dynamic are weighted and fused to obtain the comprehensive voltage stability margin λ_comprehensive.

[0039] The fusion formula is: λ_comprehensive = α·λ_static + β·λ_transient + γ·λ_dynamic Here, α, β, and γ are weighting coefficients, which are dynamically adjusted according to the power grid operation mode and assessment requirements. For example, under normal operation, the weight of static margin can be increased (α = 0.5, β = 0.3, γ = 0.2); during the post-fault recovery phase, the weight of transient margin should be increased (α = 0.2, β = 0.6, γ = 0.2). The weighting coefficients can be determined based on historical experience or expert systems, or they can be dynamically optimized through optimization algorithms.

[0040] S4: Key Factor Identification This invention utilizes a data-driven sensitivity analysis method to identify key factors and weak points affecting voltage stability, and outputs a set of weak nodes. The invention employs a global sensitivity analysis method based on Sobol, specifically including: S41: Determine the set of input parameters affecting voltage stability, including renewable energy output, load level, network topology parameters, reactive power compensation capacity, etc. The selection rules for the set of input parameters are as follows: based on the voltage stability mechanism, select physical quantities that have a significant impact on node voltage; combine with grid operation experience to eliminate redundant parameters; for strongly correlated parameter groups, use principal component analysis to reduce dimensionality.

[0041] S42: The Monte Carlo method is used to sample the input parameters, generating multiple running scenarios. Each running scenario corresponds to a set of input parameter values. The number of samplings N is usually set to 1000-10000 times to ensure statistical accuracy.

[0042] S43: For each running scenario, call the method in step S3 to calculate the voltage stability margin and establish an input-output mapping dataset.

[0043] S44: Calculate the first-order sensitivity index S_i and the total-order sensitivity index ST_i for each input parameter. Based on the sensitivity indices, identify key influencing factors (typically parameters with a cumulative contribution exceeding 80%). Simultaneously, mark nodes with low voltage stability margins (below a preset threshold) as weak nodes. The identified key influencing factors and weak nodes are output to the visualization module for highlighting and serve as a basis for operators to formulate preventative control strategies. This information can also be fed back to the model building module to guide the detailed modeling of key monitoring areas in subsequent time-domain simulations.

[0044] S5: Risk Warning and Visualization Based on the calculated voltage stability margin, multi-level risk warning signals are generated, and the voltage stability margin distribution map and risk level are displayed through a visual interface. The warning levels are divided into: Level 3 warning (yellow): The voltage stability margin is less than or equal to the first threshold but greater than the second threshold (the first threshold is 0.3), prompting operators to pay attention to the voltage change trend in this area.

[0045] Level 2 warning (orange): The voltage stability margin is less than or equal to the second threshold but greater than the third threshold (e.g., the second threshold is 0.2), indicating that preventive and control measures should be prepared, such as adjusting reactive power compensation and optimizing power generation plans.

[0046] Level 1 Warning (Red): Voltage stability margin is less than or equal to the third threshold (e.g., the third threshold is 0.1), prompting immediate emergency control measures, such as load shedding and adjusting transformer taps.

[0047] The visualization interface uses a 3D geographic wiring diagram to display the voltage stability margin distribution of each node in a heatmap format. The node color gradually changes from green (high margin) to red (low margin) according to the margin value. The interface supports timeline scrolling, allowing playback of the voltage stability margin changes at historical moments; it supports node clicking to display detailed margin components and key influencing factors; and it supports layer switching, allowing separate display of static, transient, and dynamic margin distributions.

[0048] Example 2 Reference Figure 1 The multi-scale dynamic evaluation method for grid voltage stability applicable to high-proportion renewable energy sources described in this embodiment includes the following steps: Step S201: Data Acquisition. Obtain voltage amplitude, phase angle, active power, and reactive power of each node from the SCADA system; obtain high-sampling-rate dynamic data from the PMU device; obtain wind speed and irradiance prediction data from the meteorological forecasting system; and obtain network topology parameters from the power grid model database.

[0049] Step S202: Model Construction and Update. Update the electromagnetic transient model parameters based on real-time data, including generator output, load level, and network topology. For the uncertainty of new energy output, a stochastic differential equation is used: dP_renew = 0.05·P_renew·dt + 0.1·P_renew·dW_t, where the drift coefficient of 0.05 represents the average trend, and the diffusion coefficient of 0.1 represents the fluctuation intensity.

[0050] Step S203: Static Voltage Stability Assessment. The static voltage stability margin of each node is calculated using the continuous power flow method. Taking a specific load node as an example, the load growth direction is set to synchronously increase at each node according to the current proportion, and generation dispatch is adjusted according to the economic dispatch principle. The PV curve is traced to the nose point to obtain the static voltage stability margin of that node, λ_static = 0.35.

[0051] Step S204: Transient Voltage Stability Assessment. A typical fault scenario is set: a three-phase short-circuit fault is introduced at the midpoint of a critical transmission line, and the fault is cleared after 0.1 seconds. The voltage response curves of each node after the fault are obtained through time-domain simulation. The transient voltage stability margin is comprehensively evaluated using two indicators: voltage drop depth and recovery time. The calculated transient voltage stability margin of this node is λ_transient = 0.28.

[0052] Step S205: Dynamic Voltage Stability Assessment. A small-amplitude, wide-bandwidth harmonic current is injected at the grid connection point, and the impedance-frequency characteristic curve of the system is measured. Based on the frequency corresponding to the minimum amplitude of the impedance curve, the presence of resonance risk is determined; based on the distance between the Nyquist curve and the point (-1, j0), the dynamic voltage stability margin is assessed. The calculated dynamic voltage stability margin of this node is λ_dynamic = 0.32.

[0053] Step S206: Multi-scale margin fusion. Determine the weighting coefficients based on the current operating mode: Under normal conditions, static assessment has a higher weight, with α=0.5, β=0.3, and γ=0.2; during the post-fault recovery phase, transient assessment has an increased weight, with α=0.2, β=0.6, and γ=0.2. The fusion calculation yields the comprehensive voltage stability margin λ_comprehensive = 0.5×0.35 + 0.3×0.28 + 0.2×0.32 = 0.323.

[0054] Step S207: Key Factor Identification. A global sensitivity analysis method based on Sobol was used, selecting three parameters as inputs: renewable energy output, load level, and reactive power compensation capacity. Each parameter was sampled within its typical range. The first-order sensitivity exponents for each parameter were calculated: renewable energy output S1 = 0.45, load level S2 = 0.38, and reactive power compensation capacity S3 = 0.12. The overall sensitivity exponents were: renewable energy output ST1 = 0.52, load level ST2 = 0.43, and reactive power compensation capacity ST3 = 0.15. The results show that renewable energy output is the most critical factor affecting the voltage stability of this node.

[0055] Step S208: Risk Warning. The comprehensive voltage stability margin λ_comprehensive = 0.323 is compared with the threshold: 0.323 > 0.3, so no warning is triggered. However, considering that the key factor identification results show a significant impact from renewable energy output, the system prompts operators to pay attention to the renewable energy output trend over the next half hour.

[0056] Step S209: Visualization. On the 3D geographic wiring diagram, the node is displayed in light yellow (near the level 3 warning threshold), and detailed margin values ​​and key influencing factors are displayed when the mouse hovers over it.

[0057] Example 3: Hardware-in-the-loop simulation verification This embodiment verifies the method of the present invention on a hardware-in-the-loop simulation platform. The simulation platform includes: an RTDS real-time simulator to simulate a regional power grid model with a high proportion of new energy sources; actual physical controllers, including AVC controllers for new energy power plants and PCS controllers for energy storage; and the evaluation system described in this invention is deployed in a host computer.

[0058] Test scenario: The penetration rate of new energy in the regional power grid reaches 45%. Due to cloud cover, the output of a photovoltaic power station drops sharply from 80% to 20% within 10 minutes. Voltage stability is evaluated using both traditional static evaluation methods and the method of this invention.

[0059] Traditional static assessment methods, based on steady-state power flow calculations, assess that the voltage stability margin at each node is greater than 0.4 before power output changes, thus failing to issue early warnings. However, during actual dynamic processes, voltage fluctuations are significant, with the lowest voltage dropping to 0.85 pu, approaching the low-voltage load shedding threshold.

[0060] This invention's method tracks power output changes in real time and dynamically updates the evaluation model. During a sudden drop in power output, the transient evaluation unit detected increased voltage fluctuations, and the dynamic evaluation unit detected changes in system impedance characteristics. The combined voltage stability margin decreased from 0.42 to 0.23, triggering a level-three warning. Based on the warning information, operators adjusted the reactive power compensation device in advance, preventing potential subsequent voltage instability.

[0061] Verification results show that the method of the present invention can accurately capture the voltage stability risk caused by new energy fluctuations, the assessment results are consistent with the actual dynamic process, and the timeliness of the early warning meets the operational requirements.

[0062] Example 4 This embodiment provides a multi-scale dynamic evaluation system for grid voltage stability suitable for high-proportion renewable energy sources, comprising: The data acquisition unit is used to collect power output data, load power data, power grid topology data, and voltage and electrical quantity data of each node from new energy power plants. The index calculation unit is used to calculate the voltage stability margin index of each node at different time scales based on the constructed electromagnetic transient model. The voltage stability assessment unit is used to dynamically assess the voltage stability of the power grid based on the voltage stability margin.

[0063] Example 5: Comparative Test with Existing Technologies To verify the advancement of this invention, a comparative test was conducted using the IEEE 39-node system, which incorporates a high proportion of renewable energy sources (50% penetration rate). The comparison objects include: traditional continuous power flow method (static), time-domain simulation method (transient), and the method of this invention.

[0064] Two hundred random operating scenarios were set up, including random fluctuations in renewable energy output, random changes in load, and various fault types. Evaluation metrics included: evaluation accuracy (compared to detailed time-domain simulation results), computation time, and early warning lead time.

[0065] The test results are shown in the table below:

[0066] The results show that the method of the present invention, while ensuring a high accuracy (91.8%), has a computation time (3.2 seconds) that is much shorter than that of the pure time-domain simulation method, which can meet the timeliness requirements of online evaluation; at the same time, it can provide early warning of voltage stability risks caused by new energy fluctuations, thus saving valuable response time for operators.

[0067] Example 6 This embodiment also provides a computing device. The computing device includes a bus, a processor, a memory, and a communication interface. The processor, memory, and communication interface communicate with each other via the bus. The computing device can be a server or a terminal device. It should be understood that this application does not limit the number of processors and memory in the computing device.

[0068] A bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, a bus can include a path for transmitting information between various components of a computing device (e.g., memory, processor, communication interfaces).

[0069] The processor may include any one or more of the following: central processing unit (CPU), graphics processing unit (GPU), tensor processing unit (TPU), application specific integrated circuit (ASIC), field-programmable gate array (FPGA), microprocessor (MP), or digital signal processor (DSP).

[0070] Memory can include volatile memory, such as random access memory (RAM). Processors can also include non-volatile memory. volatile memory, such as read-only memory (ROM). ROM (memory only), flash memory, hard disk drive (HDD), or solid state drive (SSD).

[0071] The memory stores executable program code, which the processor executes to implement the functions of the aforementioned units, thereby achieving, for example, the method described in Embodiment 1. That is, the memory may store instructions for the methods and functions relating to the computing device in any of the above embodiments.

[0072] The communication interface uses transceiver modules such as, but not limited to, network interface cards and transceivers to enable communication between computing devices and other devices or communication networks.

[0073] Example 7 This embodiment also provides a computer-readable storage medium storing computer instructions that, when executed by a processor, cause the processor to perform the methods and functions of the computing device involved in any of the above embodiments.

[0074] Generally, the various embodiments of this disclosure can be implemented in hardware or dedicated circuitry, software, logic, or any combination thereof. Some aspects can be implemented in hardware, while others can be implemented in firmware or software, which can be executed by a controller, microprocessor, or other computing device. Although various aspects of the embodiments of this disclosure are shown and described as block diagrams, flowcharts, or represented using some other illustration, it should be understood that the blocks, apparatuses, systems, techniques, or methods described herein can be implemented as, as non-limiting examples, in hardware, software, firmware, dedicated circuitry or logic, general-purpose hardware or controllers or other computing devices, or some combination thereof.

[0075] Example 8 This embodiment provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computer-executable instructions, such as instructions included in program modules, which execute in a device on a target real or virtual processor to perform the processes / methods as described above with reference to the accompanying drawings. Typically, program modules include routines, programs, libraries, objects, classes, components, data structures, etc., that perform specific tasks or implement specific abstract data types. In various embodiments, the functionality of program modules can be combined or divided among program modules as needed. The machine-executable instructions for the program modules can execute within a local or distributed device. In a distributed device, the program modules can reside in both local and remote storage media.

[0076] Computer program code used to implement the methods of this disclosure may be written in one or more programming languages. This computer program code may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus, such that when executed by the computer or other programmable data processing apparatus, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be performed. The program code may be executed entirely on a computer, partially on a computer, as a stand-alone software package, partially on a computer and partially on a remote computer, or entirely on a remote computer or server.

[0077] In the context of this disclosure, computer program code or related data may be carried on any suitable carrier to enable a device, apparatus, or processor to perform the various processes and operations described above. Examples of carriers include signals, computer-readable media, and so on. Examples of signals may include electrical, optical, radio, sound, or other forms of propagation signals, such as carrier waves, infrared signals, etc.

[0078] Computer-readable media can be any tangible medium that contains or stores programs for or relating to an instruction execution system, apparatus, or device, or a data storage device such as a data center containing one or more available media. Computer-readable media can be computer-readable signal media or computer-readable storage media. Computer-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. More detailed examples of computer-readable storage media include electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0079] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A multi-scale dynamic evaluation method for grid voltage stability applicable to grids with a high proportion of new energy sources, characterized in that, Includes the following steps: Collect power output data, load power data, power grid topology data, and voltage and electrical quantity data of each node from new energy power plants; Based on the constructed electromagnetic transient model, the voltage stability margin index of each node at different time scales is calculated. Dynamically assess grid voltage stability based on voltage stability margin.

2. The multi-scale dynamic evaluation method for grid voltage stability applicable to high-proportion renewable energy sources according to claim 1, characterized in that, Based on the constructed electromagnetic transient model, and combining time-domain simulation with frequency-domain analysis, the voltage stability margin index of each node at different time scales is calculated.

3. The multi-scale dynamic evaluation method for grid voltage stability applicable to high-proportion renewable energy sources according to claim 2, characterized in that, Based on the constructed electromagnetic transient model, and combining time-domain simulation with frequency-domain analysis, the voltage stability margin index of each node at different time scales is calculated. Specifically: The static voltage stability margin of each node is calculated using the continuous power flow method. Set up typical fault scenarios and perform time-domain simulation on the constructed electromagnetic transient model to obtain the transient voltage stability margin of each node after the fault. The dynamic voltage stability margin of each node is calculated using frequency domain impedance analysis. The obtained static voltage stability margin, transient voltage stability margin, and dynamic voltage stability margin are weighted and fused to obtain the voltage stability margin.

4. The multi-scale dynamic evaluation method for grid voltage stability applicable to high-proportion renewable energy sources according to claim 1, characterized in that, Dynamically assessing grid voltage stability based on voltage stability margin, specifically, is achieved through the following method: If the obtained voltage stability margin is less than or equal to the first threshold and greater than the second threshold, a level three warning will be issued to alert the user. If the obtained voltage stability margin is less than or equal to the second threshold and greater than the third threshold, a level two early warning will be issued, prompting the preparation of preventive and control measures. If the obtained voltage stability margin is less than or equal to the third threshold, a first-level warning will be issued, prompting immediate emergency control measures to be taken.

5. The multi-scale dynamic evaluation method for grid voltage stability applicable to high-proportion renewable energy sources according to claim 1, characterized in that, The evaluation method also includes: A global sensitivity analysis method based on Sobol was used to identify key factors and weak points; Develop prevention and control strategies based on key factors and weak points.

6. The multi-scale dynamic evaluation method for grid voltage stability applicable to high-proportion renewable energy sources according to claim 5, characterized in that, A global sensitivity analysis method based on Sobol was used to identify key factors and weak points. The specific method is as follows: Determine the set of input parameters that affect voltage stability; The Monte Carlo method is used to sample the set of input parameters to generate multiple running scenarios, each of which corresponds to a set of input parameter values. Calculate the voltage stability margin for each operating scenario; Calculate the first-order sensitivity exponent for each input parameter; Identify key influencing factors based on the first-order sensitivity index; Identifying weak nodes based on voltage stability margin.

7. A multi-scale dynamic evaluation system for grid voltage stability suitable for high-proportion renewable energy sources, characterized in that, include: The data acquisition unit is used to collect power output data, load power data, power grid topology data, and voltage and electrical quantity data of each node from new energy power plants. The index calculation unit is used to calculate the voltage stability margin index of each node at different time scales based on the constructed electromagnetic transient model. The voltage stability assessment unit is used to dynamically assess the voltage stability of the power grid based on the voltage stability margin.

8. An electronic device, characterized in that, The device includes a processor and a memory, the memory storing computer instructions that, when executed by the processor, cause the electronic device to perform the method of any one of claims 1 to 6.

9. A computer program product, characterized in that, The computer program product includes computer-executable instructions that, when executed, implement the method of any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions that, when executed by a processor, implement the method of any one of claims 1 to 6.

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