New energy grid-connected system small interference stability simulation method

Abstract

The invention is suitable for the technical field of system stability simulation, and provides a small-interference stability simulation method for a new energy grid-connected system, and the method comprises the steps: obtaining the dynamic operation parameters of a synchronous unit and a new energy unit of each node of the system, and generating a node dynamic parameter set; the inherent equivalent inertia of the synchronous unit and the virtual equivalent inertia of the new energy unit are calculated, and the total equivalent inertia of the system is generated; constructing a virtual equivalent inertia distribution optimization model by taking the new energy permeability and the total equivalent inertia of the system as input; configuring the optimized virtual equivalent inertia weight into a new energy grid-connected system model, injecting a preset disturbance signal, and executing dynamic simulation to generate dynamic response data; calculating a characteristic value and a damping ratio of a system dominant oscillation mode, and quantitatively evaluating a small interference stability index by combining a state matrix spectrum radius; and outputting an optimized virtual equivalent inertia weight configuration scheme and a stability evaluation report, thereby improving the accuracy and reliability of small-interference stability evaluation of the new energy grid-connected system.

Description

Technical Field

[0001] The present invention relates to the technical field of system stability simulation, and particularly relates to a small-signal stability simulation method for a new energy grid-connected system. Background Art

[0002] Small-signal stability is a key indicator to measure the ability of a power system to maintain stable operation under small disturbances. With the continuous increase in the proportion of new energy in the power system, the inertia composition and dynamic characteristics of the power system have undergone profound changes, and small-signal stability has become a key issue to ensure the safe operation of the power grid. New energy power generation equipment lacks the mechanical inertia of traditional synchronous generators and is difficult to provide effective inertia support, resulting in an increase in the rate of change of frequency and a weakening of the oscillation suppression ability under power disturbances in the system, and the risk of small-signal stability increases significantly.

[0003] In the prior art, the traditional method is to optimize the inertia distribution of virtual synchronous generators by establishing a model to improve stability. However, the optimization model does not consider multi-source dynamic factors such as load uncertainty, output uncertainty, and fault probability for the correction of inertia, resulting in insufficient flexibility in inertia calculation; and only relies on a single index of the minimum damping ratio to judge the small-signal stability of a weak-inertia new energy power system, without considering comprehensive criteria such as synchronous torque coefficient. These traditional methods are difficult to adapt to the complex and changeable dynamic characteristics in the scenario of high-proportion new energy grid connection, resulting in an incomplete evaluation of small-signal stability and simulation parameters being difficult to meet different accuracy requirements. Summary of the Invention

[0004] The present invention provides a small-signal stability simulation method for a new energy grid-connected system, which is used to solve the problems of incomplete evaluation of small-signal stability and simulation parameters being difficult to meet different accuracy requirements.

[0005] The present invention provides a small-signal stability simulation method for a new energy grid-connected system, including: Obtain the dynamic operation parameters of synchronous units at each node of the system and new energy units at each node of the system, perform node association processing on the dynamic operation parameters, and generate a node dynamic parameter set including the proportion of the capacity of each unit in the total capacity of the corresponding node; Calculate the inherent equivalent inertia of synchronous units and the virtual equivalent inertia of new energy units respectively through the node dynamic parameter set; wherein, the virtual equivalent inertia is corrected based on the frequency response characteristics of the power grid, and the corrected virtual equivalent inertia and the inherent equivalent inertia are weighted and superimposed according to the proportion of the capacity of each node in the total installed capacity of the system to generate the total equivalent inertia of the system; Construct an optimization model for virtual equivalent inertia distribution with the new energy penetration rate and the total equivalent inertia of the system as inputs, and iteratively adjust the virtual equivalent inertia weights and damping coefficients of each node through preset optimization constraints; Configure the optimized virtual equivalent inertia weight into the new - energy grid - connected system model, inject a preset disturbance signal into the new - energy grid - connected system model with the configured optimized virtual equivalent inertia weight, and perform dynamic simulation to generate dynamic response data; According to the dynamic response data, calculate the eigenvalues and damping ratios of the system's dominant oscillation mode through eigenvalue analysis, and quantitatively evaluate the small - signal stability index by combining the spectral radius of the state matrix; Output the optimized virtual equivalent inertia weight configuration scheme and the stability evaluation report. The optimized virtual equivalent inertia weight configuration scheme includes the recommended values of the virtual equivalent inertia of each node, the optimized damping coefficient, and the corresponding new - energy penetration scenarios.

[0006] Furthermore, the calculation of the inherent equivalent inertia of the synchronous generator set and the virtual equivalent inertia of the new - energy generator set through the node dynamic parameter set respectively includes: Inherent equivalent inertia Calculation formula: Where: is the number of synchronous generator sets, , and are the rated capacity, inertia time constant, and load - fluctuation attenuation coefficient of the th synchronous generator set respectively, is the failure - probability coefficient of the th synchronous generator set.

[0007] Furthermore, the calculation of the inherent equivalent inertia of the synchronous generator set and the virtual equivalent inertia of the new - energy generator set through the node dynamic parameter set respectively also includes: Virtual equivalent inertia Calculation formula: Where: is the number of new - energy generator sets, is the virtual inertia control gain of the th new - energy generator set, which is set by the controller parameters, and are the rated capacity and output uncertainty coefficient of the th new - energy generator set respectively, is the failure - probability coefficient of the th new - energy generator set.

[0008] Furthermore, the correction of the virtual equivalent inertia based on the grid - frequency response characteristics, and the weighted superposition of the corrected virtual equivalent inertia and the inherent equivalent inertia according to the proportion of the capacity of each node in the total installed capacity of the system, includes: Corrected virtual equivalent inertia : Wherein: is the real-time frequency measurement value, is the grid rated frequency; Total system equivalent inertia : Wherein: and are the number of nodes of the synchronous unit and the new energy unit respectively, and are respectively the inherent equivalent inertia corresponding to the th synchronous unit and the virtual equivalent inertia corresponding to the th new energy unit in each node, and are respectively the th synchronous unit and the th new energy unit rated capacity, is the total installed capacity of the system.

[0009] Furthermore, taking the new energy penetration rate and the total system equivalent inertia as inputs, a virtual equivalent inertia allocation optimization model is constructed, and the virtual equivalent inertia weights and damping coefficients of each node are iteratively adjusted through preset optimization constraints, including: The objective function of the virtual equivalent inertia allocation optimization model is to minimize the two-norm of the system state matrix and the weighted frequency response robustness index of the new energy penetration rate , and the expression is as follows: Wherein: is the system state matrix constructed by the virtual equivalent inertia weight and the damping coefficient , is the new energy penetration rate, is defined as the ratio of the total capacity of the new energy unit to the total capacity of the system, and are weight coefficients, is the frequency response correction factor, is the frequency deviation to the disturbance frequency sensitivity matrix, is the matrix infinity norm.

[0010] Furthermore, taking the new energy penetration rate and the total equivalent inertia of the system as inputs, a virtual equivalent inertia distribution optimization model is constructed, and the virtual equivalent inertia weights and damping coefficients of each node are iteratively adjusted through preset optimization constraints, which further includes: The constraint conditions of the virtual equivalent inertia distribution optimization model include the virtual equivalent inertia weight constraint of the node, the damping coefficient range constraint, and the stability criterion threshold constraint. Among them, the stability criterion threshold includes the real part of the eigenvalue, the damping ratio, and the synchronizing torque coefficient.

[0011] Furthermore, configuring the optimized virtual equivalent inertia weights into the new energy grid-connected system model, injecting a preset disturbance signal into the new energy grid-connected system model with the optimized virtual equivalent inertia weights configured, and performing dynamic simulation to generate dynamic response data, which includes: Configuring the optimized virtual equivalent inertia weights into the corresponding node controller in the new energy grid-connected system model, and updating the inertia distribution parameters of the system; Injecting a preset disturbance signal into the new energy grid-connected system model with the optimized virtual equivalent inertia weights configured, performing time-domain dynamic simulation, and real-time recording the frequency deviation curve of each node and the change curve of the power angle difference between synchronous units to generate frequency response data and power angle oscillation characteristic data; From the frequency response data, calculate the difference between the maximum frequency and the steady-state frequency, and determine the frequency overshoot by dividing the difference by the steady-state frequency; and calculate the time from the start of the disturbance to the frequency recovering within the steady-state error band as the adjustment time; extract the maximum instantaneous absolute value of the power angle difference of the synchronous unit from the power angle oscillation characteristic data as the maximum power angle swing.

[0012] Furthermore, injecting a preset disturbance signal into the new energy grid-connected system model with the optimized virtual equivalent inertia weights configured, which includes: The preset disturbance signal includes at least one of a step load disturbance or a random power fluctuation disturbance; The step load disturbance is realized by instantaneously increasing or decreasing the load at a specified bus, with the change amplitude being a preset percentage of the total system capacity, and the duration is set as a short-time transient process; The random power fluctuation disturbance includes injecting a random power fluctuation sequence that conforms to a statistical distribution at the new energy unit connection point, and the fluctuation amplitude is determined based on the percentage of the new energy unit rated capacity.

[0013] Furthermore, according to the dynamic response data, calculating the eigenvalues and damping ratios of the system dominant oscillation mode through eigenvalue analysis, and quantitatively evaluating the small-signal stability index in combination with the spectral radius of the state matrix, which includes: Based on the new energy grid-connected system model configured with the optimized virtual equivalent inertia weight, a state-space equation including the rotor dynamic equation of the synchronous unit, the virtual equivalent inertia control logic of the new energy unit, and the grid network equation is constructed; Calculate the eigenvalues of the state matrix in the state-space equation, and screen the low-frequency oscillation modes within the preset frequency range as the dominant oscillation modes; Calculate the damping ratio of the dominant oscillation mode, where the damping ratio reflects the attenuation speed of the oscillation amplitude; at the same time, calculate the spectral radius of the state matrix, and the spectral radius characterizes the maximum increase in the system's dynamic response; If the damping ratio reaches or exceeds the preset lower limit and the spectral radius is less than or equal to the preset radius, it is determined that the small-signal stability of the system meets the standard; Otherwise, it is determined as unqualified, and the virtual equivalent inertia weight and damping coefficient are re-optimized until the stability requirements are met.

[0014] Furthermore, the optimized virtual equivalent inertia weight configuration scheme and the stability assessment report are output. The optimized virtual equivalent inertia weight configuration scheme includes the recommended values of the virtual equivalent inertia at each node, the optimized damping coefficient, and the corresponding new energy penetration scenarios, including: Based on the optimized virtual equivalent inertia weight and damping coefficient, generate the recommended values of the virtual equivalent inertia at each node. The recommended values of the virtual equivalent inertia are bound to the new energy penetration scenarios, and their effectiveness is verified according to the frequency response data and power angle oscillation characteristic data in the dynamic simulation; Extract the damping ratio of the dominant oscillation mode, the spectral radius of the state matrix, and the real part distribution of the eigenvalues, and generate the stability level assessment result in combination with the preset threshold; The stability assessment report also includes the comparative analysis of the virtual equivalent inertia distribution under different penetration scenarios and the stability risk warning suggestions.

[0015] From the above technical solutions, it can be seen that the present invention has the following advantages: After obtaining the dynamic operation parameters of the synchronous units and new energy units, the present invention processes them to generate a node dynamic parameter combination including the proportion of the capacity of each unit in the total capacity of the node, ensuring that the subsequent inertia calculation closely conforms to the actual operation characteristics of the node; calculates the inherent equivalent inertia of the synchronous units and the virtual equivalent inertia of the new energy units respectively, corrects the virtual equivalent inertia in combination with the grid frequency response characteristics and then superimposes them to generate the total equivalent inertia of the system, fully integrating the inertia characteristics of the two types of units, and the total equivalent inertia of the system can truly reflect the system inertia level; constructs a virtual equivalent inertia allocation optimization model with the new energy penetration rate and the total equivalent inertia of the system as inputs, iteratively adjusts the virtual equivalent inertia weight and damping coefficient through preset constraints, configures the optimized virtual equivalent inertia weight into the system model and injects a preset disturbance signal for dynamic simulation to generate dynamic response data, and provides data support for analyzing the dynamic characteristics of the system under real disturbances by simulating the disturbance scenarios in actual operation; calculates the eigenvalues and damping ratios of the dominant oscillation modes through eigenvalue analysis, and quantitatively evaluates the small-signal stability index in combination with the spectral radius of the state matrix, avoiding the limitations of a single index through a multi-dimensional analysis method; finally outputs a configuration scheme and a stability evaluation report including the recommended value of the virtual equivalent inertia, the optimized damping coefficient and the penetration rate scenario. The present invention effectively improves the accuracy and reliability of the small-signal stability assessment of the new energy grid-connected system, and provides comprehensive data support for the safe and stable operation of the power grid under different new energy penetration rate scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a schematic flowchart of an embodiment of a method for simulating the small-signal stability of a new energy grid-connected system in the present invention; Figure 2 is a schematic diagram of the structure of the simulation system in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present application described here can be implemented in an order other than those illustrated or described here. In addition, the terms "including" and "corresponding to" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units does not have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0018] Embodiment 1 In this embodiment, the implementation method can be realized in the system, in the server, or in the terminal, and no specific limitation is made. Next, from the perspective of system implementation, the small-signal stability simulation method for the new energy grid-connected system in this application will be introduced. Please refer to Figure 1 , the method provided by the embodiment of this application includes the following steps: S11. Obtain the dynamic operation parameters of the synchronous units at each node of the system and the new energy units at each node of the system, perform node association processing on the dynamic operation parameters, and generate a node dynamic parameter set including the proportion of the capacity of each unit in the total capacity of the node where it is located; Taking the Guangxi Power Grid as an example, export the dynamic stability data of the synchronous units from the SCADA (Supervisory Control And Data Acquisition) data acquisition and monitoring control system, including the unit capacity, inertia time constant, and historical failure rate, as well as the grid-connected bus number; obtain the virtual equivalent inertia control parameters, output prediction error coefficients, and equipment availability rates of wind power and photovoltaic units from the new energy energy management platform, and record their access bus numbers. At the same time, parse the topology structure of the whole network AC system based on the common information model file of the Guangxi Power Grid, extract the bus node list and connection relationship, and generate a node-unit association index table.

[0019] Intelligently match the grid-connected bus numbers of the synchronous units and new energy units with the node numbers in the power grid topology. For example, directly associate the buses with unified coding through the number field; bind the low-voltage buses accessed by distributed new energy units to the nearest node in the topology model using geographical location coordinates. Secondly, check and complete the data, such as missing parameter processing and abnormal data elimination.

[0020] Associate the scattered unit dynamic operation parameters with the physical nodes in the power grid topology: for each node, accumulate the capacities of all synchronous units and new energy units under it to obtain the total node capacity; summarize the capacities of all nodes to generate the total installed capacity of the system; calculate the proportion of the capacity of each unit in the node capacity and write it into the node dynamic parameter set. The finally generated parameter set is a structured table, with each row corresponding to a node, including the following fields: node number, unit type (synchronous / new energy), unit capacity, inertia time constant (synchronous unit), virtual inertia control parameter (new energy unit), failure probability coefficient, output uncertainty coefficient (new energy unit), capacity ratio.

[0021] S12. Calculate the inherent equivalent inertia of the synchronous units and the virtual equivalent inertia of the new energy units respectively through the node dynamic parameter set; among them, correct the virtual equivalent inertia based on the power grid frequency response characteristics, and perform weighted superposition of the corrected virtual equivalent inertia and the inherent equivalent inertia according to the proportion of the capacity of each node in the total installed capacity of the system to generate the total equivalent inertia of the system; 1. Extract the rated capacity, inertia time constant, load fluctuation attenuation coefficient, and fault probability coefficient of synchronous units from the set of node dynamic parameters; among them, the load fluctuation attenuation coefficient is dynamically adjusted based on the historical load deviation rate, specifically determined according to the ratio of the load fluctuation standard deviation of the node where the unit is located in the past year to the regional average fluctuation level, and the value range is . The fault probability coefficient is assigned through the equipment reliability model or historical fault records. For example, the proportion of the annual fault times of a certain unit. The higher the fault probability of the unit, the worse its inertia availability.

[0022] For each synchronous unit, calculate its inherent equivalent inertia contribution value, summarize the contribution values of all synchronous units, and generate the total system inherent equivalent inertia : Among them: is the number of synchronous units, , and are respectively the rated capacity, inertia time constant, and load fluctuation attenuation coefficient of the th synchronous unit, is the fault probability coefficient of the th synchronous unit.

[0023] 2. Extract the virtual inertia control gain, rated capacity, output uncertainty coefficient, and fault probability coefficient of new energy units from the set of node dynamic parameters; among them, the output uncertainty coefficient is calculated based on the root mean square error of the actual output and the predicted output; the fault probability coefficient is assigned according to the equipment maintenance records or the mean time between failures (MTBF) data provided by the manufacturer. The output uncertainty coefficient is calculated as follows: Among them: is the number of samples within the statistical time window, and are respectively the actual output and predicted output at time

[0024] For each new energy unit, calculate its virtual equivalent inertia contribution value, summarize the contribution values of all new energy units, and generate the total system virtual equivalent inertia : Among them: is the number of new energy units, is the virtual inertia control gain of the th new energy unit, which is set by the controller parameters, and are respectively the rated capacity and output uncertainty coefficient of the th new energy unit, is the failure probability coefficient of the th new energy unit.

[0025] 3. Obtain the real-time frequency of the power grid through a phasor measurement unit (PMU), calculate the frequency deviation , where is the rated frequency; dynamically adjust the virtual equivalent inertia according to the frequency deviation: where: Set the upper limit of the correction coefficient to 1.2, Set the lower limit of the correction coefficient to 0.8.

[0026] Weight and superimpose the corrected equivalent inertia according to the proportion of each node's capacity in the total installed capacity of the system to generate the total equivalent inertia of the system as follows: where: and are respectively the number of nodes of synchronous units and new energy units, and are respectively the inherent equivalent inertia corresponding to the th synchronous unit and the virtual equivalent inertia corresponding to the th new energy unit in each node, and are respectively the rated capacity of the th synchronous unit and the th new energy unit, is the total installed capacity of the system.

[0027] Finally, input the total equivalent inertia of the system into the simulation model to verify whether the frequency response curve meets the requirements of the preset overshoot (≤5%) and adjustment time (≤10 seconds).

[0028] The above steps dynamically correct the virtual equivalent inertia through the frequency deviation, enhancing the inertia support ability of the system in the frequency emergency state; the capacity ratio weighting ensures the dominant role of large-capacity units in the system inertia.

[0029] S13. With the new energy penetration rate and the total equivalent inertia of the system as inputs, construct a virtual equivalent inertia allocation optimization model, and iteratively adjust the virtual equivalent inertia weights and damping coefficients of each node through preset optimization constraints; 1. By optimizing the virtual equivalent inertia weight and damping coefficient, the system dynamic response risk is minimized and the frequency robustness is improved, as follows: Objective function of the virtual equivalent inertia allocation optimization model To minimize the second norm of the system state matrix and the weighted frequency response robustness index of new energy penetration , the expression is as follows: Where: is the system state matrix constructed by the virtual equivalent inertia weight and the damping coefficient , is the new energy penetration rate, defined as the ratio of the total capacity of new energy units to the total system capacity, and are weight coefficients, , used to balance the priority of state matrix stability and frequency robustness, is the frequency deviation with respect to the perturbation frequency sensitivity matrix, is the matrix infinity norm, representing the frequency deviation suppression ability under the worst perturbation. The first term in the formula : The larger the total equivalent inertia of the system, the greater the allowable system dynamic response fluctuation, and the state matrix gain needs to be reduced to maintain stability; the second term : The higher the new energy penetration rate, the more critical the frequency anti-perturbation ability, and key optimization is required.

[0030] 2. The constraint conditions of the virtual equivalent inertia allocation optimization model include node virtual equivalent inertia weight constraints, damping coefficient range constraints, and stability criterion threshold constraints. Among them, the stability criterion thresholds include the real part of the eigenvalue, the damping ratio, and the synchronizing torque coefficient.

[0031] Specifically, the node virtual equivalent inertia weight constraint: Where: and are the virtual equivalent inertia weight and capacity of the th new energy unit. The upper limit of the single-node weight is that the virtual equivalent inertia allocation of the new energy unit does not exceed its capacity ratio, to avoid over-allocation; the lower limit of the total weight is that the total contribution of the virtual equivalent inertia needs to match the new energy penetration rate and the total system inertia to ensure the inertial support demand; is the virtual equivalent inertia allocation ratio coefficient, set by the system inertia demand.

[0032] Damping coefficient range constraint: Wherein: is the damping coefficient of the th unit, and are the preset lower and upper limits of the damping coefficient respectively. The damping coefficient needs to be within a reasonable range to prevent over-damping (slow response) or under-damping (continuous oscillation).

[0033] Stability criterion threshold constraint: Wherein: is the real part of the eigenvalue of the dominant oscillation mode, which needs to be less than the negative threshold to ensure rapid decay of the oscillation; is the damping ratio, which needs to be greater than the lower limit to suppress the oscillation amplitude; is the synchronizing torque coefficient, which needs to be positive to maintain the stability of the power angle.

[0034] 3. Iterative optimization process: Set the initial virtual equivalent inertia weight , damping coefficient ; construct the state matrix , calculate the objective function ; use the gradient descent algorithm or particle swarm optimization (PSO) to calculate the gradients of the objective function with respect to and , and update the parameters: Wherein: is the learning rate.

[0035] Apply constraints to the updated parameters: truncate to , limit within . Verify the stability criterion. If not met, return for continued iteration. When the change rate of the objective function and the stability criterion are satisfied, terminate the optimization.

[0036] S14. Configure the optimized virtual equivalent inertia weight into the new energy grid-connected system model, inject a preset disturbance signal into the new energy grid-connected system model configured with the optimized virtual equivalent inertia weight, and perform dynamic simulation to generate dynamic response data; 1. Configure the optimized virtual equivalent inertia weight into the node controller corresponding to the new energy grid-connected system model, and update the inertia distribution parameters of the system; The inertia distribution parameters of the system refer to the virtual equivalent inertia weights and damping coefficients of the virtual synchronous generators (VSGs) at each node; the weight conversion is to convert the optimized weights into the actual inertia time constants; the damping coefficient assignment is to directly use the optimized . After the parameters are updated, check the parameters of the VSG module in the Simulink model to confirm and have been updated. Please refer to Figure 2 , Sync-G nodes (1 - 3): Keep the fixed parameters , , no update is required; VSG nodes (4, 7, 9): Update the parameters according to the optimization results.

[0037] 2. Inject a preset disturbance signal into the new - energy grid - connected system model configured with the optimized virtual equivalent inertia weights, perform time - domain dynamic simulation, and record the frequency deviation curves of each node and the change curves of the power angle difference between synchronous units in real time to generate frequency response data and power angle oscillation characteristic data; Specifically, the preset disturbance signal includes at least one of step - load disturbance or random power fluctuation disturbance; the step - load disturbance is realized by instantaneously increasing or decreasing the load at a specified bus, with the change amplitude being a preset percentage of the total system capacity and the duration being set as a short - time transient process; the random power fluctuation disturbance includes injecting a random power fluctuation sequence that conforms to a statistical distribution at the connection point of new - energy units, and the fluctuation amplitude is determined based on the percentage of the rated capacity of new - energy units.

[0038] As Figure 2 , the step - load disturbance can be set at all load nodes (Load5, Load6, Load8). For example, a 10% sudden drop of the total system capacity (such as a 100 - MW sudden drop in a 1000 - MW system) is applied at Load5, lasting for 0.1 s. The random power fluctuation disturbance can be set at all or some of the VSG nodes (VSG4, VSG7, VSG9). For example, a normally - distributed random fluctuation with an amplitude of 5% of the rated capacity (such as ±10 MW fluctuation for a 200 - MW unit) is injected at VSG4. Simulation execution: Set the simulation parameters: Time: 0 - 10 s; Step size: 1 ms; Solver: ode23t (suitable for stiff systems). Frequency deviation: of Sync - G nodes (1 - 3); Power angle difference: .

[0039] 3. From the frequency response data, calculate the difference between the maximum frequency and the steady - state frequency, and determine the frequency overshoot by dividing the difference by the steady - state frequency; and calculate the time from the start of the disturbance until the frequency returns to within the steady - state error band as the regulation time; extract the maximum instantaneous absolute value of the power angle difference of the synchronous units from the power angle oscillation characteristic data as the maximum power angle swing.

[0040] By analyzing the frequency response data, calculate the difference between the maximum frequency and the steady-state frequency, and determine the frequency overshoot by dividing the difference by the steady-state frequency. Calculate the time from the start of the disturbance until the frequency returns within the steady-state error band as the regulation time; identify the maximum deviation value from the power angle oscillation data as the power angle swing, and quantify the system's dynamic response performance. This process combines the peak detection algorithm with the time window determination to ensure the accuracy and repeatability of the index extraction.

[0041] S15. According to the dynamic response data, calculate the eigenvalues and damping ratios of the system's dominant oscillation modes through eigenvalue analysis, and quantitatively evaluate the small-signal stability index by combining with the spectral radius of the state matrix; 1. Based on the new energy grid-connected system model configured with the optimized virtual equivalent inertia weight, construct a state-space equation including the rotor dynamic equation of the synchronous unit, the virtual equivalent inertia control logic of the new energy unit, and the grid network equation; Synchronous unit model (nodes 1-3): The dynamic behavior of the synchronous generator is described by the rotor motion equation: where: is the rotor angle of the th synchronous generator, is the speed deviation of the th synchronous generator, is the initial speed deviation, is the inertia time constant of the th synchronous generator, is the damping coefficient of the th synchronous generator, characterizing the decay ability of the speed deviation, and are the mechanical power input and electromagnetic power output of the th synchronous generator.

[0042] VSG control model (nodes 4, 7, 9): The dynamic equation of the virtual synchronous generator (VSG) simulates the inertial characteristics of the synchronous unit: where: is the inertia time constant of the th VSG, is the damping coefficient of the th VSG, and are the reference power and actual output power of the th VSG respectively.

[0043] Power balance equation based on the nodal admittance matrix: Wherein: and are the active and reactive powers injected into the node . is the voltage phasor of the node . is the element of the nodal admittance matrix, which characterizes the topological connection relationship of the power grid.

[0044] 2. Calculate the eigenvalues of the state matrix in the state-space equation, and screen the low-frequency oscillation modes within the preset frequency range as the dominant oscillation modes; Linearize the synchronous generator set and VSG equations to generate a 10×10 state matrix A, including: 3 synchronous generator sets (2 state variables for each set , ), 3 VSGs (2 state variables for each set , ).

[0045] Calculate the eigenvalues of the state matrix A, and screen the low-frequency oscillation modes: Wherein: is the real part of the eigenvalue, reflecting the oscillation decay rate, is the imaginary part of the eigenvalue, corresponding to the oscillation frequency, is the oscillation frequency.

[0046] 3. Calculate the damping ratio of the dominant oscillation mode, and the damping ratio reflects the decay rate of the oscillation amplitude; at the same time, calculate the spectral radius of the state matrix, and the spectral radius characterizes the maximum increase of the system dynamic response; 4. If the damping ratio reaches or exceeds the preset lower limit and the spectral radius is less than or equal to the preset radius, it is determined that the small-signal stability of the system meets the standard; 5. Otherwise, it is determined as unqualified, and the virtual equivalent inertia weight and damping coefficient are re-optimized until the stability requirements are met.

[0047] First, based on the constructed state matrix A, calculate its eigenvalues and screen out the dominant low-frequency oscillation modes, and calculate the damping ratio through the formula : , and at the same time obtain the spectral radius , where is the eigenvalue of the dominant oscillation mode, and represent the real part and the imaginary part of the eigenvalue respectively; if the damping ratio satisfies and the spectral radius , it is determined that the system is stable; otherwise, the virtual equivalent inertia weight and damping coefficient need to be adjusted through gradient descent or particle swarm optimization algorithm, and after updating the parameters, the simulation and stability evaluation are performed again until all indicators are qualified.

[0048] S16. Output the optimized virtual equivalent inertia weight configuration scheme and stability evaluation report. The optimized virtual equivalent inertia weight configuration scheme includes the recommended values of virtual equivalent inertia for each node, the optimized damping coefficient, and the corresponding new energy penetration rate scenarios.

[0049] 1. Based on the optimized virtual equivalent inertia weight and damping coefficient, generate the recommended values of virtual equivalent inertia for each node. The recommended values are bound to the new energy penetration rate scenarios, and their effectiveness is verified according to the frequency response data and power angle oscillation characteristic data in the dynamic simulation; 2. Extract the damping ratio, state matrix spectral radius, and real part distribution of eigenvalues of the dominant oscillation mode, and generate the stability level evaluation results in combination with the preset thresholds; 3. The report also includes the comparative analysis of virtual equivalent inertia distribution under different penetration rate scenarios and stability risk warning suggestions.

[0050] Specifically, based on the optimized virtual equivalent inertia weight and damping coefficient, generate the recommended values of virtual equivalent inertia for each node. For example, for node 4 , for node 7 ; and bind them to different new energy penetration rate scenarios (such as 30%, 50%, 70%), and verify their effectiveness through the frequency overshoot and power angle swing of the dynamic simulation; extract the damping ratio, state matrix spectral radius, and real part distribution of eigenvalues of the dominant oscillation mode, generate the stability level (such as "excellent", "warning", "dangerous"), and finally output the report to compare the inertia distribution differences under different penetration rate scenarios (such as higher penetration rate requires higher ), and put forward risk warnings (such as increasing the standby inertia when the penetration rate exceeds 50%) to ensure that the configuration scheme can directly guide the engineering implementation.

[0051] The above embodiments realize data integration, inertia calculation, optimized allocation, simulation evaluation, and result output through multi-step collaboration. Accurately process multi-source dynamic data, comprehensively consider the system inertia characteristics and new energy penetration rate, optimize the virtual equivalent inertia allocation and comprehensively evaluate the stability, effectively making up for the deficiencies of traditional methods in terms of uncertainty adaptation, multi-scenario adaptation, and comprehensive evaluation. Effectively improve the accuracy and reliability of the small-signal stability evaluation of the new energy grid-connected system, and provide technical support for the safe and stable operation of the power grid under different new energy penetration rate scenarios.

[0052] It can be understood that those skilled in the art can, under the guidance of the above embodiments, combine various implementation manners in the above various embodiments to obtain technical solutions of various implementation manners.

[0053] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A small disturbance stability simulation method for a new energy grid-connected system, characterized in that: include: Acquire dynamic operating parameters of synchronous units at each node of the system and new energy units at each node of the system, perform node association processing on the dynamic operating parameters, and generate a node dynamic parameter set including the ratio of the capacity of each unit to the total capacity of the node; The inherent equivalent inertia of the synchronous unit and the virtual equivalent inertia of the new energy unit are calculated respectively by the node dynamic parameter set; wherein the virtual equivalent inertia is corrected based on the frequency response characteristics of the power grid, and the corrected virtual equivalent inertia and the inherent equivalent inertia are weightedly superimposed according to the proportion of each node capacity to the total installed capacity of the system to generate the total equivalent inertia of the system; Taking the new energy penetration rate and the total equivalent inertia of the system as input, a virtual equivalent inertia distribution optimization model is constructed, and the virtual equivalent inertia weight and damping coefficient of each node are iteratively adjusted through preset optimization constraints; configuring the optimized virtual equivalent inertia weight into the new energy grid-connected system model, injecting a preset disturbance signal into the new energy grid-connected system model configured with the optimized virtual equivalent inertia weight, and performing dynamic simulation to generate dynamic response data; According to the dynamic response data, the eigenvalue and damping ratio of the dominant oscillation mode of the system are calculated by eigenvalue analysis, and the small disturbance stability index is quantitatively evaluated in combination with the state matrix spectrum radius; Output the optimized virtual equivalent inertia weight configuration scheme and stability assessment report. The optimized virtual equivalent inertia weight configuration scheme includes the recommended value of virtual equivalent inertia of each node, optimized damping coefficient and corresponding new energy penetration scenario.

2. The small disturbance stability simulation method of the new energy grid-connected system according to claim 1 is characterized in that: The calculating the inherent equivalent inertia of the synchronous unit and the virtual equivalent inertia of the new energy unit respectively through the node dynamic parameter set includes: Intrinsic equivalent inertia The calculation formula is: in: is the number of synchronous units, , and Respectively Rated capacity, inertia time constant and load fluctuation attenuation coefficient of each synchronous unit, For the Failure probability coefficient of synchronous units.

3. The small disturbance stability simulation method of the new energy grid-connected system according to claim 2 is characterized in that: The calculating of the inherent equivalent inertia of the synchronous unit and the virtual equivalent inertia of the new energy unit by using the node dynamic parameter set also includes: Virtual equivalent inertia The calculation formula is: in: is the number of new energy units, For the The virtual inertia control gain of each new energy unit is set by the controller parameters. and Respectively The rated capacity and output uncertainty coefficient of each new energy unit, For the The failure probability coefficient of each new energy unit.

4. The small disturbance stability simulation method of the new energy grid-connected system according to claim 3 is characterized in that: The virtual equivalent inertia is corrected based on the grid frequency response characteristics, and the corrected virtual equivalent inertia is weightedly superimposed with the inherent equivalent inertia according to the proportion of each node capacity to the total installed capacity of the system to generate the total equivalent inertia of the system, including: Corrected virtual equivalent inertia : in: is the real-time frequency measurement value, is the rated frequency of the power grid; Total equivalent inertia of the system : in: and are the number of nodes of synchronous units and new energy units, and are the first The corresponding inherent equivalent inertia of the first synchronous unit and the The virtual equivalent inertia corresponding to each new energy unit is and Respectively Synchronous units and The rated capacity of each new energy unit is is the total installed capacity of the system.

5. The small disturbance stability simulation method of the new energy grid-connected system according to claim 4 is characterized in that: The virtual equivalent inertia distribution optimization model is constructed by taking the new energy penetration rate and the total equivalent inertia of the system as inputs, and the virtual equivalent inertia weight and damping coefficient of each node are iteratively adjusted through preset optimization constraints, including: Objective function of virtual equivalent inertia distribution optimization model To minimize the bi-norm of the system state matrix and the robustness index of frequency response weighted by renewable energy penetration , the expression is as follows: in: The virtual equivalent inertia weight and the damping coefficient The system state matrix constructed, is the penetration rate of new energy, Defined as the ratio of the total capacity of new energy units to the total capacity of the system, and is the weight coefficient, is the frequency response correction factor, is the frequency deviation The disturbance frequency The sensitivity matrix, is the matrix infinity norm.

6. The small disturbance stability simulation method of the new energy grid-connected system according to claim 5 is characterized in that: The virtual equivalent inertia distribution optimization model is constructed by taking the new energy penetration rate and the total equivalent inertia of the system as inputs, and the virtual equivalent inertia weight and damping coefficient of each node are iteratively adjusted through preset optimization constraints, and also includes: The constraints of the virtual equivalent inertia distribution optimization model include node virtual equivalent inertia weight constraints, damping coefficient range constraints and stability criterion threshold constraints, among which the stability criterion threshold includes the real part of the eigenvalue, the damping ratio and the synchronization distance coefficient.

7. The small disturbance stability simulation method of the new energy grid-connected system according to claim 1 is characterized in that: The step of configuring the optimized virtual equivalent inertia weight into the new energy grid-connected system model, injecting a preset disturbance signal into the new energy grid-connected system model configured with the optimized virtual equivalent inertia weight, and performing dynamic simulation to generate dynamic response data includes: The optimized virtual equivalent inertia weight is configured to the corresponding node controller in the new energy grid-connected system model, and the inertia distribution parameters of the system are updated; Injecting a preset disturbance signal into the new energy grid-connected system model configured with the optimized virtual equivalent inertia weight, performing time domain dynamic simulation, recording the frequency deviation curve of each node and the power angle difference change curve between synchronous units in real time, and generating frequency response data and power angle oscillation characteristic data; From the frequency response data, the difference between the maximum frequency and the steady-state frequency is calculated, and the frequency overshoot is determined by dividing the difference by the steady-state frequency; and the time from the start of the disturbance to the frequency recovery to the steady-state error band is calculated as the adjustment time; and the maximum instantaneous absolute value of the angle of attack difference of the synchronous unit is extracted from the angle of attack oscillation characteristic data as the maximum angle of attack swing.

8. The small disturbance stability simulation method of the new energy grid-connected system according to claim 7 is characterized in that: The injecting a preset disturbance signal into the new energy grid-connected system model configured with the optimized virtual equivalent inertia weight comprises: The preset disturbance signal includes at least one form of a step load disturbance or a random power fluctuation disturbance; The step load disturbance is achieved by applying an instantaneous sudden increase or decrease in load at a designated bus, the change amplitude is a preset proportion of the total system capacity, and the duration is set to a short-term transient process; The random power fluctuation disturbance includes injecting a random power fluctuation sequence that conforms to the statistical distribution at the grid connection point of the new energy unit, and the fluctuation amplitude is determined based on the percentage of the rated capacity of the new energy unit.

9. The small disturbance stability simulation method of the new energy grid-connected system according to claim 7 is characterized in that: The method of calculating the eigenvalue and damping ratio of the dominant oscillation mode of the system by eigenvalue analysis based on the dynamic response data and quantitatively evaluating the small disturbance stability index in combination with the state matrix spectrum radius includes: Based on the new energy grid-connected system model configured with the optimized virtual equivalent inertia weight, a state space equation including a synchronous unit rotor dynamic equation, a new energy unit virtual equivalent inertia control logic and a power grid network equation is constructed; Calculating the eigenvalue of the state matrix in the state space equation, and selecting a low-frequency oscillation mode in a preset frequency range as a dominant oscillation mode; Calculating the damping ratio of the dominant oscillation mode, wherein the damping ratio reflects the decay rate of the oscillation amplitude; and calculating the spectral radius of the state matrix, wherein the spectral radius represents the maximum increase of the dynamic response of the system; If the damping ratio reaches or exceeds the preset lower limit, and the spectrum radius is less than or equal to the preset radius, it is determined that the system small disturbance stability meets the standard; Otherwise, it is judged as not meeting the standard, and the virtual equivalent inertia weight and damping coefficient are re-optimized until the stability requirements are met.

10. The small disturbance stability simulation method of the new energy grid-connected system according to claim 1, characterized in that: The output optimized virtual equivalent inertia weight configuration scheme and stability evaluation report, the optimized virtual equivalent inertia weight configuration scheme includes the recommended value of virtual equivalent inertia of each node, the optimized damping coefficient and the corresponding new energy penetration scenario, including: Based on the optimized virtual equivalent inertia weight and damping coefficient, the recommended virtual equivalent inertia value of each node is generated. The recommended virtual equivalent inertia value is bound to the new energy penetration scenario, and its effectiveness is verified based on the frequency response data and power angle oscillation characteristic data in the dynamic simulation. Extract the damping ratio, state matrix spectrum radius and eigenvalue real distribution of the dominant oscillation mode, and generate stability level evaluation results based on the preset threshold; The stability assessment report also includes comparative analysis of virtual equivalent inertia distribution under different permeability scenarios and stability risk warning recommendations.

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