New energy power station multi-source coupling active frequency modulation method and system

Abstract

The invention relates to the field of power grid frequency modulation, in particular to a multi-source coupling active frequency modulation method and system for a new energy power station. The method comprises the steps of monitoring power grid frequency in real time and calculating a frequency deviation signal; acquiring real-time operation data of a fan unit, a photovoltaic unit and an energy storage system based on the signal, and evaluating rotor kinetic energy reserve, power regulation margin, state of charge and chargeable and dischargeable power to obtain multi-source frequency modulation capability data; dynamically distributing a frequency modulation task and generating a coordination instruction through a multi-objective optimization function considering the frequency modulation response speed, the capacity and the system operation stability in combination with the signals and the data; an execution instruction controls the three types of equipment to cooperatively adjust, and rapid frequency support is provided. Three types of core frequency modulation resources are integrated, the new energy frequency modulation potential is fully excavated, and the problems that in the prior art, resource utilization is insufficient, and the power grid frequency supporting capacity is insufficient due to the fact that multiple targets are not considered are solved.

Description

Technical Field

[0001] This invention relates to the field of power grid frequency regulation, and in particular to a multi-source coupling active frequency regulation method and system for new energy power plants. Background Technology

[0002] With the rapid development of new energy power generation technologies, the penetration rate of new energy power sources such as wind turbines and photovoltaic units in the power grid continues to increase, providing important support for achieving the "dual carbon" goal. However, this also brings severe challenges to the stable control of power grid frequency. New energy power generation has inherent randomness, volatility, and intermittency. Its drastic output fluctuations can cause the power grid frequency to deviate from the rated value, leading to frequency deviation problems. Meanwhile, the frequency regulation capacity of traditional thermal power units is gradually decreasing, further exacerbating the pressure on power grid frequency regulation. To solve this problem, the industry has proposed technical approaches for new energy to participate in power grid frequency regulation. Among them, wind-storage coordinated frequency regulation has become a research hotspot. For example, the existing technology CN115313430B discloses a wind-storage coordinated power grid frequency regulation optimization method, which realizes the frequency regulation requirement through power allocation of wind turbines and energy storage devices. However, this method only considers the coordination between wind turbines and energy storage systems and does not include photovoltaic units, an important new energy frequency regulation resource. This results in the underutilization of frequency regulation resources. Moreover, its power allocation only focuses on cost, wind turbine rotor stall safety margin, and energy storage real-time adjustment margin, without simultaneously taking into account the multi-objective optimization requirements of frequency regulation response speed, frequency regulation capacity, and system operation stability, resulting in insufficient dynamic adaptability. Furthermore, existing technologies often employ simplified models to assess the rotor kinetic energy reserves of wind turbine units, failing to accurately reflect the dynamic characteristics of the transmission chain and resulting in significant deviations in the estimation of kinetic energy frequency regulation capacity. The assessment of power regulation margin for photovoltaic units lacks precise models that consider dynamic changes in environmental conditions, making it difficult to clearly define their bidirectional power regulation capabilities and voltage operating range. The estimation accuracy of the state of charge (SOC) of energy storage systems is insufficient, and it does not fully consider battery aging and temperature factors, affecting the accurate determination of chargeable and dischargeable power. Simultaneously, in the frequency regulation task allocation stage, existing methods do not differentiate allocation for different time-scale components of frequency deviation, resulting in the inability to accurately meet the frequency regulation requirements of rapid fluctuation components and slow trend components. Moreover, the execution of control strategies lacks specificity, and problems such as torsional vibration of the wind turbine transmission chain, fluctuations in the operating point switching of photovoltaic systems, and multi-time-scale response differences in energy storage remain unresolved. Ultimately, this leads to slow frequency regulation response speed, insufficient frequency regulation capacity, and poor system operational stability in new energy power plants, making it difficult to meet the high-precision frequency support requirements of the power grid. Summary of the Invention

[0003] This invention provides a multi-source coupled active frequency regulation method and system for new energy power plants, aiming to solve the problems of insufficient utilization of frequency regulation resources and failure to take into account the multi-objective optimization of frequency regulation response speed, capacity and system operation stability in existing new energy frequency regulation technologies, resulting in insufficient grid frequency support capacity.

[0004] To achieve the above objectives, the following technical solution is adopted.

[0005] A multi-source coupled active frequency regulation method for new energy power plants includes the following steps: The system monitors the grid frequency in real time, acquires the measured grid frequency value, and calculates the difference between the measured grid frequency value and the rated frequency to obtain a frequency deviation signal. Based on the frequency deviation signal, it simultaneously collects real-time operating status data of wind turbine units, photovoltaic units, and energy storage systems in the new energy power plant, and evaluates the rotor kinetic energy reserve of the wind turbine unit, the power regulation margin of the photovoltaic unit, and the state of charge and charge / discharge power of the energy storage system to obtain multi-source frequency regulation capability data. Based on the frequency deviation signal and the multi-source frequency regulation capability data, it dynamically allocates the frequency regulation tasks of the wind turbine unit, photovoltaic unit, and energy storage system according to a preset multi-objective optimization function, and generates multi-source coordinated frequency regulation commands. The multi-objective optimization function simultaneously considers frequency regulation response speed, frequency regulation capacity, and system operation stability. It executes the multi-source coordinated frequency regulation commands to control the wind turbine unit to adjust its output power and rotor speed, control the photovoltaic unit to adjust its operating point from the maximum power point, and control the energy storage system to regulate its charge and discharge power, jointly providing rapid frequency support.

[0006] Optional steps for assessing the rotor kinetic energy reserve of the wind turbine unit include: A three-mass drivetrain model of the wind turbine unit is established, which includes the dynamic characteristics of the low-speed shaft, gearbox, and high-speed shaft. Real-time data collection is performed on the high-speed shaft speed, blade pitch angle, and generator torque of the wind turbine unit to obtain the first set of real-time data. This first set of real-time data is input into the three-mass drivetrain model to calculate the specific kinetic energy value currently stored in the wind turbine rotor. Simultaneously, based on the aerodynamic characteristics of the wind turbine blades and the current wind speed data, the maximum aerodynamic capture power of the wind turbine is calculated. According to the generator's thermal capacity constraint and the gearbox's torque limit, the upper limit of the rotor kinetic energy frequency modulation power and the upper limit of the duration are determined. Combining the specific kinetic energy value, the maximum aerodynamic capture power, the upper limit of the power, and the upper limit of the duration, the specific rotor kinetic energy frequency modulation capacity that the wind turbine unit can safely release or absorb under the current operating conditions is obtained by solving the polyhedral description of the feasible region for kinetic energy frequency modulation.

[0007] Optionally, the steps for evaluating the power regulation margin of photovoltaic units specifically include: A single-diode equivalent circuit model of the photovoltaic array is constructed, which includes series resistance, parallel resistance, and diode ideality factor parameters. Parameter identification is performed on the single-diode equivalent circuit model under different environmental conditions to obtain an accurate mathematical model of the photovoltaic unit. The DC-side voltage and current of the photovoltaic unit are monitored in real time, and combined with current irradiance and ambient temperature measurements to obtain a second set of real-time data. This second set of real-time data is input into the accurate mathematical model of the photovoltaic unit to calculate the maximum output power corresponding to the current maximum power point. The rate of change of power with voltage is accurately calculated by solving the derivative of the PV characteristic curve of the photovoltaic array at the current operating point. Based on the maximum output power and the rate of change of power with voltage, a feasible range for power regulation considering inverter capacity constraints and grid voltage requirements is established. This feasible range clearly defines the specific power regulation capabilities of the photovoltaic unit in both upward and downward frequency regulation directions, as well as the corresponding voltage operating range.

[0008] Optionally, the steps for assessing the state of charge and charge / discharge capacity of an energy storage system include: A dual-state estimation method combining the extended Kalman filter algorithm and open-circuit voltage calibration is employed. The specific execution process includes: establishing a second-order equivalent circuit model of the energy storage system, which includes ohmic internal resistance, polarization resistance, and polarization capacitance parameters; real-time acquisition of the energy storage system's terminal voltage, current, and ambient temperature to obtain a third set of real-time data; processing the third set of real-time data using the extended Kalman filter algorithm to estimate the energy storage system's state of charge (SOC) value in real time, while simultaneously estimating changes in model parameters; measuring the open-circuit voltage during the energy storage system's resting period to periodically calibrate the SOC value estimated by the extended Kalman filter algorithm; based on the calibrated SOC value, combined with the battery aging model and real-time temperature data from the thermal management system, constructing an optimal power capacity calculation framework under multiple constraints; and determining the maximum sustainable charging power and maximum sustainable discharging power of the energy storage system under the current SOC and temperature conditions by solving an optimization problem aimed at minimizing battery life loss, and calculating the corresponding power regulation duration.

[0009] Optionally, the step of dynamically allocating frequency modulation tasks according to a preset multi-objective optimization function specifically includes: A multi-objective optimization function is constructed, comprising a frequency deviation integral, a frequency regulation cost function, and a device stress penalty term. The frequency deviation signal is decomposed into frequency components at different time scales, including rapidly changing frequency fluctuation components and slowly changing frequency trend components. For the rapidly changing frequency fluctuation components, an allocation subproblem with response speed as the primary optimization objective is established and solved online using a model predictive control framework to generate fast power allocation instructions for wind turbine units and energy storage systems. For the slowly changing frequency trend components, an allocation subproblem with economy as the primary optimization objective is established and solved using a mixed integer programming method to generate slow power allocation instructions for photovoltaic units and energy storage systems. A two-layer coordination mechanism is designed, in which the upper-layer coordinator adaptively adjusts the weight coefficients of the fast and slow allocation subproblems according to real-time frequency characteristics, and the lower-layer actuators execute the corresponding power allocation instructions respectively. By monitoring the actual response of each frequency regulation resource in real time, feedback is sent to the upper-layer coordinator to form a closed-loop optimization, ultimately outputting a specific power allocation scheme that considers multiple time scales and resource characteristics.

[0010] Optionally, the steps for controlling the fan unit to adjust its output power and rotor speed specifically include: The design of a nonlinear controller based on feedback linearization includes: establishing a full-order mathematical model of the wind turbine that incorporates aerodynamic characteristics, transmission chain flexibility, and generator dynamics; performing a differential homeomorphic transformation on the full-order mathematical model of the wind turbine to convert it into the Brunovsky canonical form; calculating the virtual control quantity in the converted system based on the frequency modulation power command; decomposing the original nonlinear system into multiple decoupled linear subsystems through precise linearization using nonlinear state feedback; designing a proportional-integral-derivative controller for each linear subsystem, used for tracking the power command and regulating the rotor speed, respectively; monitoring the torsional vibration mode of the transmission chain in real time during frequency modulation, and actively suppressing mechanical vibration caused by rapid power adjustment by introducing a torsional vibration damping control loop; and activating a robust control-based protection strategy when a drastic change in wind speed or a risk of exceeding the speed limit is detected, ensuring the structural safety of the wind turbine while maintaining the frequency modulation effect.

[0011] Optionally, the step of controlling the photovoltaic unit to adjust its operating point from the maximum power point specifically includes: A two-layer operating point localization algorithm based on a combination of binary search and gradient descent is proposed. The specific execution process includes: in the inner loop, a gradient descent method is used to perform a local fine search near the current voltage reference value, determining the direction of the fastest power change by calculating the partial derivative of power with respect to voltage; in the outer loop, a binary search algorithm is used to perform a global coarse search within the entire photovoltaic array's operating voltage range to quickly locate the target power region; a dynamic optimization strategy for operating point switching is established. Upon receiving a frequency modulation command, the optimal switching trajectory from the current operating point to the target operating point is first calculated. This trajectory is constrained by the inverter's DC-side voltage change rate limit, with the optimization objective being minimizing energy loss during the switching process; by adjusting the photovoltaic inverter's voltage control loop reference value in real time, the photovoltaic unit smoothly transitions to the target operating point along the optimal switching trajectory; in the power regulation stage, a sliding mode variable structure control strategy is adopted to force the system state variables to move on a preset sliding surface, suppressing power fluctuations caused by sudden changes in illumination.

[0012] Optionally, the steps for controlling the charging and discharging power regulation of the energy storage system specifically include: The design incorporates a multi-timescale power allocation architecture based on fractional calculus, specifically including: establishing a fractional-order equivalent circuit model of the battery considering diffusion dynamics and electrochemical polarization; decomposing the frequency modulation power command into power components with different time constants, including millisecond-level inertial response components, second-level primary frequency modulation components, and minute-level secondary frequency modulation components; for the millisecond-level power components, employing a fractional-order proportional-integral-derivative controller, utilizing the memory characteristics of fractional-order operators to improve the tracking accuracy of fast power commands; for the second-level power components, designing a power divider based on model predictive control, balancing frequency modulation requirements with energy storage system losses by solving a finite-time domain optimization problem; for the minute-level power components, constructing an optimized scheduling framework aimed at extending battery life, comprehensively considering state-of-charge recovery requirements and frequency modulation market benefits; establishing a multi-timescale coordination mechanism, ensuring the consistency of control objectives at each level through Lyapunov optimization methods, and ultimately generating specific power control signals that satisfy multiple constraints to drive the energy storage converter to perform precise charging and discharging operations.

[0013] A multi-source coupled active frequency regulation system for a new energy power plant includes: a frequency monitoring module, a multi-source capability assessment module, a task allocation module, and an execution control module; The frequency monitoring module is configured to monitor the power grid frequency in real time, obtain the power grid frequency measurement value, calculate the difference between the power grid frequency measurement value and the rated frequency to obtain a frequency deviation signal, and send the frequency deviation signal to the multi-source capability assessment module and the task allocation module. The multi-source capability assessment module is configured to simultaneously collect real-time operating status data of wind turbine units, photovoltaic units, and energy storage systems in the new energy power station based on the received frequency deviation signal, assess the rotor kinetic energy reserve of the wind turbine unit, the power regulation margin of the photovoltaic unit, and the state of charge and chargeable / dischargeable power of the energy storage system, respectively, obtain multi-source frequency regulation capability data, and send the multi-source frequency regulation capability data to the task allocation module; The task allocation module is configured to dynamically allocate frequency regulation tasks of the wind turbine, the photovoltaic unit, and the energy storage system based on the received frequency deviation signal and the multi-source frequency regulation capability data, according to a preset multi-objective optimization function, generate multi-source coordinated frequency regulation instructions, and send the multi-source coordinated frequency regulation instructions to the execution control module. The execution control module is configured to execute the received multi-source coordinated frequency modulation command, control the wind turbine to adjust its output power and rotor speed, control the photovoltaic unit to adjust its operating point to deviate from the maximum power point, and control the energy storage system to adjust its charging and discharging power, so as to jointly provide rapid frequency support.

[0014] Optionally, the task allocation module includes: a frequency decomposition unit, an optimization solution unit, and a coordination and control unit; The frequency decomposition unit is configured to decompose the received frequency deviation signal into frequency components at different time scales, including rapidly changing frequency fluctuation components and slowly changing frequency trend components, and send the decomposed rapidly changing frequency fluctuation components and slowly changing frequency trend components to the optimization solution unit. The optimization solution unit is configured to establish an allocation subproblem with response speed as the primary optimization objective for the rapidly changing frequency fluctuation component, solve it online using a model predictive control framework, and generate fast power allocation instructions for wind turbine units and energy storage systems. Simultaneously, for the slowly changing frequency trend component, it establishes an allocation subproblem with economy as the primary optimization objective, solves it using a mixed integer programming method, generates slow power allocation instructions for photovoltaic units and energy storage systems, and sends the fast power allocation instructions and the slow power allocation instructions to the coordination control unit. The coordination and control unit is configured to adaptively adjust the weight coefficients of the fast allocation subproblem and the slow allocation subproblem according to the real-time frequency characteristics, integrate the fast power allocation command and the slow power allocation command through a two-layer coordination mechanism to form the final multi-source coordinated frequency modulation command, and send the multi-source coordinated frequency modulation command to the execution control module.

[0015] Compared with the prior art, the present invention has the following beneficial effects: This application provides a multi-source coupled active frequency regulation method for new energy power plants. By integrating three core frequency regulation resources—wind turbine units, photovoltaic units, and energy storage systems—it fully explores and efficiently utilizes the frequency regulation potential of new energy sources. Based on real-time grid frequency deviation signals, this method comprehensively collects operational status data from each source and systematically evaluates its frequency regulation capabilities. It dynamically allocates frequency regulation tasks using a multi-objective optimization function that considers frequency regulation response speed, capacity, and system stability. This enables various frequency regulation resources to accurately respond to grid frequency demands based on their own characteristics, rapidly suppressing frequency fluctuations while ensuring the overall stability of the system, effectively improving the frequency support capability of new energy power plants for the grid.

[0016] By establishing precise equipment models and scientific evaluation methods, this approach significantly improves the regulation accuracy and reliability of various frequency regulation resources: Based on a three-mass transmission chain model and aerodynamic characteristics, the kinetic energy reserve assessment of the wind turbine rotor can accurately obtain the kinetic energy frequency regulation capacity that can be safely released or absorbed, providing a reliable basis for power allocation; combined with a single-diode equivalent circuit model and parameter identification technology, the photovoltaic power regulation margin assessment clearly defines the bidirectional regulation capability and voltage operating range of photovoltaic units under different operating conditions, improving the controllability of photovoltaic participation in frequency regulation; the energy storage state estimation method, combining extended Kalman filtering and open-circuit voltage calibration, accurately calculates the chargeable and dischargeable power by considering battery aging and temperature factors, ensuring the safety and continuity of energy storage frequency regulation. Meanwhile, by decomposing the different time-scale components of the frequency deviation and adopting differentiated optimization solution strategies, the fine allocation of frequency regulation tasks was achieved. Combined with targeted execution control strategies, such as torsional vibration damping control of wind turbines, stable operating point switching algorithm of photovoltaics, and multi-time-scale power allocation architecture of energy storage, the fluctuations in equipment operation were further suppressed, component wear was reduced, and equipment service life was extended. While improving the frequency regulation effect, the system operating cost was reduced, realizing the precision, safety and economy of multi-source coupled frequency regulation of new energy power plants. Detailed Implementation

[0017] The present invention will now be described in detail with reference to embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in the embodiments of this application can be combined with each other.

[0018] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. The terminology used in this invention is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.

[0019] Example 1 This embodiment provides a multi-source coupled active frequency regulation method for new energy power plants. By integrating the frequency regulation resources of wind turbine units, photovoltaic units, and energy storage systems, and based on accurate state assessment and multi-objective optimization allocation strategies, it achieves rapid and stable support for the grid frequency. The specific implementation process is as follows: The first step involves monitoring the power grid frequency and generating a frequency deviation signal. A high-precision synchronous phasor measurement unit (TPMU) is used as the core device for frequency acquisition. This device has GPS synchronization capabilities, enabling time synchronization across multiple measurement points and ensuring the accuracy of frequency measurements. The device acquires three-phase voltage signals from the power grid side through voltage transformers. The sampling frequency is set appropriately based on the power grid frequency fluctuation characteristics to fully capture millisecond-level frequency changes. The acquired raw voltage signals undergo filtering. A Kalman filter algorithm is used to suppress interference factors such as power grid harmonics and measurement noise. This algorithm effectively improves the signal-to-noise ratio by establishing the state equation and observation equation of the signal and updating the optimal estimate in real time. After filtering, a frequency estimation algorithm based on Fourier transform is used to extract the fundamental frequency component from the voltage signal to obtain the measured power grid frequency. The difference between the measured power grid frequency and the rated power grid frequency is calculated to generate the frequency deviation signal. To prevent transient interference from causing false triggering of the frequency modulation system, a signal anti-jitter mechanism is implemented. When the absolute value of the frequency deviation signal continuously exceeds a preset threshold for a set duration, it is considered a valid signal and transmitted to subsequent processing stages. If these conditions are not met, it is considered transient interference, and the frequency modulation process is not initiated. Simultaneously, the measured grid frequency, frequency deviation signal, and signal trigger time are stored in real-time using industrial-grade solid-state drives. The storage period is set according to operational requirements, providing data support for subsequent frequency modulation effect analysis and system optimization.

[0020] Next, multi-source operational status data acquisition and frequency regulation capability assessment are performed. Upon receiving a valid frequency deviation signal, the multi-source data acquisition process is initiated. Communication connections are established with the local controllers of the wind turbine, photovoltaic unit, and energy storage system through standard industrial communication interfaces to collect real-time operational status data of various devices. For the wind turbine, the collected parameters cover parameters related to rotor kinetic energy reserves, such as high-speed shaft speed, blade pitch angle, generator torque, current wind speed, turbine output power, and nacelle temperature. The acquisition cycle is set according to the dynamic response characteristics of the wind turbine to ensure real-time reflection of changes in the wind turbine's operating status. For the photovoltaic unit, parameters such as DC-side voltage, DC-side current, irradiance, ambient temperature, inverter module temperature, and photovoltaic module operating status are collected. The acquisition cycle is adapted to the response speed of photovoltaic power changes. For the energy storage system, parameters such as terminal voltage, charging and discharging current, ambient temperature, state of charge, battery cell voltage, and charging and discharging circuit temperature are collected. The acquisition cycle is set according to the charging and discharging dynamic characteristics of the energy storage system to ensure the real-time nature of the state of charge estimation. The collected data must undergo validity verification. Abnormal data is removed through methods such as threshold judgment and trend analysis to ensure data reliability.

[0021] To assess the kinetic energy reserve of the wind turbine rotor, a three-mass transmission chain model is first constructed. This model is based on the mechanical structural parameters of the wind turbine, including the rotational inertia of the low-speed shaft, the rotational inertia of the high-speed shaft, the gearbox transmission ratio, the gearbox damping coefficient, the stiffness coefficient of the low-speed shaft, and the stiffness coefficient of the high-speed shaft. These parameters are obtained from the wind turbine's factory technical documents or calibrated through on-site testing. Data such as the collected high-speed shaft speed, blade pitch angle, and generator torque are input into the model. By solving the dynamic differential equations, the specific kinetic energy value currently stored in the wind turbine rotor is calculated. The Runge-Kutta algorithm is used to solve the dynamic equations to ensure computational accuracy and efficiency. Simultaneously, combined with current wind speed data, an aerodynamic power calculation model is constructed based on Betz theory, considering factors such as the airfoil parameters and installation angle of the wind turbine blades, to calculate the maximum aerodynamic capture power of the wind turbine. Based on the generator's thermal capacity characteristics, combined with parameters such as rated power, heat dissipation coefficient, and allowable maximum temperature, the upper limit of rotor kinetic energy frequency regulation power is determined. Based on the gearbox design parameters, including rated torque, fatigue strength, and service life, the upper limit of rotor kinetic energy frequency regulation duration is determined. Using specific kinetic energy values, maximum aerodynamic capture power, upper power limit, and upper duration limit as constraints, a multi-objective optimization algorithm is employed to solve the polyhedral description of the feasible region for kinetic energy frequency regulation. This algorithm transforms each constraint into inequality equations, constructs the boundary of the feasible region, and ultimately outputs the specific rotor kinetic energy frequency regulation capacity that the wind turbine unit can safely release or absorb under current operating conditions, ensuring that the wind turbine does not stall, overload, or suffer mechanical damage during the frequency regulation process.

[0022] In the evaluation of the power regulation margin of photovoltaic (PV) units, a single-diode equivalent circuit model is first constructed. This model includes core parameters such as series resistance, parallel resistance, and diode ideality factor. Parameter identification employs a combination of offline calibration and online correction. In the offline phase, the PV array is tested under different irradiance and temperature conditions to acquire multiple sets of voltage-current data. The least squares method is used for preliminary calibration of the model parameters. In the online phase, the collected DC-side voltage, current, irradiance, and ambient temperature data are used to correct the model parameters in real time using a recursive least squares method to adapt to the impact of environmental changes on PV characteristics. The corrected model is then applied to the current operating state to calculate the maximum output power corresponding to the current maximum power point. A numerical differentiation method is used to solve for the derivative of the PV characteristic curve of the PV array at the current operating point. Specifically, a small voltage increment is selected near the current operating point, and the corresponding power change is calculated to obtain the rate of change of power with voltage. By combining the inverter's rated capacity, maximum input voltage range, maximum output current, and other constraints, as well as the allowable fluctuation range of the grid connection point voltage, a feasible range for power regulation is established. The specific power regulation capabilities of the photovoltaic unit in both upward and downward frequency regulation directions are clarified, and the corresponding voltage operating range is determined to ensure the safe and stable operation of the photovoltaic unit and the inverter.

[0023] The assessment of the state of charge (SOC) and charge / dischargeable power of the energy storage system employs a dual-state estimation method combining the extended Kalman filter (EPF) algorithm and open-circuit voltage calibration. First, a second-order equivalent circuit model of the energy storage system is established, incorporating parameters such as ohmic internal resistance, polarization resistance, and polarization capacitance, reflecting the dynamic voltage-current characteristics of the energy storage battery. The EPF algorithm processes the collected terminal voltage, charge / discharge current, and ambient temperature data. Through iterative calculations in prediction and update steps, the algorithm estimates the SOC value of the energy storage system in real time, while simultaneously estimating changes in model parameters online to adapt to characteristic drift caused by battery aging and temperature variations. To further improve the accuracy of SOC estimation, during the energy storage system's resting period (i.e., after the charge / discharge current has been zero for a set duration), the open-circuit voltage calibration process is initiated. The open-circuit voltage of the battery is measured using a high-precision voltage sensor, and the SOC value estimated by the EPF algorithm is periodically calibrated based on a preset open-circuit voltage-SOC relationship curve. Based on the calibrated state of charge (SOC) values, combined with the battery aging model and real-time temperature data from the thermal management system, an optimal power capacity calculation framework under multiple constraints is constructed. These constraints include upper and lower SOC limits, charge / discharge rate limits, temperature limits, and battery cycle life constraints. With minimizing battery life loss as the optimization objective, a convex optimization algorithm is used to solve the optimization problem, determining the specific maximum sustainable charging power and maximum sustainable discharging power of the energy storage system under the current SOC and temperature conditions. Furthermore, based on the difference between the current SOC and the upper and lower SOC limits, the maximum charge / discharge power, and the battery capacity, the corresponding power regulation duration is calculated.

[0024] After completing the multi-source frequency modulation capability assessment, frequency modulation tasks are dynamically allocated based on the frequency deviation signal and multi-source frequency modulation capability data. First, a multi-objective optimization function is constructed, which includes a frequency deviation integral term, a frequency modulation cost function, and an equipment stress penalty term. The frequency deviation integral term measures the frequency modulation effect, using a time-weighted integral form to assign different weights to frequency deviations in different time periods, emphasizing the suppression of severe frequency deviations. The frequency modulation cost function includes wind turbine frequency modulation costs, photovoltaic frequency modulation costs, and energy storage frequency modulation costs. The wind turbine frequency modulation cost considers factors such as rotor kinetic energy consumption and equipment wear; the photovoltaic frequency modulation cost considers factors such as power loss and conversion efficiency degradation; and the energy storage frequency modulation cost considers factors such as charging and discharging losses and capacity decay. The equipment stress penalty term limits excessive equipment adjustment, setting a penalty coefficient based on the equipment's fatigue strength characteristics to prevent the equipment's service life from being shortened due to frequent and drastic adjustments.

[0025] A wavelet packet decomposition algorithm is used to decompose the frequency deviation signal into rapidly changing frequency fluctuation components and slowly changing frequency trend components. During the decomposition process, appropriate wavelet basis functions and decomposition levels are selected based on the characteristics of the frequency signal to ensure effective separation of components at different time scales. For the rapidly changing frequency fluctuation components, an allocation subproblem with response speed as the primary optimization objective is established and solved online using a model predictive control framework. Reasonable prediction and control time domains are set, and the kinetic energy reserve of the wind turbine rotor and the rechargeable / dischargeable power of the energy storage are used as constraints. Through rolling optimization, fast power allocation instructions for wind turbine units and energy storage systems are generated. The instructions clearly specify the power adjustment amplitude, adjustment rate, and response time requirements to ensure rapid suppression of frequency fluctuations. For the slowly changing frequency trend components, an allocation subproblem with economic efficiency as the primary optimization objective is established and solved using a mixed integer programming method. The photovoltaic power regulation margin and the rechargeable / dischargeable power of the energy storage are used as constraints. Factors such as frequency regulation costs and equipment operating losses are considered to generate slow power allocation instructions for photovoltaic units and energy storage systems. The instructions clearly specify the power adjustment amplitude, adjustment duration, and economic optimization objectives.

[0026] The two sets of power allocation commands are integrated through a two-layer coordination mechanism. The upper-layer coordinator receives real-time grid frequency characteristic data, including frequency change rate, frequency deviation amplitude, and frequency fluctuation period. It adaptively adjusts the weight coefficients of the fast and slow allocation sub-problems using a fuzzy control algorithm. When grid frequency fluctuations are severe, the weight coefficient of the fast allocation sub-problem is increased to prioritize frequency regulation response speed; when the grid frequency tends to stabilize, the weight coefficient of the slow allocation sub-problem is increased to prioritize operational economy. The lower-layer actuator, based on the adjusted weight coefficients, weights and fuses the fast and slow power allocation commands to form the final multi-source coordinated frequency regulation command. Simultaneously, it monitors the actual response of each frequency regulation resource in real time, collecting feedback data such as actual wind turbine output power, actual photovoltaic regulation, and actual energy storage charging and discharging power. It calculates the response deviation and feeds it back to the upper-layer coordinator, forming a closed-loop optimization. Based on the magnitude and trend of the deviation, it adjusts the weight coefficients and power allocation commands to ensure that the frequency regulation effect continuously meets grid requirements.

[0027] Finally, a multi-source coordinated frequency modulation command is executed to control all equipment to collaboratively provide frequency support. For the wind turbine unit, a nonlinear control strategy based on feedback linearization is adopted to adjust the output power and rotor speed. First, a full-order mathematical model of the wind turbine, including aerodynamic characteristics, transmission chain flexibility, and generator dynamics, is established. The model parameters are determined through the wind turbine's factory technical documents and field test data. A differential homeomorphic transformation is performed on the full-order mathematical model of the wind turbine to convert the nonlinear model into the Brunovsky canonical form, simplifying the control law design. Based on the frequency modulation power command, the virtual control quantity in the transformed system is calculated using the inverse system method. Then, through nonlinear state feedback precise linearization, the original nonlinear system is decomposed into two decoupled linear subsystems: power regulation and speed regulation. A proportional-integral-derivative controller is designed for each linear subsystem. The controller performance is optimized through parameter tuning. The power channel controller is used to accurately track the power command, and the speed channel controller is used to stabilize the rotor speed. During frequency regulation, torsional vibration signals are collected by vibration sensors installed at both ends of the gearbox. An adaptive filtering algorithm is used to extract the torsional vibration frequency components, and a torsional vibration damping control loop is introduced based on the magnitude of the torsional vibration amplitude. By adjusting the generator torque, mechanical vibration is actively suppressed, ensuring the safety of the transmission chain. When a drastic change in wind speed or a risk of exceeding the speed limit is detected, a robust control-based protection strategy is activated. This strategy limits the rotor speed by adjusting the blade pitch angle and appropriately reduces the power regulation accuracy, ensuring the structural safety of the wind turbine while maintaining the basic frequency regulation effect.

[0028] For photovoltaic (PV) units, a two-layer operating point localization algorithm combining binary search and gradient descent is used to control the operating point's deviation from the maximum power point. In the inner loop, gradient descent is used for a local fine-grained search near the current voltage reference value. The direction of the fastest power change is determined by calculating the partial derivative of power with respect to voltage, and the step size is dynamically adjusted based on the power change rate to ensure search accuracy. In the outer loop, a binary search algorithm is used for a global coarse search across the entire PV array's operating voltage range to quickly locate the target power region and reduce search time. To avoid power surges during operating point switching, a dynamic optimization strategy for operating point switching is established. Upon receiving the frequency modulation command, based on the parameters of the current and target operating points, combined with the inverter's DC-side voltage change rate limit, the optimal switching trajectory is calculated using a dynamic programming algorithm, with the goal of minimizing energy loss during switching. Subsequently, by adjusting the voltage control loop reference value of the PV inverter in real time, the PV unit smoothly transitions to the target operating point along the optimal switching trajectory. During the power regulation phase, a sliding mode variable structure control strategy is adopted. The preset sliding surface is a linear combination of power deviation and integral term. By switching the control law, the system state variables are forced to move on the preset sliding surface, which effectively suppresses power fluctuations caused by sudden changes in illumination and improves the stability of power regulation.

[0029] For energy storage systems, a multi-timescale power allocation architecture based on fractional calculus is used to control charging and discharging power regulation. A fractional-order equivalent circuit model of the battery considering diffusion dynamics and electrochemical polarization is established, which can more accurately describe the dynamic characteristics of the battery at different time scales. A low-pass filter is used to decompose the frequency modulation power command into millisecond-level inertial response components, second-level primary frequency modulation components, and minute-level secondary frequency modulation components, and the filter parameters are set according to the time constant of each component. For the millisecond-level power component, a fractional-order proportional-integral-derivative controller is used, utilizing the memory characteristics and flexible parameter tuning capabilities of fractional-order operators to improve the tracking accuracy of fast power commands. For the second-level power component, a power divider based on model predictive control is designed, a reasonable prediction time domain is set, and the frequency modulation demand and energy storage system losses are balanced by solving an optimization problem in the finite time domain. For the minute-level power component, an optimization scheduling framework aimed at extending battery life is constructed, comprehensively considering the state-of-charge recovery demand and frequency modulation market benefits, and a dynamic optimization algorithm is used to generate scheduling commands. A multi-timescale coordination mechanism is established, and the consistency of control objectives at each level is ensured through the Lyapunov optimization method to avoid conflicts between control strategies at different timescales. Ultimately, a specific power control signal that satisfies multiple constraints is generated to drive the energy storage converter to perform precise charging and discharging operations, ensuring that the energy storage system can respond to frequency regulation requirements while extending its service life and reducing operating losses.

[0030] Example 2 This embodiment provides a multi-source coupled active frequency modulation system for new energy power plants, used to implement the active frequency modulation method described in Embodiment 1 above. The system adopts a modular design, has good scalability and compatibility, and can be adapted to new energy power plants of different scales and types. Specifically, it includes a frequency monitoring module, a multi-source capability assessment module, a task allocation module, and an execution control module. Each module achieves high-speed data interaction through industrial Ethernet and works together to complete the frequency modulation task.

[0031] The frequency monitoring module, as the core of the system's sensing capabilities, consists of a high-precision synchronous phasor measurement unit, a data preprocessing module, a communication interface module, and a storage module. The synchronous phasor measurement unit incorporates a GPS synchronization module, achieving microsecond-level time synchronization accuracy. It can acquire three-phase voltage and current signals from the power grid in real time, with the sampling frequency rationally set according to the power grid frequency monitoring requirements to ensure the capture of rapid fluctuations in the power grid frequency. The data preprocessing module is equipped with a dedicated digital signal processing chip, integrating Kalman filtering and frequency estimation algorithms. It filters and extracts frequencies from the acquired raw signals, generating power grid frequency measurements and frequency deviation signals. The communication interface module supports multiple industrial communication protocols, including ModbusTCP and IEC61850, enabling real-time transmission of frequency deviation signals to the multi-source capability assessment module and task allocation module. Data transmission latency is controlled within a set range to ensure real-time response to frequency modulation commands. The storage module uses an industrial-grade solid-state drive to store power grid frequency data, frequency deviation signals, and trigger times. The storage period is no less than a set duration, providing data support for subsequent system operation and maintenance and frequency modulation effect analysis. In addition, the frequency monitoring module also has a self-diagnostic function, which can monitor its own hardware status and the validity of measurement data in real time, and issue an alarm signal in a timely manner when a fault occurs.

[0032] The multi-source capability assessment module is responsible for collecting and evaluating the real-time frequency regulation capabilities of various frequency regulation resources. Its hardware configuration includes a data acquisition terminal, edge computing nodes, storage units, and a communication module. The data acquisition terminal establishes communication connections with the local controllers of wind turbines, photovoltaic units, and energy storage systems via standard industrial interfaces, including Ethernet and RS485 interfaces, to adapt to the communication needs of different devices. The data acquisition terminal collects operational status data from the wind turbines, photovoltaic units, and energy storage systems according to a preset acquisition cycle. The acquisition cycle is set separately based on the dynamic response characteristics of each device to ensure the real-time nature and effectiveness of the data acquisition. The collected multi-source data is transmitted to the edge computing nodes via the communication module. The edge computing nodes are equipped with multi-core processors and high-performance graphics cards, possessing powerful parallel computing and data processing capabilities, enabling them to simultaneously complete the capability assessment of the three types of frequency regulation resources.

[0033] Edge computing nodes incorporate various evaluation models and algorithms, including wind turbine rotor kinetic energy reserve evaluation models, photovoltaic power regulation margin evaluation models, and energy storage system state evaluation models. These models and algorithms are integrated into the edge computing nodes through software programming. In the wind turbine rotor kinetic energy reserve evaluation process, the edge computing node calls a three-mass block transmission chain model, inputs collected wind turbine operating data, solves the kinetic energy value of the rotor through kinetic equations, combines the aerodynamic power calculation model and equipment constraints, and uses a multi-objective optimization algorithm to solve for the kinetic energy frequency regulation feasible region, outputting the wind turbine rotor kinetic energy frequency regulation capacity. In the photovoltaic power regulation margin evaluation process, a single-diode equivalent circuit model is called, and the maximum output power and power change rate are calculated using online corrected parameters. Combined with inverter and grid constraints, a feasible power regulation range is established. In the energy storage system state evaluation process, an extended Kalman filter algorithm and an open-circuit voltage calibration program are run to accurately estimate the state of charge value. Combined with a battery aging model and temperature data, a convex optimization algorithm is used to determine the maximum sustainable charge / discharge power and regulation duration. The storage unit is used to store the collected operating status data and evaluation results. The communication module sends the evaluation results to the task allocation module and receives control commands from the task allocation module.

[0034] The task allocation module is the core of the system's decision-making process. It consists of a frequency decomposition unit, an optimization and solution unit, a coordination and control unit, and a communication interface unit. The hardware is built on an industrial-grade server and runs a real-time operating system to ensure rapid generation and transmission of decision commands. The frequency decomposition unit receives frequency deviation signals from the frequency monitoring module and runs a wavelet packet decomposition algorithm to decompose the frequency deviation signal into rapidly changing frequency fluctuation components and slowly changing frequency trend components. The decomposed components are transmitted to the optimization and solution unit via an internal bus. The optimization and solution unit is equipped with a multi-core processor and a dedicated mathematical computing chip, integrating model predictive control algorithms and mixed-integer programming algorithms. For the rapidly changing frequency fluctuation components, it establishes an allocation subproblem with response speed as the primary objective, sets the prediction time domain and control time domain, and generates fast power allocation commands through rolling optimization. For the slowly changing frequency trend components, it establishes an allocation subproblem with economy as the primary objective, considers various constraints, and generates slow power allocation commands through a mixed-integer programming algorithm.

[0035] The coordination and control unit incorporates a two-layer coordination mechanism and a closed-loop optimization program. The upper-layer coordinator receives real-time grid frequency characteristic data from the frequency monitoring module and equipment response data from the execution control module, and adaptively adjusts the weight coefficients of the fast and slow allocation sub-problems using a fuzzy control algorithm. The lower-layer actuator, based on the adjusted weight coefficients, weights and fuses the fast and slow power allocation commands to form the final multi-source coordinated frequency regulation command. The closed-loop optimization program adjusts the weight coefficients and power allocation commands in real-time based on equipment response deviations to ensure that the frequency regulation effect meets grid requirements. The communication interface unit supports high-speed communication with the multi-source capability assessment module and the execution control module, sending the multi-source coordinated frequency regulation command to the execution control module while simultaneously receiving multi-source frequency regulation capability data from the multi-source capability assessment module and equipment response data from the execution control module.

[0036] The execution control module, as the core of the system, consists of a wind turbine control unit, a photovoltaic control unit, an energy storage control unit, and a feedback acquisition unit. Each control unit establishes a communication connection with its corresponding equipment controller via an industrial bus. The communication protocol supports the rapid transmission of real-time control commands and real-time feedback of equipment status. The wind turbine control unit receives the wind turbine regulation command from the multi-source coordinated frequency modulation command and runs a nonlinear control program based on feedback linearization. This includes functions such as constructing a full-order mathematical model of the wind turbine, differential homeomorphic transformation, virtual control quantity calculation, decoupling of linear subsystems, and PID controller parameter tuning. It also integrates a torsional vibration damping control algorithm and a robust protection program. By adjusting the generator torque and pitch angle, it controls the output power and rotor speed of the wind turbine unit. The photovoltaic control unit receives the photovoltaic regulation command and runs a dual-layer operating point positioning algorithm, an operating point switching dynamic optimization program, and a sliding mode variable structure control program. By adjusting the voltage control loop reference value of the photovoltaic inverter, it controls the photovoltaic unit's operating point to deviate from the maximum power point, thereby achieving power regulation. The energy storage control unit receives energy storage regulation commands, runs a fractional-order equivalent circuit model, a power component decomposition program, and a multi-time-scale controller, and drives the energy storage converter to perform charging and discharging operations by generating precise power control signals. The feedback acquisition unit collects feedback data such as the actual output power and operating status of the wind turbine, photovoltaic unit, and energy storage system in real time, and transmits it to the task allocation module to provide data support for closed-loop optimization.

[0037] The system's modules interact via industrial Ethernet, employing standardized data formats and communication protocols to ensure reliable and compatible data transmission. The system boasts excellent scalability, supporting the flexible integration of new wind turbine units, photovoltaic units, and energy storage systems. Different types of equipment can be adapted simply by modifying configuration files. Furthermore, the system features self-diagnostic and fault-tolerant capabilities. When a module fails, it automatically switches to a backup module or adopts a degraded operation strategy to ensure continuous and stable system operation. In practical applications, the system can flexibly adjust the hardware configuration and software parameters of each module according to the scale of the new energy power plant and the grid frequency regulation requirements, achieving an optimal balance between frequency regulation performance, equipment safety, and operational economy.

[0038] As is known from common technical knowledge, this invention can be implemented through other embodiments that do not depart from its spirit or essential characteristics. Therefore, the disclosed embodiments described above are merely illustrative in all respects and are not the only ones. All modifications within the scope of this invention or its equivalents are included in this invention.

Claims

1. A multi-source coupling active frequency modulation method for new energy power plants, characterized in that, Includes the following steps: The system monitors the grid frequency in real time, acquires the measured grid frequency value, and calculates the difference between the measured grid frequency value and the rated frequency to obtain a frequency deviation signal. Based on the frequency deviation signal, it simultaneously collects real-time operating status data of wind turbine units, photovoltaic units, and energy storage systems in the new energy power plant, and evaluates the rotor kinetic energy reserve of the wind turbine unit, the power regulation margin of the photovoltaic unit, and the state of charge and charge / discharge power of the energy storage system to obtain multi-source frequency regulation capability data. Based on the frequency deviation signal and the multi-source frequency regulation capability data, it dynamically allocates the frequency regulation tasks of the wind turbine unit, photovoltaic unit, and energy storage system according to a preset multi-objective optimization function, and generates multi-source coordinated frequency regulation commands. The multi-objective optimization function simultaneously considers frequency regulation response speed, frequency regulation capacity, and system operation stability. It executes the multi-source coordinated frequency regulation commands to control the wind turbine unit to adjust its output power and rotor speed, control the photovoltaic unit to adjust its operating point from the maximum power point, and control the energy storage system to regulate its charge and discharge power, jointly providing rapid frequency support.

2. The multi-source coupling active frequency modulation method for new energy power plants according to claim 1, characterized in that, The steps for assessing the rotor kinetic energy reserve of a wind turbine unit specifically include: A three-mass drivetrain model of the wind turbine unit is established, which includes the dynamic characteristics of the low-speed shaft, gearbox, and high-speed shaft. Real-time data collection is performed on the high-speed shaft speed, blade pitch angle, and generator torque of the wind turbine unit to obtain the first set of real-time data. This first set of real-time data is input into the three-mass drivetrain model to calculate the specific kinetic energy value currently stored in the wind turbine rotor. Simultaneously, based on the aerodynamic characteristics of the wind turbine blades and the current wind speed data, the maximum aerodynamic capture power of the wind turbine is calculated. According to the generator's thermal capacity constraint and the gearbox's torque limit, the upper limit of the rotor kinetic energy frequency modulation power and the upper limit of the duration are determined. Combining the specific kinetic energy value, the maximum aerodynamic capture power, the upper limit of the power, and the upper limit of the duration, the specific rotor kinetic energy frequency modulation capacity that the wind turbine unit can safely release or absorb under the current operating conditions is obtained by solving the polyhedral description of the feasible region for kinetic energy frequency modulation.

3. The multi-source coupling active frequency modulation method for new energy power plants according to claim 1, characterized in that, The steps for assessing the power regulation margin of a photovoltaic unit specifically include: A single-diode equivalent circuit model of the photovoltaic array is constructed, which includes series resistance, parallel resistance, and diode ideality factor parameters. Parameter identification is performed on the single-diode equivalent circuit model under different environmental conditions to obtain an accurate mathematical model of the photovoltaic unit. The DC-side voltage and current of the photovoltaic unit are monitored in real time, and combined with current irradiance and ambient temperature measurements to obtain a second set of real-time data. This second set of real-time data is input into the accurate mathematical model of the photovoltaic unit to calculate the maximum output power corresponding to the current maximum power point. The rate of change of power with voltage is accurately calculated by solving the derivative of the PV characteristic curve of the photovoltaic array at the current operating point. Based on the maximum output power and the rate of change of power with voltage, a feasible range for power regulation considering inverter capacity constraints and grid voltage requirements is established. This feasible range clearly defines the specific power regulation capabilities of the photovoltaic unit in both upward and downward frequency regulation directions, as well as the corresponding voltage operating range.

4. The multi-source coupling active frequency modulation method for a new energy power plant according to claim 1, characterized in that, The steps for assessing the state of charge and charge / discharge capacity of an energy storage system include: A dual-state estimation method combining the extended Kalman filter algorithm and open-circuit voltage calibration is employed. The specific execution process includes: establishing a second-order equivalent circuit model of the energy storage system, which includes ohmic internal resistance, polarization resistance, and polarization capacitance parameters; real-time acquisition of the energy storage system's terminal voltage, current, and ambient temperature to obtain a third set of real-time data; processing the third set of real-time data using the extended Kalman filter algorithm to estimate the energy storage system's state of charge (SOC) value in real time, while simultaneously estimating changes in model parameters; measuring the open-circuit voltage during the energy storage system's resting period to periodically calibrate the SOC value estimated by the extended Kalman filter algorithm; based on the calibrated SOC value, combined with the battery aging model and real-time temperature data from the thermal management system, constructing an optimal power capacity calculation framework under multiple constraints; and determining the maximum sustainable charging power and maximum sustainable discharging power of the energy storage system under the current SOC and temperature conditions by solving an optimization problem aimed at minimizing battery life loss, and calculating the corresponding power regulation duration.

5. The multi-source coupling active frequency modulation method for new energy power plants according to claim 1, characterized in that, The steps for dynamically allocating frequency modulation tasks according to a preset multi-objective optimization function specifically include: A multi-objective optimization function is constructed, comprising a frequency deviation integral, a frequency regulation cost function, and a device stress penalty term. The frequency deviation signal is decomposed into frequency components at different time scales, including rapidly changing frequency fluctuation components and slowly changing frequency trend components. For the rapidly changing frequency fluctuation components, an allocation subproblem with response speed as the primary optimization objective is established and solved online using a model predictive control framework to generate fast power allocation instructions for wind turbine units and energy storage systems. For the slowly changing frequency trend components, an allocation subproblem with economy as the primary optimization objective is established and solved using a mixed integer programming method to generate slow power allocation instructions for photovoltaic units and energy storage systems. A two-layer coordination mechanism is designed, in which the upper-layer coordinator adaptively adjusts the weight coefficients of the fast and slow allocation subproblems according to real-time frequency characteristics, and the lower-layer actuators execute the corresponding power allocation instructions respectively. By monitoring the actual response of each frequency regulation resource in real time, feedback is sent to the upper-layer coordinator to form a closed-loop optimization, ultimately outputting a specific power allocation scheme that considers multiple time scales and resource characteristics.

6. The multi-source coupling active frequency modulation method for a new energy power plant according to claim 1, characterized in that, The steps for controlling the fan unit to adjust its output power and rotor speed specifically include: The design of a nonlinear controller based on feedback linearization includes: establishing a full-order mathematical model of the wind turbine that incorporates aerodynamic characteristics, transmission chain flexibility, and generator dynamics; performing a differential homeomorphic transformation on the full-order mathematical model of the wind turbine to convert it into the Brunovsky canonical form; calculating the virtual control quantity in the converted system based on the frequency modulation power command; decomposing the original nonlinear system into multiple decoupled linear subsystems through precise linearization using nonlinear state feedback; designing a proportional-integral-derivative controller for each linear subsystem, used for tracking the power command and regulating the rotor speed, respectively; monitoring the torsional vibration mode of the transmission chain in real time during frequency modulation, and actively suppressing mechanical vibration caused by rapid power adjustment by introducing a torsional vibration damping control loop; and activating a robust control-based protection strategy when a drastic change in wind speed or a risk of exceeding the speed limit is detected, ensuring the structural safety of the wind turbine while maintaining the frequency modulation effect.

7. The multi-source coupling active frequency modulation method for a new energy power plant according to claim 1, characterized in that, The step of controlling the photovoltaic unit to adjust its operating point from the maximum power point specifically includes: A two-layer operating point localization algorithm based on a combination of binary search and gradient descent is proposed. The specific execution process includes: in the inner loop, a gradient descent method is used to perform a local fine search near the current voltage reference value, determining the direction of the fastest power change by calculating the partial derivative of power with respect to voltage; in the outer loop, a binary search algorithm is used to perform a global coarse search within the entire photovoltaic array's operating voltage range to quickly locate the target power region; a dynamic optimization strategy for operating point switching is established. Upon receiving a frequency modulation command, the optimal switching trajectory from the current operating point to the target operating point is first calculated. This trajectory is constrained by the inverter's DC-side voltage change rate limit, with the optimization objective being minimizing energy loss during the switching process; by adjusting the photovoltaic inverter's voltage control loop reference value in real time, the photovoltaic unit smoothly transitions to the target operating point along the optimal switching trajectory; in the power regulation stage, a sliding mode variable structure control strategy is adopted to force the system state variables to move on a preset sliding surface, suppressing power fluctuations caused by sudden changes in illumination.

8. The multi-source coupling active frequency modulation method for a new energy power plant according to claim 1, characterized in that, The steps for controlling the charging and discharging power regulation of an energy storage system specifically include: The design incorporates a multi-timescale power allocation architecture based on fractional calculus, specifically including: establishing a fractional-order equivalent circuit model of the battery considering diffusion dynamics and electrochemical polarization; decomposing the frequency modulation power command into power components with different time constants, including millisecond-level inertial response components, second-level primary frequency modulation components, and minute-level secondary frequency modulation components; for the millisecond-level power components, employing a fractional-order proportional-integral-derivative controller, utilizing the memory characteristics of fractional-order operators to improve the tracking accuracy of fast power commands; for the second-level power components, designing a power divider based on model predictive control, balancing frequency modulation requirements with energy storage system losses by solving a finite-time domain optimization problem; for the minute-level power components, constructing an optimized scheduling framework aimed at extending battery life, comprehensively considering state-of-charge recovery requirements and frequency modulation market benefits; establishing a multi-timescale coordination mechanism, ensuring the consistency of control objectives at each level through Lyapunov optimization methods, and ultimately generating specific power control signals that satisfy multiple constraints to drive the energy storage converter to perform precise charging and discharging operations.

9. A multi-source coupled active frequency regulation system for a new energy power plant, based on the multi-source coupled active frequency regulation method for a new energy power plant according to any one of claims 1 to 8, characterized in that, include: Frequency monitoring module, multi-source capability assessment module, task allocation module, and execution control module; The frequency monitoring module is configured to monitor the power grid frequency in real time, obtain the power grid frequency measurement value, calculate the difference between the power grid frequency measurement value and the rated frequency to obtain a frequency deviation signal, and send the frequency deviation signal to the multi-source capability assessment module and the task allocation module. The multi-source capability assessment module is configured to simultaneously collect real-time operating status data of wind turbine units, photovoltaic units, and energy storage systems in the new energy power station based on the received frequency deviation signal, assess the rotor kinetic energy reserve of the wind turbine unit, the power regulation margin of the photovoltaic unit, and the state of charge and chargeable / dischargeable power of the energy storage system, respectively, obtain multi-source frequency regulation capability data, and send the multi-source frequency regulation capability data to the task allocation module; The task allocation module is configured to dynamically allocate frequency regulation tasks of the wind turbine, the photovoltaic unit, and the energy storage system based on the received frequency deviation signal and the multi-source frequency regulation capability data, according to a preset multi-objective optimization function, generate multi-source coordinated frequency regulation instructions, and send the multi-source coordinated frequency regulation instructions to the execution control module. The execution control module is configured to execute the received multi-source coordinated frequency modulation command, control the wind turbine to adjust its output power and rotor speed, control the photovoltaic unit to adjust its operating point to deviate from the maximum power point, and control the energy storage system to adjust its charging and discharging power, so as to jointly provide rapid frequency support.

10. A multi-source coupled active frequency regulation system for a new energy power plant according to claim 9, characterized in that, The task allocation module includes: a frequency decomposition unit, an optimization solution unit, and a coordination and control unit; The frequency decomposition unit is configured to decompose the received frequency deviation signal into frequency components at different time scales, including rapidly changing frequency fluctuation components and slowly changing frequency trend components, and send the decomposed rapidly changing frequency fluctuation components and slowly changing frequency trend components to the optimization solution unit. The optimization solution unit is configured to establish an allocation subproblem with response speed as the primary optimization objective for the rapidly changing frequency fluctuation component, solve it online using a model predictive control framework, and generate fast power allocation instructions for wind turbine units and energy storage systems. Simultaneously, for the slowly changing frequency trend component, it establishes an allocation subproblem with economy as the primary optimization objective, solves it using a mixed integer programming method, generates slow power allocation instructions for photovoltaic units and energy storage systems, and sends the fast power allocation instructions and the slow power allocation instructions to the coordination control unit. The coordination and control unit is configured to adaptively adjust the weight coefficients of the fast allocation subproblem and the slow allocation subproblem according to the real-time frequency characteristics, integrate the fast power allocation command and the slow power allocation command through a two-layer coordination mechanism to form the final multi-source coordinated frequency modulation command, and send the multi-source coordinated frequency modulation command to the execution control module.