CN116581780A - Primary frequency modulation characteristic modeling and control method for wind-storage combined system - Google Patents
Primary frequency modulation characteristic modeling and control method for wind-storage combined system Download PDFInfo
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- H—ELECTRICITY
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
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Abstract
The invention provides a primary frequency modulation characteristic modeling and control method of a wind-storage combined system, which comprises the following steps: establishing a detailed frequency modulation model of the wind turbine generator through mechanism analysis, and acquiring frequency modulation data of the wind turbine generator under different working conditions; establishing finite difference regression vectors under different frequency modulation working conditions, and carrying out parameter identification on each finite difference regression vector by utilizing wind turbine generator frequency modulation data under different working conditions; carrying out high-dimensional clustering on the finite difference regression vectors of different working conditions to divide the working condition domain; establishing a function model of the wind-storage combined system participating in primary frequency modulation of the power grid according to the finite difference regression vector representing the charge-discharge characteristics of the energy storage; and establishing an optimization model by taking the frequency modulation cost of the wind power and the energy storage device in unit time in a prediction time domain as a target when frequency fluctuation occurs, and solving the prediction model according to an optimization solver. The invention can improve the system stability under the premise of considering the economic cost of frequency modulation.
Description
Technical Field
The invention relates to the technical field of wind turbine generator control, in particular to a primary frequency modulation characteristic modeling and control method of a wind storage combined system.
Background
At present, the novel power system is mainly characterized by realizing high-proportion access of new energy, and integrating information technology and energy supply depth. However, the new energy access brings serious challenges to the safe and economical operation of the power grid while realizing low carbon. The grid-connected main body at the power generation side has the capabilities of participating in frequency modulation, peak regulation and standby of the power system. Wind power generation has an increasing proportion in energy sources, and frequency modulation strategies and control methods thereof are also receiving more and more attention.
With the continuous improvement of the permeability of wind power, the interaction mechanism between the power system and the wind power plant is more and more complex, the large-scale investment of wind power reduces the frequency modulation capability of the system, and the frequency stability of a power grid is greatly affected. In the wind power plant participating in frequency modulation, the power grid dispatching system and the wind power plant level frequency modulation controller calculate the active power increment required by the wind power plant participating in frequency modulation according to the sagging control and other methods, and the plant level control system completes active power distribution according to the running state of the wind turbine generator. However, the number of the units in the wind power plant is large, the running state of the fans cannot be guaranteed to be consistent, the wind power units or the output is insufficient easily, or the output capacity is virtually thrown, finally the wind power plant cannot complete the active output task issued by the power grid, and even the unstable output of the wind power plant affects the active dispatching of the power grid. For this reason, how to effectively distribute the frequency modulated power in a wind farm is a major concern.
Besides the wind power plant directly participates in the frequency modulation of the power grid, the rapid development of the energy storage technology also provides a new solution for the frequency modulation, and the combination of energy storage and wind power is jointly involved in the primary frequency modulation of the power grid, so that the stability of the system can be improved, but the energy storage cost is higher at present, and the economic cost of the frequency modulation also needs to be considered when the wind power storage combined frequency modulation is carried out.
Therefore, a wind farm primary frequency modulation characteristic modeling and control method with participation of energy storage is provided.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention aims to provide a novel optical fiber.
In order to achieve the above object, the present invention provides the following solutions:
a primary frequency modulation characteristic modeling and control method of a wind-storage combined system comprises the following steps:
acquiring a frequency modulation control strategy of the wind turbine;
based on the wind turbine frequency modulation control strategy, a wind turbine frequency modulation detailed model is established through mechanism analysis, and different frequency modulation working conditions are generated through changing input wind speed and load so as to obtain wind turbine frequency modulation data under different working conditions;
establishing finite difference regression vectors under different frequency modulation working conditions according to actual output influence parameters of the wind turbine, and carrying out parameter identification on each finite difference regression vector by utilizing frequency modulation data of the wind turbine under different working conditions to obtain a linear model of frequency modulation transient characteristics and steady-state characteristics;
carrying out high-dimensional clustering on finite difference regression vectors of different working conditions to divide the working condition domains, classifying the wind turbines in the same working condition domain into one type so as to divide the wind turbines with similar frequency modulation characteristics;
establishing a function model of the wind-storage combined system participating in primary frequency modulation of the power grid according to a finite difference regression vector representing the charge-discharge characteristics of the energy storage, and predicting and controlling standard equation discretization according to the function model;
establishing an optimization model by taking frequency modulation cost of wind power and an energy storage device in unit time in a prediction time domain as a target when frequency fluctuation occurs, and establishing a prediction model according to the optimization model and preset constraint conditions;
and solving the prediction model according to an optimization solver to obtain an active output reference value of each wind turbine group participating in frequency modulation and an energy storage device power variation reference value.
Preferably, the wind turbine generator frequency modulation control strategy comprises: rotor kinetic energy control and power standby control; the rotor kinetic energy control comprises virtual inertia control, sagging control and integrated inertia control combined by the virtual inertia control and the sagging control, wherein power standby control comprises pitch control and overspeed control, and the power standby control is used for a load shedding operation mode of the wind turbine.
Preferably, the method for constructing the frequency modulation detailed model of the wind turbine comprises the following steps:
establishing a mathematical model of wind turbines, transmission systems, electric systems and rotor kinetic energy control of the wind turbine generator set, wherein the mathematical model participates in frequency modulation;
and constructing on a simulation platform according to the mathematical model to obtain the wind turbine frequency modulation detailed model.
Preferably, the finite difference regression vector includes an amount of power system frequency variation, an input wind speed, active power, a pitch angle, and a rotor speed.
Preferably, the preset constraint condition includes: the energy storage device power variation constraint, the energy storage device charge state constraint and the primary frequency modulation power balance condition constraint.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention provides a primary frequency modulation characteristic modeling and control method of a wind-storage combined system, which comprises the following steps: acquiring a frequency modulation control strategy of the wind turbine; based on the wind turbine frequency modulation control strategy, a wind turbine frequency modulation detailed model is established through mechanism analysis, and different frequency modulation working conditions are generated through changing input wind speed and load so as to obtain wind turbine frequency modulation data under different working conditions; establishing finite difference regression vectors under different frequency modulation working conditions according to actual output influence parameters of the wind turbine, and carrying out parameter identification on each finite difference regression vector by utilizing frequency modulation data of the wind turbine under different working conditions to obtain a linear model of frequency modulation transient characteristics and steady-state characteristics; carrying out high-dimensional clustering on finite difference regression vectors of different working conditions to divide the working condition domains, classifying the wind turbines in the same working condition domain into one type so as to divide the wind turbines with similar frequency modulation characteristics; establishing a function model of the wind-storage combined system participating in primary frequency modulation of the power grid according to a finite difference regression vector representing the charge-discharge characteristics of the energy storage, and predicting and controlling standard equation discretization according to the function model; establishing an optimization model by taking frequency modulation cost of wind power and an energy storage device in unit time in a prediction time domain as a target when frequency fluctuation occurs, and establishing a prediction model according to the optimization model and preset constraint conditions; and solving the prediction model according to an optimization solver to obtain an active output reference value of each wind turbine group participating in frequency modulation and an energy storage device power variation reference value. The invention can improve the system stability under the premise of considering the economic cost of frequency modulation.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a primary frequency modulation function model of an energy storage participation wind farm provided by an embodiment of the invention;
FIG. 3 is a schematic diagram of rotor inertia control according to an embodiment of the present invention;
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
Fig. 1 is a flowchart of a method provided by an embodiment of the present invention, and as shown in fig. 1, the present invention provides a method for modeling and controlling primary frequency modulation characteristics of a wind-storage combined system, including:
step 100: acquiring a frequency modulation control strategy of the wind turbine;
step 200: based on the wind turbine frequency modulation control strategy, a wind turbine frequency modulation detailed model is established through mechanism analysis, and different frequency modulation working conditions are generated through changing input wind speed and load so as to obtain wind turbine frequency modulation data under different working conditions;
step 300: establishing finite difference regression vectors under different frequency modulation working conditions according to actual output influence parameters of the wind turbine, and carrying out parameter identification on each finite difference regression vector by utilizing frequency modulation data of the wind turbine under different working conditions to obtain a linear model of frequency modulation transient characteristics and steady-state characteristics;
step 400: carrying out high-dimensional clustering on finite difference regression vectors of different working conditions to divide the working condition domains, classifying the wind turbines in the same working condition domain into one type so as to divide the wind turbines with similar frequency modulation characteristics;
step 500: establishing a function model of the wind-storage combined system participating in primary frequency modulation of the power grid according to a finite difference regression vector representing the charge-discharge characteristics of the energy storage, and predicting and controlling standard equation discretization according to the function model;
step 600: establishing an optimization model by taking frequency modulation cost of wind power and an energy storage device in unit time in a prediction time domain as a target when frequency fluctuation occurs, and establishing a prediction model according to the optimization model and preset constraint conditions;
step 700: and solving the prediction model according to an optimization solver to obtain an active output reference value of each wind turbine group participating in frequency modulation and an energy storage device power variation reference value.
Specifically, the first step in this embodiment is: and researching a frequency modulation strategy of the wind turbine generator, and making a comprehensive strategy of the wind turbine generator for participating in frequency control of the power system in a free power generation state. And (3) analyzing a common frequency modulation strategy of the wind turbine, such as a rotor kinetic energy control principle combined by virtual inertia and droop control and a variable pitch control principle under a free power generation state, and formulating a cooperative frequency modulation strategy integrating transient rotor kinetic energy and steady-state variable pitch control.
Further, the second step in this embodiment is: and (3) establishing a mathematical model of a pneumatic system, a transmission system and an electrical system of the wind turbine, constructing a detailed frequency modulation model of the wind turbine based on a simulation platform, generating different working conditions by changing input wind speed and load, and acquiring frequency modulation data of the wind turbine under the different working conditions.
Further, the third step in this embodiment is: a finite difference modeling method and a finite difference modeling flow based on data driving are provided. Including the composition of regression vectors, determination of delay orders, and determination of state space equation input variables, output variables, and state quantities.
Further, the fourth step in this embodiment is: and establishing a frequency modulation active response model of the wind turbine generator set for practical application of the finite difference modeling method. And a hybrid finite difference regression vector for representing wind power frequency modulation characteristics is provided and is used as a model input variable, an output variable and a state quantity are determined, and a linear model of frequency modulation transient characteristics and steady-state characteristics is established through parameter identification.
Further, the fifth step in this embodiment is: and carrying out high-dimensional clustering on the finite difference regression vectors of different working conditions to divide the working condition domains, and classifying the wind turbines in the same working condition domain into one class, thereby realizing the division of the wind turbines with similar frequency modulation characteristics.
Further, the sixth step in this embodiment is: and providing a finite difference regression vector for representing the charge and discharge characteristics of the energy storage, establishing a function model of the wind-storage combined system participating in primary frequency modulation of the power grid, and discretizing according to a model predictive control standard equation.
Further, step seven in this embodiment is: considering that the power distribution of the wind power and energy storage combined system can influence the economy of system frequency modulation, establishing an optimization model by taking the unit time frequency modulation cost of wind power and energy storage devices in a prediction time domain as a target when frequency fluctuation occurs, and restraining variables such as the power variation quantity, the charge state and the wind power unit output of the energy storage devices. And solving the prediction model through an optimization solver, and verifying the effectiveness of a wind power storage optimization distribution strategy under MPC control.
Preferably, the wind turbine generator frequency modulation control strategy comprises: rotor kinetic energy control and power standby control; the rotor kinetic energy control comprises virtual inertia control, sagging control and integrated inertia control combined by the virtual inertia control and the sagging control, wherein power standby control comprises pitch control and overspeed control, and the power standby control is used for a load shedding operation mode of the wind turbine.
Specifically, in the first step, the wind turbine generator frequency modulation control strategy is mainly divided into rotor kinetic energy control and power standby control, wherein the rotor kinetic energy control is applicable to any wind speed, and comprises virtual inertia control, sagging control and integrated inertia control combined with the virtual inertia control and sagging control, and the power standby control comprises pitch control and overspeed control and is applicable to a load shedding operation mode of the wind turbine generator. The rotor kinetic energy control can provide additional reference when the unit operates in a maximum power tracking state, and economical efficiency is ensured, so that the wind turbine generator set operates in a free power generation mode, the rotor kinetic energy control is selected as a frequency modulation strategy, and the variable pitch control is added to ensure the steady-state characteristic of the wind turbine generator set.
Preferably, the method for constructing the frequency modulation detailed model of the wind turbine comprises the following steps:
establishing a mathematical model of wind turbines, transmission systems, electric systems and rotor kinetic energy control of the wind turbine generator set, wherein the mathematical model participates in frequency modulation;
and constructing on a simulation platform according to the mathematical model to obtain the wind turbine frequency modulation detailed model.
In the second step, a mathematical model of wind turbines, transmission systems and electric systems of the wind turbine and rotor kinetic energy control participating in frequency modulation is established, a detailed model is established on a Matlab/Simlink simulation platform, different working conditions are established by changing wind speed and load, and operating characteristics of system frequency change, wind turbine active power, pitch angle, rotor rotating speed and the like in the different working conditions are obtained by operating the simulation model.
Preferably, the finite difference regression vector includes an amount of power system frequency variation, an input wind speed, active power, a pitch angle, and a rotor speed.
Further, in step three of the present embodiment, a finite difference modeling method is proposed. The process comprises the following steps: determining a variable affecting the output characteristic of the system, and judging the delay order of the variable by an AIC criterion to form a finite difference regression vector; taking a finite difference regression vector as input, determining output quantity and state quantity by mechanism analysis, and establishing a linear model; and carrying out specific experimental data, and obtaining a specific state space model through parameter identification.
Further, in step four of the present embodiment, finite difference modeling is performed on wind turbines participating in frequency modulation of the power system, hybrid finite difference regression vectors of each working condition are determined, where the hybrid finite difference regression vectors include a power system frequency variation, an input wind speed, active power, a pitch angle, a rotor rotation speed, and the like, and a delay order is determined through an AIC criterion. And establishing a state space equation model of each working condition, wherein a finite difference regression vector is taken as input in a state space equation, an actual power increment is taken as output, and an input matrix and a state matrix are obtained through parameter identification.
Further, in the fifth step of the embodiment, a high-dimensional clustering algorithm is adopted to cluster regression vectors of different working conditions, and mixed finite difference regression vectors reflect the frequency modulation characteristic of each working condition, so that the regression vectors are clustered to not only cluster data, but also cluster frequency modulation working conditions, and wind turbine generators running in the same working condition are clustered, thereby realizing division of wind turbine generators.
Furthermore, in step six of the present embodiment, the energy storage device has the advantages of rapid response, flexibility, controllability and stable operation, and the linear model characterizing the charge and discharge characteristics of the energy storage device is determined by a finite difference modeling method in consideration of the state of charge SOC of the energy storage device in the charge and discharge process. And synthesizing the wind power and the energy storage model to obtain a system equation set in the primary frequency modulation process, discretizing according to a model predictive control standard equation, determining a state variable matrix, an output variable matrix and an interference variable matrix, and obtaining a state input matrix, a control input matrix, an output matrix and an interference input matrix through parameter identification.
Preferably, the preset constraint condition includes: the energy storage device power variation constraint, the energy storage device charge state constraint and the primary frequency modulation power balance condition constraint.
Furthermore, in step seven of the embodiment, based on the participation of the energy storage system and the wind farm in the primary frequency modulation, an optimization model with the minimum frequency modulation cost per unit time of the fan and the energy storage device is established in a prediction time domain when frequency fluctuation occurs. The frequency modulation cost in the objective function comprises wind farm primary frequency modulation cost and energy storage device primary frequency modulation cost. The constraint conditions comprise energy storage device power variation constraint, energy storage device charge state constraint, primary frequency modulation power balance condition constraint and the like. And solving the prediction model by using an optimization solver to obtain an active output reference value of each wind turbine group participating in frequency modulation and an energy storage device power variation reference value, updating the system state, and entering the next moment to perform optimization solving again.
As an optional implementation manner, as shown in figure 2, the invention finally completes the modeling and control of energy storage to participate in primary frequency modulation of a wind farm under the framework of wind turbine group division, wind turbine and energy storage device active power model, MPC controller design, establishment of an optimization model and optimization algorithm solution, and specifically comprises the following steps:
step one: and formulating a steady-state control strategy and a frequency modulation transient control strategy of the wind turbine. When the wind turbine generator is in a free power generation state, a variable pitch control strategy is adopted to maintain the output stability. When the wind turbine generator participates in the frequency regulation of the power system, a frequency modulation controller is added to the wind turbine generator, and the frequency modulation strategy is a coordinated control strategy combining two strategies of virtual inertia control and droop control, as shown in fig. 3. The virtual inertial control links can be expressed by the following formula:
droop control active output characteristics may be expressed as:
and (3) comprehensive inertia control:
step two: and (5) constructing a wind turbine frequency modulation detailed simulation model to obtain experimental data. Wind turbines typically include subsystems such as wind turbines, drive trains, electrical systems, pitch systems, and control systems. And taking the doubly-fed wind turbine generator as a research object, establishing a mathematical model of each unit of the wind turbine generator, and constructing a detailed power electronic model containing a frequency modulation control strategy of the wind turbine generator on a Matlab/Simlink platform. The wind speed is used as an input variable, the wind speed is changed, different frequency modulation working conditions are obtained, transient characteristic data and steady-state characteristics after the frequency modulation event is completed when the wind turbine generator set participates in frequency modulation in each working condition are recorded.
Step three: for a single operating condition, a hybrid finite difference regression vector is established. Parameters such as input wind speed, power system frequency, rotor speed of the wind turbine, change condition of pitch angle and inertia of the wind turbine can influence actual output increment of the wind turbine. Finite difference regression vectors containing these influencing factors are constructed, and the AIC criterion is used to judge the delay order of each variable. The state space equation when the wind turbine generator participates in frequency adjustment is established as follows:
the method comprises the steps of taking a reference power increment and a finite difference regression vector of a wind turbine generator issued by a power grid as input and taking an actual output increment as output. And obtaining a specific function model through parameter identification.
Step four: clustering the regression vectors of all working conditions to divide the wind power clusters. The number of wind turbines in the wind power plant is large, the output characteristics are inconsistent, and when modeling the wind power plant, it is difficult to traverse all the wind turbines, so that regression vectors are clustered to divide different working condition domains, and characteristic fans in the different working condition domains are selected to represent the output characteristics of the wind power plant.
Step five: and establishing a linear model for representing the output characteristics of the energy storage device by adopting a similar finite difference modeling method.
The state of charge SOC of the energy storage device can change in the charging and discharging process, and the expression of the energy storage device is as follows without considering the charging and discharging efficiency:
S＝S-ΔP bess T/E bess (1.8)
wherein E is bess And the capacity of the energy storage device is T, the sampling step length of the prediction model is T, and the state of charge of energy storage is S. In the studied system, the linearization prediction model standard form is:
where k is the sampling time and A, B, R, C is the state matrix, the control input matrix, the interference matrix, and the output matrix, respectively.
Step six: and (3) establishing an optimization model by taking the frequency modulation cost of the wind power and the energy storage device in unit time in a prediction time domain as a target when frequency fluctuation occurs. The optimization objective function is:
wherein C is wind (k+i|k) and C bess (k+i|k) is the predicted value of the current sampling time k to the primary frequency modulation cost of the fan and the energy storage device at the time k+1, N P To predict the time domain.
The constraint conditions include: and constraining the power variation quantity of the energy storage device, the state of charge of the energy storage device, the pitch angle variation quantity of the fan and the primary frequency modulation power balance condition.
Step seven: and solving the prediction model by using an optimization solver in Matlab, wherein a control variable in the obtained result comprises a control sequence for a period of time, and the first parameter is taken as a reference value of the power variation of the wind power and the energy storage device in the result, so that the system state is updated, and the next moment is entered for optimizing and solving again.
The beneficial effects of the invention are as follows:
(1) According to the invention, a cooperative frequency modulation strategy integrating transient rotor kinetic energy and steady-state variable pitch control is formulated, so that the wind turbine generator participates in the frequency support of the power system in a free power generation mode.
(2) According to the method, mathematical models of all units of the wind turbine including the control system are built, a detailed frequency modulation model of the wind turbine is built based on a simulation platform, and experimental data of the wind turbine participating in frequency modulation under different working conditions are obtained.
(3) The invention provides a finite difference modeling method and a finite difference modeling flow based on data driving.
(4) The invention provides a hybrid finite difference regression vector for representing wind power frequency modulation characteristics, which adopts a finite difference modeling method to establish a linear model containing steady-state characteristics and transient characteristics through parameter identification.
(5) The method carries out high-dimensional clustering division on the finite difference regression vectors under different working conditions to divide the operation domain, thereby realizing division of wind turbines with similar frequency modulation characteristics.
(6) The method is used for constructing a function model comprising loads, synchronous units, wind power plants and energy storage devices participating in power system frequency modulation based on hybrid finite difference regression vectors.
(7) The invention provides an objective function and constraint conditions with minimum frequency modulation cost in unit time, and the model forecast controller is used for carrying out power distribution and model solving.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (5)
1. A primary frequency modulation characteristic modeling and control method of a wind-energy-storage combined system is characterized by comprising the following steps:
acquiring a frequency modulation control strategy of the wind turbine;
based on the wind turbine frequency modulation control strategy, a wind turbine frequency modulation detailed model is established through mechanism analysis, and different frequency modulation working conditions are generated through changing input wind speed and load so as to obtain wind turbine frequency modulation data under different working conditions;
establishing finite difference regression vectors under different frequency modulation working conditions according to actual output influence parameters of the wind turbine, and carrying out parameter identification on each finite difference regression vector by utilizing frequency modulation data of the wind turbine under different working conditions to obtain a linear model of frequency modulation transient characteristics and steady-state characteristics;
carrying out high-dimensional clustering on finite difference regression vectors of different working conditions to divide the working condition domains, classifying the wind turbines in the same working condition domain into one type so as to divide the wind turbines with similar frequency modulation characteristics;
establishing a function model of the wind-storage combined system participating in primary frequency modulation of the power grid according to a finite difference regression vector representing the charge-discharge characteristics of the energy storage, and predicting and controlling standard equation discretization according to the function model;
establishing an optimization model by taking frequency modulation cost of wind power and an energy storage device in unit time in a prediction time domain as a target when frequency fluctuation occurs, and establishing a prediction model according to the optimization model and preset constraint conditions;
and solving the prediction model according to an optimization solver to obtain an active output reference value of each wind turbine group participating in frequency modulation and an energy storage device power variation reference value.
2. The method for modeling and controlling primary frequency modulation characteristics of a wind power generation and storage combined system according to claim 1, wherein the wind power generation set frequency modulation control strategy comprises: rotor kinetic energy control and power standby control; the rotor kinetic energy control comprises virtual inertia control, sagging control and integrated inertia control combined by the virtual inertia control and the sagging control, wherein power standby control comprises pitch control and overspeed control, and the power standby control is used for a load shedding operation mode of the wind turbine.
3. The method for modeling and controlling primary frequency modulation characteristics of a wind power generation set combined system according to claim 1, wherein the method for constructing the wind power generation set frequency modulation detailed model comprises the following steps:
establishing a mathematical model of wind turbines, transmission systems, electric systems and rotor kinetic energy control of the wind turbine generator set, wherein the mathematical model participates in frequency modulation;
and constructing on a simulation platform according to the mathematical model to obtain the wind turbine frequency modulation detailed model.
4. The method for modeling and controlling primary frequency modulation characteristics of a wind power storage system according to claim 1, wherein the finite difference regression vector comprises a power system frequency variation, an input wind speed, active power, a pitch angle and a rotor speed.
5. The method for modeling and controlling primary frequency modulation characteristics of a wind-energy-storage combined system according to claim 1, wherein the preset constraint conditions include: the energy storage device power variation constraint, the energy storage device charge state constraint and the primary frequency modulation power balance condition constraint.
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