Wind storage system frequency modulation method and device, storage medium and product

Abstract

The invention discloses a wind storage system frequency modulation method and device, a storage medium and a product. The method comprises the steps that a prediction model of a wind storage system is established; establishing a frequency modulation optimization model of the wind storage system based on the prediction model of the wind storage system; solving the frequency modulation optimization model to obtain a frequency modulation power reference value, a pitch angle variation reference value and an energy storage power increment reference value; selecting a main draught fan, obtaining the current rotating speed of the main draught fan, and calculating the frequency modulation power of the main draught fan according to the current rotating speed of the main draught fan and the frequency modulation power reference value; the current rotating speed of the non-dominant draught fan is obtained, and the frequency modulation power of the non-dominant draught fan is calculated according to the current rotating speed of the non-dominant draught fan; adjusting the power according to the frequency modulation power of the main draught fan and the pitch angle variable quantity reference value; adjusting the power according to the frequency modulation power of the non-dominant fan; and adjusting the output power of the energy storage battery according to the energy storage power increment reference value. The frequency modulation effect and the stability of the wind power plant are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of wind storage systems, and in particular relates to a frequency modulation method, equipment, storage medium and product of a wind storage system taking into account the comprehensive optimization of rotation speed and pitch angle. Background Art

[0002] As a clean and efficient renewable energy, wind power has been widely used around the world in recent years. With the large-scale replacement of traditional energy for wind power access to the power system, the penetration rate of new energy in the power system has increased. Since wind turbines are connected to the grid through power electronic devices, the coupling relationship between the rotor speed and the system frequency is lost; at the same time, in order to prevent resource waste such as wind abandonment, most wind turbines work in MPPT mode (Maximum power point tracking) to achieve the maximum utilization of wind energy, that is, wind turbines have already issued their own power supply limit and cannot participate in system frequency regulation by increasing power like traditional synchronous generators. The above two characteristics of wind turbines make the system frequency stability face severe challenges. In order to ensure the stable operation of the system, many countries and regions have issued guidelines, requirements or regulations on wind power auxiliary service functions, requiring wind farms to have the ability to participate in power system frequency regulation like conventional power plants.

[0003] Virtual inertia control is currently a traditional way for wind turbines to participate in power system frequency regulation. It supports the grid frequency by releasing rotor kinetic energy. However, since traditional virtual inertia control does not consider the operating status of wind turbines before participating in frequency regulation, the following problems generally exist:

[0004] When virtual inertia control is adopted for low wind speed units, the speed drops too much, which can easily trigger the rotor protection mechanism and exit the frequency regulation prematurely, causing a secondary drop. The frequency regulation effect is poor, and the wind farm wind turbines collectively deviate from the maximum power point and the speed drops, affecting the stability of the entire wind farm.

[0005] In view of the fact that low wind speed units are prone to triggering speed protection, the pitch angle can be changed to make the energy obtained from the source of the wind farm during frequency modulation change accordingly, thereby reducing the degree of rotor kinetic energy release. However, the pitch change action will cause wear of the mechanical structure, further affecting the stable operation of the wind farm. In order to ensure the frequency modulation effect and stability, it is urgent to improve the traditional virtual inertia control method according to the operating status of the wind turbines, improve the frequency modulation effect, and while improving the frequency modulation effect of the wind farm, minimize the impact of both rotor changes and pitch angle changes on the stability of the wind farm. In addition, the traditional centralized frequency modulation control strategy of wind farms has the problem of "dimensional disaster", that is, large-scale wind farms contain hundreds or even thousands of wind turbines. If information is collected and processed centrally for each wind turbine, there will be disadvantages of large amount of calculation and long calculation time, resulting in inefficient frequency modulation of wind farms.

[0006] Therefore, it is urgent to propose a distributed fast frequency support coordinated control strategy suitable for wind farms. Summary of the invention

[0007] The purpose of the present invention is to provide a wind storage system frequency regulation method, equipment, storage medium and product to solve at least one of the problems that the inertial control of traditional wind turbines leads to poor frequency regulation effect, affecting the stability of the entire wind farm, and the dimensional disaster problem caused by large-scale wind farms participating in frequency regulation.

[0008] The present invention solves the above technical problems through the following technical solutions: A frequency modulation method for a wind storage system, comprising:

[0009] Establishing a prediction model of the wind-storage system according to the state information and energy conversion relationship of the wind-storage system;

[0010] Based on the prediction model of the wind-storage system, a frequency regulation optimization model of the wind-storage system is established;

[0011] Solving the frequency regulation optimization model of the wind-storage system to obtain a frequency regulation power reference value and a pitch angle variation reference value of the wind turbine and an energy storage power increment reference value of the energy storage battery;

[0012] A leading fan is selected and a current speed of the leading fan is obtained, and the frequency modulation power of the leading fan is calculated according to the current speed of the leading fan and a frequency modulation power reference value;

[0013] Obtaining the current speed of the non-dominant fan, and calculating the frequency modulation power of the non-dominant fan according to the current speed of the non-dominant fan;

[0014] The power of the dominant wind turbine is adjusted according to the frequency modulation power and pitch angle variation reference value of the dominant wind turbine; the power of the non-dominant wind turbine is adjusted according to the frequency modulation power of the non-dominant wind turbine; the output power of the energy storage battery is adjusted according to the energy storage power increment reference value.

[0015] Furthermore, the specific process of establishing the prediction model of the wind energy storage system includes:

[0016] According to the state information and energy conversion relationship of each wind turbine in the wind storage system, a prediction model of the wind turbine is established; wherein the specific expression of the prediction model of the wind turbine is:

[0017]

[0018] in, represents the derivative of the state variable of the i-th fan, A i The coefficient matrix representing the state variables of the i-th wind turbine, Δx irepresents the state variable of the i-th fan, B i represents the coefficient matrix of the control variables of the i-th fan, Δu i represents the control variable of the i-th fan, Δy i represents the increment of the state variable of the i-th fan, C i The coefficient matrix representing the state variables and their increments of the i-th fan, E i represents the initial measurement matrix of the i-th wind turbine, Δω r,i represents the speed change of the i-th fan, ΔP e,i represents the change in electromagnetic power of the i-th fan, Δθ i represents the pitch angle change of the i-th wind turbine, ΔP m,i represents the change in mechanical power of the i-th fan, Δθ ref,i represents the reference value of the pitch angle change of the i-th wind turbine, ΔP ref,i represents the frequency modulation power reference value of the i-th fan, P e0,i represents the initial value of electromagnetic power of the i-th fan, P m0,i represents the initial value of the mechanical power of the i-th fan, H W represents the inertia time constant of the i-th fan, ω r0,i represents the initial speed value of the i-th fan, T c represents the control period, T θ represents the response time constant of pitch angle control, k0 represents the linear relationship coefficient between mechanical power and pitch angle;

[0019] Establish a prediction model for energy storage batteries; wherein the specific expression of the prediction model for energy storage batteries is:

[0020]

[0021] in, Represents the derivative of the state variable of the energy storage battery, A E The coefficient matrix representing the state variables of the energy storage battery, Δx E Represents the state variable of the energy storage battery, B E The coefficient matrix representing the control variables of the energy storage battery, Δu E represents the control variable of the energy storage battery, Δy E Represents the state variable increment of the energy storage battery, C E The coefficient matrix representing the state variables and their increments of the energy storage battery, E E represents the initial measurement matrix of the energy storage battery, Δf soc Indicates the change in the state of charge of the energy storage battery, ΔP ESS

[0022] eIndicates the energy storage power increment of the energy storage battery, ΔP int represents the state function, Δi D Indicates the DC increment of the energy storage battery, Indicates the energy storage power increment reference value of the energy storage battery, Q c Represents the energy storage capacity of the energy storage battery, T fd represents the time constant of the energy storage control loop, k pd represents the proportional gain of the PI controller of the energy storage DC current control loop, k id represents the integral gain of the PI controller of the energy storage DC current control loop, T id Indicates the time constant of the active power control loop, U D Indicates the energy storage DC voltage of the energy storage battery;

[0023] According to the prediction model of the wind turbine and the energy storage battery, a prediction model of the wind storage system is established; wherein the specific expression of the prediction model of the wind storage system is:

[0024]

[0025]

[0026] in, represents the derivative of the state variables of the wind-storage system, A represents the coefficient matrix of the state variables of the wind-storage system, B represents the coefficient matrix of the control variables of the wind-storage system, C represents the coefficient matrix of the state variables and their increments of the wind-storage system, E represents the initial measurement matrix of the wind-storage system, Δx represents the state variables of the wind-storage system, Δu represents the control variables of the wind-storage system, Δy represents the increment of the state variables of the wind-storage system, N represents the number of wind turbines in the wind-storage system, and diag represents a diagonal matrix.

[0027] Furthermore, the specific expression of the frequency regulation optimization model of the wind storage system is:

[0028]

[0029] -(θ 0,i -θ min,i )<Δθ ref,i <(θ max,i -θ 0,i );

[0030]

[0031] Among them, λ ωr represents the weight coefficient of speed control, p represents the number of sampling points, N represents the number of wind turbines in the wind energy storage system, Δω r,i (k) represents the speed change of the i-th fan at the k-th moment, Δω r,ave(k) represents the average value of the speed change of all fans at the kth moment, λ β1 represents the weight coefficient of pitch angle control, θ i (k) represents the pitch angle of the i-th wind turbine at the k-th moment, θ ave (k) represents the average pitch angle of all wind turbines at the kth moment, λ β2 Represents the weight coefficient of pitch angle change control, Δθ i (k) represents the pitch angle change of the i-th wind turbine at the k-th moment, λ E represents the weight coefficient of energy storage loss control, f L (k) represents the energy storage loss cost, P e0,i represents the initial value of electromagnetic power of the i-th fan, ΔP ref,i represents the frequency modulation power reference value of the i-th fan, represents the initial energy storage power of the i-th energy storage battery, represents the energy storage power increment reference value of the i-th energy storage battery, P max,i represents the rated capacity of the i-th fan, θ 0,i represents the initial pitch angle of the i-th wind turbine, θ min,i represents the minimum pitch angle of the i-th wind turbine, Δθ ref,i represents the reference value of the pitch angle change of the i-th wind turbine, θ max,i represents the maximum pitch angle of the i-th wind turbine,

[0032] Indicates the upper limit of the discharge power of the energy storage battery. represents the energy storage power of the i-th energy storage battery, represents the energy storage power increment reference value of the i-th energy storage battery, Indicates the upper limit of the charging power of the energy storage battery, ΔP ESS,i Represents the power increment of the i-th energy storage battery, ΔP ref Indicates the frequency modulation power demand, K p Indicates the droop control coefficient of virtual inertia control, K d represents the inertia control coefficient of virtual inertia control, Δf represents the change of grid frequency, and t represents time.

[0033] Furthermore, the quadrog optimization solver provided in MATLAB is used to solve the frequency modulation optimization model of the wind storage system.

[0034] Further, the frequency modulation power of the leading fan is calculated according to the current speed of the leading fan and the frequency modulation power reference value, including:

[0035] Calculating a consistency state factor of the leading fan according to the current rotation speed of the leading fan;

[0036] Performing PI adjustment on the consistency state factor of the leading fan to obtain a first PI adjustment result;

[0037] Superimposing the first PI adjustment result and the frequency modulation power reference value of the leading wind turbine to obtain a superimposed result;

[0038] Performing maximum power point tracking on the current rotation speed of the leading wind fan to obtain the maximum frequency modulation power of the leading wind fan;

[0039] Superimposing the maximum frequency modulation power of the leading fan with the superposition result to obtain the frequency modulation power of the leading fan;

[0040] Calculating the frequency modulation power of the non-dominant fan according to the current rotation speed of the non-dominant fan includes:

[0041] Calculating a consistency state factor of the non-dominant fan according to the current rotation speed of the non-dominant fan;

[0042] Performing PI adjustment on the consistency state factor of the non-dominant fan to obtain a second PI adjustment result;

[0043] Performing maximum power point tracking on the current rotation speed of the non-dominant fan to obtain the maximum frequency modulation power of the non-dominant fan;

[0044] The maximum frequency modulation power of the non-dominant fan is superimposed on the second PI adjustment result to obtain the frequency modulation power of the non-dominant fan.

[0045] Furthermore, the specific calculation formula of the consistency state factor is:

[0046]

[0047] Among them, c i represents the consistency state factor of the i-th fan, ω r0,i represents the initial speed value of the i-th fan, ω r,i represents the speed of the i-th fan, ω r,min Indicates the minimum speed of the fan.

[0048] Furthermore, the specific calculation formula of the second PI adjustment result is:

[0049]

[0050] Where ΔP nref,l represents the frequency modulation power reference value of the lth non-dominant wind turbine, k P,l and k I,l They represent the proportional gain and integral gain of the first non-dominant fan when performing PI regulation, c l represents the consistency state factor of the lth non-dominant wind turbine, cj represents the consistency state factor of the jth non-dominant wind turbine, and N represents the number of wind turbines in the wind-storage system.

[0051] Based on the same concept, the present invention also provides an electronic device, including a memory, a processor, and a computer program / instruction stored in the memory, wherein the processor executes the computer program / instruction to implement the wind storage system frequency modulation method as described above.

[0052] Based on the same concept, the present invention also provides a computer-readable storage medium on which a computer program / instruction is stored. When the computer program / instruction is executed by a processor, the frequency modulation method of the wind-storage system as described above is implemented.

[0053] Based on the same concept, the present invention also provides a computer program product, including a computer program / instruction, which implements the wind storage system frequency modulation method as described above when executed by a processor.

[0054] Beneficial Effects

[0055] Compared with the prior art, the advantages of the present invention are:

[0056] The present invention fully considers the operating status of the wind farm and the energy storage system in the wind-storage system, and provides a reasonable and reliable wind-storage system joint distributed method for reducing the system inertia caused by the access of large-scale wind farms to the power grid, improves the secondary drop problem, improves the frequency regulation effect, and is conducive to optimizing the frequency stability of the power grid system and ensuring the safe and stable operation of the power grid.

[0057] Compared with the traditional inertial control method, during the inertial control process of the wind farm, the present invention can not only realize the coordinated control of the active power of the wind power generation unit and the energy storage system unit in the distributed energy storage wind turbine, but also reduce the charging and discharging loss cost of the energy storage system, and ensure the stability of the rotor speed of all wind turbines in the wind farm, thereby avoiding the exit of the wind turbine from frequency modulation due to excessive drop in wind turbine speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solution of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only one embodiment of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0059] Figure 1 It is a flow chart of the frequency modulation method of the wind storage system in an embodiment of the present invention. DETAILED DESCRIPTION

[0060] The following is a clear and complete description of the technical solutions in the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0061] The technical solution of the present application is described in detail with specific embodiments below. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0062] like Figure 1 As shown, a wind-storage system frequency modulation method provided by an embodiment of the present invention includes the following steps:

[0063] Step 1: Establish a prediction model of the wind-storage system based on the state information and energy conversion relationship of the wind-storage system.

[0064] In a specific embodiment of the present invention, the specific process of establishing the prediction model of the wind energy storage system includes:

[0065] Step 1.1: Establish a prediction model for wind turbines based on the status information and energy conversion relationship of each wind turbine in the wind energy storage system.

[0066] When the wind farm adopts pitch angle control at medium and high wind speeds, the time scale of frequency modulation is short. In order to simplify the model, the wind speed is set to remain at a certain higher value; when the wind speed is constant, the linear relationship between the mechanical energy captured by the wind turbine (i.e. wind turbine or wind turbine) and the pitch angle can be obtained as follows:

[0067] P m =k0θ+b (1)

[0068] Among them, k0 represents the linear relationship coefficient, b represents the constant, P m represents the mechanical energy captured by the wind turbine, and θ represents the pitch angle of the wind turbine. In a pitch angle control cycle, the pitch angle can be expressed as:

[0069] θ=θ0+Δθ (2)

[0070] Wherein, θ0 represents the initial pitch angle; Δθ represents the change in pitch angle. Then formula (1) can be expressed as:

[0071] ΔP m =k0Δθ (3)

[0072] Where ΔP m Indicates the change in mechanical power of the wind turbine.

[0073] The response of the wind turbine pitch angle control is relatively slow, and its transfer function can be equivalent to a first-order transfer function, that is:

[0074] G θ (s) = 1 / (1+T θ s) (4)

[0075] Among them, T θ represents the time constant of the pitch angle control response, G θ (s) represents the transfer function of the pitch angle control response. Then, the pitch angle change Δθ and the pitch angle change reference value command Δθ can be obtained. ref The relationship between:

[0076] Δθ=Δθ ref G θ (s) (5)

[0077] So we can get:

[0078] ΔP m =k0Δθ ref G θ (s) (6)

[0079] Converting formula (5) and formula (6) into time domain expressions, we get:

[0080]

[0081] The rotor motion equation of a wind turbine is:

[0082]

[0083] Among them, ω r Indicates the rotor speed of the wind turbine; P e Represents the electromagnetic power of the wind turbine; H W Represents the inertia time constant of the wind turbine. Assume that the current speed value fed back by the wind turbine, that is, the initial value of the speed change within a control cycle, is ω r0 , the speed change is Δω r , the initial value of electromagnetic power is P e0 , the change in electromagnetic power is ΔP e , the initial value of mechanical power is P m0 , then formula (9) can be written as:

[0084]

[0085] Taylor expansion is used to linearize formula (10), and assuming that the mechanical power remains unchanged within a control cycle, we have:

[0086]

[0087] In each control cycle, it is assumed that the reference value of the electromagnetic power change of the fan frequency regulation is ΔP ref , and the process of the fan tracking the active reference value change command can be approximated as a first-order inertia link, that is:

[0088]

[0089] Among them, T c Represents a control cycle. The internal loss of the fan and other issues lead to the inability of the wind energy captured by the fan to be fully converted into electromagnetic power. The conversion relationship between the two can be expressed by a first-order transfer function:

[0090]

[0091] Where ΔP m is the mechanical power change, T is the time constant, and T = T c .

[0092] Therefore, according to formula (12) and formula (13), the change of electromagnetic power can be expressed as:

[0093]

[0094] Convert formula (14) into time domain expression:

[0095]

[0096] Combining formula (7), formula (8), formula (11) and formula (15), we get:

[0097]

[0098] Establish a prediction model for the i-th wind turbine in the wind farm and select the speed change Δω r,i , Electromagnetic power change ΔP e,i , pitch angle change Δθ i and mechanical power change ΔP m.i is the state variable, and the reference value instruction of electromagnetic power variation (i.e., frequency modulation power reference value) is selected as ΔP ref.i and pitch angle change reference value command Δθ ref.i is the control variable, then:

[0099]

[0100] in, represents the derivative of the state variable of the i-th fan, A i The coefficient matrix representing the state variables of the i-th wind turbine, Δx i represents the state variable of the i-th fan, B irepresents the coefficient matrix of the control variables of the i-th fan, Δu i represents the control variable of the i-th fan, Δy i represents the increment of the state variable of the i-th fan, C i The coefficient matrix representing the state variables and their increments of the i-th fan, E i represents the initial measurement matrix of the i-th wind turbine, Δω r,i represents the speed change of the i-th fan, ΔP e,i represents the change in electromagnetic power of the i-th fan, Δθ i represents the pitch angle change of the i-th wind turbine, ΔP m,i represents the change in mechanical power of the i-th fan, Δθ ref,i represents the reference value of the pitch angle change of the i-th wind turbine, ΔP ref,i represents the frequency modulation power reference value of the i-th fan, P e0,i represents the initial value of electromagnetic power of the i-th fan, P m0,i represents the initial value of the mechanical power of the i-th fan, H W represents the inertia time constant of the i-th fan, ω r0,i represents the initial speed value of the i-th fan, T c represents the control period, T θ represents the response time constant of pitch angle control, and k0 represents the linear relationship coefficient between mechanical power and pitch angle.

[0101] Step 1.2: Establish a prediction model for the energy storage battery based on the state information and energy conversion relationship of the energy storage battery in the wind storage system.

[0102] When the wind turbine participates in the system frequency regulation alone, its inertial control ability is weak, and it affects the stability of the system frequency when exiting the inertial control. The energy storage system has the characteristics of flexible control and fast response. It can provide active backup through energy storage to enhance the ability of the wind turbine to participate in the system frequency regulation. In order to facilitate the construction of the state space model of the energy storage battery, the state function ΔP is introduced int for:

[0103]

[0104] in, Indicates the energy storage power increment reference value of the energy storage battery, ΔP e ESS Represents the energy storage power increment of the energy storage battery. The specific expression of the prediction model (or state space model) of the energy storage battery is:

[0105]

[0106] in, Represents the derivative of the state variable of the energy storage battery, AE The coefficient matrix representing the state variables of the energy storage battery, Δx E Represents the state variable of the energy storage battery, B E The coefficient matrix representing the control variables of the energy storage battery, Δu E represents the control variable of the energy storage battery, Δy E Represents the state variable increment of the energy storage battery, C E The coefficient matrix representing the state variables and their increments of the energy storage battery, E E represents the initial measurement matrix of the energy storage battery, Δf soc Indicates the change in the state of charge of the energy storage battery, ΔP e ESS Indicates the energy storage power increment of the energy storage battery, ΔP int represents the state function, Δi D Indicates the DC increment of the energy storage battery, Indicates the energy storage power increment reference value of the energy storage battery, Q c Represents the energy storage capacity of the energy storage battery, T fd represents the time constant of the energy storage control loop, k pd represents the proportional gain of the PI controller of the energy storage DC current control loop, k id represents the integral gain of the PI controller of the energy storage DC current control loop, T id Indicates the time constant of the active power control loop, U D Indicates the energy storage DC voltage of the energy storage battery.

[0107] Step 1.3: Based on the prediction models of wind turbines and energy storage batteries, a prediction model for the wind energy storage system is established.

[0108] Assume that the wind-storage system has N wind turbines and N energy storage batteries. According to the prediction models of wind turbines and energy storage batteries, the prediction model (or state space model) of the entire wind-storage system can be obtained as follows:

[0109]

[0110] in, represents the derivative of the state variables of the wind-storage system, A represents the coefficient matrix of the state variables of the wind-storage system, B represents the coefficient matrix of the control variables of the wind-storage system, C represents the coefficient matrix of the state variables and their increments of the wind-storage system, E represents the initial measurement matrix of the wind-storage system, Δx represents the state variables of the wind-storage system, Δu represents the control variables of the wind-storage system, Δy represents the increment of the state variables of the wind-storage system, and diag represents a diagonal matrix.

[0111] In order to solve the prediction model, the continuous state space model (Formula (24)) is converted into the sampling time T P The discrete state space model of is:

[0112]

[0113] Wherein, Δx(k) represents the increment of the state variable at time k in the discrete state space model of the wind-storage system; Δu(k) represents the control variable in the discrete state space model of the wind-storage system; Δy(k+1) represents the increment of the output variable at time k+1 of the discrete state space model of the wind-storage system with Δu as the control variable; G represents the output state coefficient matrix of the discrete state space model of the wind-storage system; H represents the control matrix of the discrete state space model of the wind-storage system; and

[0114]

[0115] Step 2: Based on the prediction model of the wind-storage system, establish the frequency regulation optimization model of the wind-storage system.

[0116] Establish the objective function and corresponding constraints to ensure the stable participation of the wind storage system in the grid frequency regulation. Since the wind speed of each wind turbine is different, the speed changes differently with the wind speed, and the rotor kinetic energy that can be released is also different. If each wind turbine participates in the system frequency regulation in the same inertial control method, the frequency regulation may be terminated during the frequency regulation process due to the low speed of some wind turbines. When the rotor kinetic energy regulation reaches the limit, the pitch angle control is introduced to change the captured mechanical power to continue to support the grid frequency regulation.

[0117] This embodiment considers three control objectives at the wind farm level. The first control objective is to reduce the difference in rotor speed to ensure the stability of the rotor speed of each wind turbine in the wind farm; the second control objective is to minimize the difference in pitch angles between wind turbines to ensure the stable operation of each wind turbine in the wind farm; when only the second control objective is used, that is, the difference in pitch angles between wind turbines is minimized, which means that the pitch angles of all wind turbines converge to the same value, the third control objective is set to reduce the change in the pitch angle of the wind turbine itself, in order to minimize wind energy loss. The mathematical forms of the three control objectives Obj1, Obj2, and Obj3 are:

[0118]

[0119] Among them, || || 2 represents the 2-norm, p represents the number of sampling points, N represents the number of wind turbines in the wind energy storage system, Δω r,i (k) represents the speed change of the i-th fan at the k-th moment, Δω r,ave (k) represents the average value of the speed change of all fans at the kth moment, θ i (k) represents the pitch angle of the i-th wind turbine at the k-th moment, θ ave (k) represents the average pitch angle of all wind turbines at the kth moment, Δθ i(k) represents the pitch angle change of the i-th wind turbine at the k-th moment.

[0120] The control target Obj4 at the battery energy storage level is set to minimize the energy storage loss cost, that is:

[0121]

[0122] in:

[0123]

[0124] in, and Respectively represent the charging power and discharging power of the energy storage battery; μ ESS Represents the cost coefficient of energy storage battery; γ ESS Indicates the rated life of the energy storage battery; Indicates the initial state of charge; Indicates the standard state of charge; T ESS Indicates the temperature of the energy storage battery; E R represents the capacity of the energy storage battery; α1, α2, α3 and α4 represent the life cycle coefficient of the energy storage battery; T c Indicates the control period.

[0125] Therefore, the overall control target is based on a certain weight coefficient λ ωr , β1 , β2 , E Assigned as:

[0126]

[0127] Among them, λ ωr Represents the weight coefficient of speed control, λ β1 represents the weight coefficient of pitch angle control, λ β2 represents the weight coefficient of pitch angle change control, λ E Represents the weight coefficient of energy storage loss control.

[0128] During the control process, the frequency modulation power reference value of the wind turbine should be less than its capacity upper limit; since the pitch angle is controlled by the hydraulic servo system, there needs to be certain constraints on the pitch angle θ; the energy storage can operate in the charge and discharge mode, and its output power should follow the charge and discharge power limit, that is, the frequency modulation power reference value, pitch angle variation reference value and energy storage power inequality constraints of the wind turbine are:

[0129]

[0130] -(θ 0,i -θ min,i )<Δθ ref,i<(θ max,i -θ 0,i ) i=1,2,...,N (36)

[0131]

[0132] Among them, P e0,i represents the initial value of electromagnetic power of the i-th fan, ΔP ref,i represents the frequency modulation power reference value of the i-th fan, P max,i represents the rated capacity of the i-th fan; θ 0,i represents the initial pitch angle of the i-th wind turbine, Δθ ref,i represents the reference value of the pitch angle change of the i-th wind turbine, θ min,i represents the minimum pitch angle of the i-th wind turbine, θ max,i represents the maximum pitch angle of the i-th wind turbine, represents the energy storage power of the i-th energy storage battery, represents the energy storage power increment reference value of the i-th energy storage battery, and Respectively represent the upper and lower limits of the charging and discharging power of the energy storage battery.

[0133] In each control cycle of model predictive control (MPC), the sum of the frequency modulation power reference value instructions issued by the MPC controller to each wind turbine should be equal to the frequency modulation power demand of the system for the wind farm. Assuming that the frequency modulation power demand of the system for the wind storage system is based on the fixed inertia and damping characteristics of the system frequency, that is, the total frequency modulation power of the wind storage system is calculated in accordance with the virtual inertia control method, therefore, the frequency modulation power reference value equation constraint can be expressed as:

[0134]

[0135] Where ΔP ESS,i Indicates the increment of energy storage power, ΔP ref Indicates the frequency modulation power demand, K p Indicates the droop control coefficient of virtual inertia control, K d represents the inertia control coefficient of virtual inertia control, Δf represents the change of grid frequency, and t represents time.

[0136] Step 3: Solve the frequency regulation optimization model of the wind-storage system to obtain the frequency regulation power reference value and pitch angle change reference value of the leading wind turbine and the energy storage power increment reference value of the energy storage battery.

[0137] The quadrog optimization solver in MATLAB is used to solve the frequency modulation optimization model of the wind-storage system under multi-constraint conditions. The control variables in the solution result include a control sequence for a period of time [Δu(k), Δu(k+1),...Δu(k+N p )], where N p To control the time domain, the first parameter Δu(k) is taken from the solution, which includes the frequency modulation power reference value ΔP of the leading wind turbine. ref,i and pitch angle variation reference value Δθ ref,i And the energy storage power increment reference value of the energy storage battery. Update the system state, enter the next moment to optimize and solve again, and repeat this cycle until the control quantity reference value at the last moment is obtained.

[0138] Step 4: Select a leading fan and obtain the current speed of the leading fan, and calculate the frequency modulation power of the leading fan according to the current speed of the leading fan and the frequency modulation power reference value.

[0139] The wind farm includes N wind turbines. One of the N wind turbines is selected as the dominant wind turbine, and the other wind turbines are non-dominant wind turbines. The dominant wind turbine and energy storage battery are controlled by the model predictive control optimization algorithm. The non-dominant wind turbine will follow the actions of the dominant wind turbine through consistency indicators to achieve distributed control.

[0140] When the scale of wind farm is large, if all wind turbines receive the system frequency signal, communication delay will occur. Therefore, based on the leader-follower consistency algorithm, a consistent distributed coordination control strategy is proposed. The calculation formula of the wind turbine consistency state factor is:

[0141]

[0142] Among them, c i represents the consistency state factor of the i-th fan, ω r0,i represents the initial speed value of the i-th fan, ω r,i represents the speed of the i-th fan, ω r,min Indicates the minimum speed of the fan. The consistency state factor indicates the deviation of the current operating state of each fan relative to the initial state of the fan.

[0143] In a specific implementation of the present invention, the frequency modulation power of the leading fan is calculated according to the current rotation speed of the leading fan and the frequency modulation power reference value, including:

[0144] Step 4.1: Calculate the consistency state factor of the leading fan according to the current speed of the leading fan, see formula (39);

[0145] Step 4.2: Perform PI adjustment on the consistency state factor of the leading fan to obtain a first PI adjustment result;

[0146] Step 4.3: Compare the first PI adjustment result with the frequency modulation power reference value ΔP of the leading fan ref,i Perform superposition to obtain a superposition result;

[0147] Step 4.4: Perform maximum power point tracking on the current speed of the leading fan to obtain the maximum frequency modulation power of the leading fan;

[0148] Step 4.5: Superimpose the maximum frequency modulation power of the leading fan and the superposition result to obtain the frequency modulation power of the leading fan.

[0149] Step 5: Obtain the current rotation speed of the non-dominant fan, and calculate the frequency modulation power of the non-dominant fan according to the current rotation speed of the non-dominant fan.

[0150] Only the selected dominant fan (the i-th fan is selected as the dominant fan) receives the system frequency for comprehensive inertia control, and the remaining fans (i.e., non-dominant fans) only need to exchange consistency indicators with adjacent fans, so as to follow the state of the dominant fan to participate in frequency modulation. In a specific embodiment of the present invention, the frequency modulation power of the non-dominant fan is calculated according to the current speed of the non-dominant fan, including:

[0151] Step 5.1: Calculate the consistency state factor of the non-dominant fan according to the current speed of the non-dominant fan, see formula (39);

[0152] Step 5.2: Perform PI adjustment on the consistency state factor of the non-dominant fan to obtain a second PI adjustment result;

[0153] Step 5.3: Perform maximum power point tracking on the current speed of the non-dominant fan to obtain the maximum frequency modulation power of the non-dominant fan;

[0154] Step 5.4: Superimpose the maximum frequency modulation power of the non-dominant fan and the second PI adjustment result to obtain the frequency modulation power of the non-dominant fan.

[0155] In this embodiment, the calculation formula of the second PI adjustment result (ie, the frequency modulation power reference value of the non-dominant wind turbine) is:

[0156]

[0157] Where ΔP nref,l represents the frequency modulation power reference value of the lth non-dominant wind turbine, k P,l and k I,l They represent the proportional gain and integral gain of the first non-dominant fan when performing PI regulation, c l represents the consistency state factor of the lth non-dominant wind turbine, c j It represents the consistency state factor of the j-th non-dominant fan, and the j-th non-dominant fan is the adjacent fan of the l-th non-dominant fan.

[0158] Step 6: Regulate the power of the dominant wind turbine according to the frequency modulation power and pitch angle variation reference value of the dominant wind turbine; regulate the power of the non-dominant wind turbine according to the frequency modulation power of the non-dominant wind turbine; and regulate the output power of the energy storage battery according to the energy storage power increment reference value.

[0159] The wind turbine works normally in the maximum power point tracking state, that is, the active power reference value is obtained by the MPPT controller. When the system is disturbed and needs to participate in frequency modulation, it is necessary to superimpose a frequency modulation power reference value on the active power reference value to adjust the frequency modulation. The frequency modulation power reference value of the dominant wind turbine in the wind farm is obtained by the MPC controller through optimization. In order to achieve the non-dominant wind turbine following the action of the dominant wind turbine, the frequency modulation power reference value of the non-dominant wind turbine will be obtained by the consistency state factor through the PI regulator, and the following power needs to be superimposed on the frequency modulation power reference value of the dominant wind turbine. In order to ensure the overall effect of frequency modulation, the pitch angle adjustment and power response of the energy storage system to improve the stability of the wind farm are introduced. Only the dominant wind turbine participates in the adjustment of the pitch angle, that is, only the dominant wind turbine receives the pitch angle adjustment signal (that is, the pitch angle change reference value) and acts on its own pitch angle adjustment system. The energy storage system only receives the power adjustment signal to achieve rapid response to the system.

[0160] In summary, the MPC controller needs to optimize and solve the frequency modulation power reference value of the dominant wind turbine, the pitch angle adjustment signal and the power adjustment signal of the energy storage system (i.e., the energy storage power increment reference value). The dominant wind turbine receives the frequency modulation power reference value and the pitch angle change reference value of the MPC controller. The non-dominant wind turbine only obtains the frequency modulation power reference value by exchanging the consistency state factor, i.e., PI adjustment. The energy storage system receives the energy storage power increment reference value of the MPC controller.

[0161] The present invention considers the operating state of the leading wind turbine before participating in the frequency adjustment of the power system, establishes a prediction model for the speed change, electromagnetic power change, pitch angle change, and mechanical power change, introduces an energy storage system and establishes a prediction model for the charge state change, energy storage power increment, and energy storage DC current increment, and establishes a wind-storage joint frequency regulation optimization model with wind farm stability, optimal frequency regulation effect, and minimum energy storage loss cost as the control objectives by joint model predictive control (MPC). The frequency regulation power reference value, pitch angle change reference value, and energy storage power increment reference value of the leading wind turbine are obtained through optimization and solution; at the same time, a consistency index for measuring the frequency regulation effect of the wind turbine is proposed, and the non-leading wind turbine will follow the action of the leading wind turbine through the consistency index, realizing distributed control and improving the frequency regulation effect. The present invention can provide a control method considering the operating state for the wind farm participating in the power grid frequency regulation, and realize the distributed control of the wind farm, ensuring the safety and stability of the power system frequency.

[0162] Example 2

[0163] An embodiment of the present invention further provides an electronic device, which includes: a memory, a processor, and a computer program / instructions stored in the memory, and the processor executes the computer program / instructions to implement the wind storage system frequency modulation method in the embodiment of the present application.

[0164] Although not shown, the electronic device includes a processor, which can perform various appropriate operations and processes according to the program and / or data stored in the read-only memory (ROM) or the program and / or data loaded from the storage portion into the random access memory (RAM). The processor can be a multi-core processor, or it can include multiple processors. In some embodiments, the processor can include a general main processor and one or more special coprocessors, such as a central processing unit, a graphics processing unit (GPU), a neural network processor (NPU), a digital signal processor (DSP), etc. In RAM, various programs and data required for device operation are also stored. The processor, ROM and RAM are connected to each other via a bus. The input / output (I / O) interface is also connected to the bus.

[0165] The processor and memory are used together to execute the program / instructions stored in the memory. When the program / instructions are executed by the computer, the methods, steps or functions described in the above embodiments can be implemented.

[0166] Although not shown, an embodiment of the present invention further provides a computer-readable storage medium on which a computer program / instruction is stored. When the computer program / instruction is executed by a processor, the wind-storage system frequency modulation method in the embodiment of the present application is implemented.

[0167] Readable storage media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include temporary computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0168] Although not shown, an embodiment of the present invention further provides a computer program product, including: a computer program / instruction, which, when executed by a processor, implements the wind-storage system frequency modulation method in the embodiment of the present application.

[0169] What is disclosed above is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or modifications within the technical scope disclosed in the present invention, which should be covered within the protection scope of the present invention.

Claims

1. A frequency modulation method for a wind energy storage system, characterized in that: The frequency modulation method comprises: Establishing a prediction model of the wind-storage system according to the state information and energy conversion relationship of the wind-storage system; Based on the prediction model of the wind-storage system, a frequency regulation optimization model of the wind-storage system is established; Solving the frequency regulation optimization model of the wind-storage system to obtain a frequency regulation power reference value and a pitch angle variation reference value of the wind turbine and an energy storage power increment reference value of the energy storage battery; A leading fan is selected and a current speed of the leading fan is obtained, and the frequency modulation power of the leading fan is calculated according to the current speed of the leading fan and a frequency modulation power reference value; Obtaining the current speed of the non-dominant fan, and calculating the frequency modulation power of the non-dominant fan according to the current speed of the non-dominant fan; The power of the dominant wind turbine is adjusted according to the frequency modulation power and pitch angle variation reference value of the dominant wind turbine; the power of the non-dominant wind turbine is adjusted according to the frequency modulation power of the non-dominant wind turbine; the output power of the energy storage battery is adjusted according to the energy storage power increment reference value.

2. The wind energy storage system frequency modulation method according to claim 1, characterized in that: The specific process of establishing the prediction model of the wind storage system includes: According to the state information and energy conversion relationship of each wind turbine in the wind storage system, a prediction model of the wind turbine is established; wherein the specific expression of the prediction model of the wind turbine is: in, represents the derivative of the state variable of the i-th fan, A i The coefficient matrix representing the state variables of the i-th wind turbine, Δx i represents the state variable of the i-th fan, B i represents the coefficient matrix of the control variables of the i-th fan, Δu i represents the control variable of the i-th fan, Δy i represents the increment of the state variable of the i-th fan, C i The coefficient matrix representing the state variables and their increments of the i-th fan, E i represents the initial measurement matrix of the i-th wind turbine, Δω r,i represents the speed change of the i-th fan, ΔP e,i represents the change in electromagnetic power of the i-th fan, Δθ i represents the pitch angle change of the i-th wind turbine, ΔP m,i represents the change in mechanical power of the i-th fan, Δθ ref,i represents the reference value of the pitch angle change of the i-th wind turbine, ΔP ref,i represents the frequency modulation power reference value of the i-th fan, P e0,i represents the initial value of electromagnetic power of the i-th fan, P m0,i represents the initial value of the mechanical power of the i-th fan, H W represents the inertia time constant of the i-th fan, ω r0,i represents the initial speed value of the i-th fan, T c represents the control period, T θ represents the response time constant of pitch angle control, k0 represents the linear relationship coefficient between mechanical power and pitch angle; Establish a prediction model for energy storage batteries; wherein the specific expression of the prediction model for energy storage batteries is: in, Represents the derivative of the state variable of the energy storage battery, A E The coefficient matrix representing the state variables of the energy storage battery, Δx E Represents the state variable of the energy storage battery, B E The coefficient matrix representing the control variables of the energy storage battery, Δu E represents the control variable of the energy storage battery, Δy E Represents the state variable increment of the energy storage battery, C E The coefficient matrix representing the state variables and their increments of the energy storage battery, E E represents the initial measurement matrix of the energy storage battery, Δf soc Indicates the change in the state of charge of the energy storage battery. Indicates the energy storage power increment of the energy storage battery, ΔP int represents the state function, Δi D Indicates the DC increment of the energy storage battery, Indicates the energy storage power increment reference value of the energy storage battery, Q c Represents the energy storage capacity of the energy storage battery, T fd represents the time constant of the energy storage control loop, k pd represents the proportional gain of the PI controller of the energy storage DC current control loop, k id represents the integral gain of the PI controller of the energy storage DC current control loop, T id Indicates the time constant of the active power control loop, U D Indicates the energy storage DC voltage of the energy storage battery; According to the prediction model of the wind turbine and the energy storage battery, a prediction model of the wind storage system is established; wherein the specific expression of the prediction model of the wind storage system is: in, represents the derivative of the state variables of the wind-storage system, A represents the coefficient matrix of the state variables of the wind-storage system, B represents the coefficient matrix of the control variables of the wind-storage system, C represents the coefficient matrix of the state variables and their increments of the wind-storage system, E represents the initial measurement matrix of the wind-storage system, Δx represents the state variables of the wind-storage system, Δu represents the control variables of the wind-storage system, Δy represents the increment of the state variables of the wind-storage system, N represents the number of wind turbines in the wind-storage system, and diag represents a diagonal matrix.

3. The frequency modulation method of the wind energy storage system according to claim 1, characterized in that: The specific expression of the frequency regulation optimization model of the wind storage system is: -(θ 0,i -θ min,i )<Δθ ref,i <(θ max,i -θ 0,i ); Among them, λ ωr represents the weight coefficient of speed control, p represents the number of sampling points, N represents the number of wind turbines in the wind energy storage system, Δω r,i (k) represents the speed change of the i-th fan at the k-th moment, Δω r,ave (k) represents the average value of the speed change of all fans at the kth moment, λ β1 represents the weight coefficient of pitch angle control, θ i (k) represents the pitch angle of the i-th wind turbine at the k-th moment, θ ave (k) represents the average pitch angle of all wind turbines at the kth moment, λ β2 Represents the weight coefficient of pitch angle change control, Δθ i (k) represents the pitch angle change of the i-th wind turbine at the k-th moment, λ E represents the weight coefficient of energy storage loss control, f L (k) represents the energy storage loss cost, P e0,i represents the initial value of electromagnetic power of the i-th fan, ΔP ref,i represents the frequency modulation power reference value of the i-th fan, represents the initial energy storage power of the i-th energy storage battery, represents the energy storage power increment reference value of the i-th energy storage battery, P max,i represents the rated capacity of the i-th fan, θ 0,i represents the initial pitch angle of the i-th wind turbine, θ min,i represents the minimum pitch angle of the i-th wind turbine, Δθ ref,i represents the reference value of the pitch angle change of the i-th wind turbine, θ max,i represents the maximum pitch angle of the i-th wind turbine, Indicates the upper limit of the discharge power of the energy storage battery. represents the energy storage power of the i-th energy storage battery, represents the energy storage power increment reference value of the i-th energy storage battery, Indicates the upper limit of the charging power of the energy storage battery, ΔP ESS,i Represents the power increment of the i-th energy storage battery, ΔP ref Indicates the frequency modulation power demand, K p Indicates the droop control coefficient of virtual inertia control, K d represents the inertia control coefficient of virtual inertia control, Δf represents the change of grid frequency, and t represents time.

4. The frequency modulation method of the wind energy storage system according to claim 1, characterized in that: The quadrog optimization solver provided by MATLAB is used to solve the frequency modulation optimization model of the wind storage system.

5. The frequency modulation method of a wind-storage system according to any one of claims 1 to 4, characterized in that: The frequency modulation power of the leading fan is calculated according to the current speed of the leading fan and the frequency modulation power reference value, including: Calculating a consistency state factor of the leading fan according to the current rotation speed of the leading fan; Performing PI adjustment on the consistency state factor of the leading fan to obtain a first PI adjustment result; Superimposing the first PI adjustment result and the frequency modulation power reference value of the leading wind turbine to obtain a superimposed result; Performing maximum power point tracking on the current rotation speed of the leading wind fan to obtain the maximum frequency modulation power of the leading wind fan; Superimposing the maximum frequency modulation power of the leading fan with the superposition result to obtain the frequency modulation power of the leading fan; Calculating the frequency modulation power of the non-dominant fan according to the current rotation speed of the non-dominant fan includes: Calculating a consistency state factor of the non-dominant fan according to the current rotation speed of the non-dominant fan; Performing PI adjustment on the consistency state factor of the non-dominant fan to obtain a second PI adjustment result; Performing maximum power point tracking on the current rotation speed of the non-dominant fan to obtain the maximum frequency modulation power of the non-dominant fan; The maximum frequency modulation power of the non-dominant fan is superimposed on the second PI adjustment result to obtain the frequency modulation power of the non-dominant fan.

6. The frequency modulation method of the wind energy storage system according to claim 5, characterized in that: The specific calculation formula of the consistency state factor is: Among them, c i represents the consistency state factor of the i-th fan, ω r0,i represents the initial speed value of the i-th fan, ω r,i represents the speed of the i-th fan, ω r,min Indicates the minimum speed of the fan.

7. The frequency modulation method of the wind energy storage system according to claim 5, characterized in that: The specific calculation formula of the second PI adjustment result is: Where ΔP nref,l represents the frequency modulation power reference value of the lth non-dominant wind turbine, k P,l and k I,l They represent the proportional gain and integral gain of the first non-dominant fan when performing PI regulation, c l represents the consistency state factor of the lth non-dominant wind turbine, c j represents the consistency state factor of the jth non-dominant wind turbine, and N represents the number of wind turbines in the wind-storage system.

8. An electronic device comprising a memory, a processor, and a computer program / instruction stored in the memory, characterized in that: The processor executes the computer program / instructions to implement the wind energy storage system frequency regulation method according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instruction is executed by a processor, the wind energy storage system frequency regulation method as described in any one of claims 1 to 7 is implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the wind energy storage system frequency regulation method as described in any one of claims 1 to 7 is implemented.

Images