Black-start power control method and device for net-forming type wind storage system and medium

Abstract

The invention relates to the technical field of operation control of a wind storage system, and discloses a black-start power control method and device for a network-forming type wind storage system and a medium. By adopting a robust model predictive control optimization method, the output power of the wind storage system can be adaptively adjusted on line according to the change of a black-start load; the supply and demand balance of the active power is maintained, and stable and rapid black start is realized. Meanwhile, the power change of the following grid type photovoltaic power in the black start process is regarded as uncertain disturbance, and the change is estimated through the constructed disturbance observer state equation, so that the accurate tracking of the power of the grid construction wind storage system is improved, the response speed is high, the method does not need to depend on the accurate prediction of the photovoltaic power, and the control accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of wind-storage system operation control, and in particular to a black start power control method, device and medium for a grid-type wind-storage system. Background Art

[0002] With the increase of new energy access, the uncertainty and complexity of the power grid increase, and black start technology can ensure that power supply is quickly re-established after system failure or catastrophic events, reduce interruption time, improve the resilience of the power grid, and enhance the stability of the power grid. Due to natural resource constraints, traditional black start power sources in areas with abundant wind and solar power are insufficient. Using wind storage systems as black start power sources can improve the black start capability of regional power grids.

[0003] At present, scholars have proposed a solution to use wind, solar and storage power generation systems as black start power sources, and to use model predictive control to achieve power coordination during the black start process. However, this solution only achieves power coordination between black start power sources, and also relies on accurate predictions of wind power and photovoltaic power. However, since the grid-building and grid-following units in new energy stations are often mixed in a certain proportion, the output power of the grid-following units may fluctuate during the black start process, which in turn causes the grid frequency and voltage to be unstable. Summary of the invention

[0004] In view of this, the present invention provides a black start power control method, device and medium for a grid-connected wind-storage system to solve the problem that the existing power coordination scheme during the black start process relies on the accurate prediction of wind power and photovoltaic power, but because the grid-connected and grid-following units in the new energy station are often mixed in a certain proportion, the output power of the grid-following units may fluctuate during the black start process, which in turn leads to the problem that the grid frequency and voltage cannot remain stable.

[0005] In a first aspect, the present invention provides a black start power control method for a grid-type wind-storage system, the method comprising:

[0006] The power change of grid-following photovoltaic during the black start process is taken as the uncertain disturbance of the grid-connected wind-storage system, and the black start prediction model of the grid-connected wind-storage system is obtained; the disturbance observer state equation is constructed based on the black start prediction model; based on the preset objective function and constraints, the robust model predictive control optimization problem is established using the disturbance observer state equation and the black start prediction model; the robust model predictive control optimization problem is solved to obtain the black start power control result of the grid-connected wind-storage system.

[0007] The black start power control method of the grid-type wind-storage system provided by the present invention can adaptively adjust the output power of the wind-storage system online according to the change of the black start load by adopting a robust model predictive control optimization method, maintain the supply and demand balance of active power, and achieve a stable and fast black start. At the same time, the power change of the grid-type photovoltaic during the black start process is regarded as an uncertain disturbance, and its change is estimated by the constructed disturbance observer state equation, which improves the accurate tracking of the power of the grid-type wind-storage system, has a fast response speed, does not need to rely on the accurate prediction of photovoltaic power, and improves the control accuracy.

[0008] In an optional implementation, the power change of the grid-following photovoltaic system during the black start process is used as the uncertain disturbance of the grid-connected wind-storage system, and a black start prediction model of the grid-connected wind-storage system is obtained, including:

[0009] The linearized model of wind turbines in the grid-connected wind-storage system is obtained. The power change of grid-connected photovoltaic power during the black start process is taken as the uncertain disturbance of the grid-connected wind-storage system, and a black start prediction model of the grid-connected wind-storage system is established based on the linearized model of wind turbines.

[0010] The black start power control method for a grid-connected wind-storage system provided by the present invention regards the power change of the grid-connected photovoltaic system during the black start process as an uncertain disturbance, and can establish a corresponding black start prediction model in combination with the linearization model of the wind turbines in the grid-connected wind-storage system, thereby providing support for subsequently improving the power control accuracy of the grid-connected wind-storage system.

[0011] In an optional implementation, obtaining a linearized model of a wind turbine generator set of a grid-type wind-storage system includes:

[0012] Obtain the operation mechanism of wind turbines in the grid-type wind storage system; establish a linear model of wind turbines based on the operation mechanism of wind turbines.

[0013] The black start power control method of the grid-type wind-storage system provided by the present invention can establish a linear model of the wind turbine under a certain wind speed by combining the operation mechanism of the wind turbine generator set of the grid-type wind-storage system, thereby providing support for the establishment of a black start prediction model.

[0014] In an optional implementation, based on a preset objective function and constraints, a robust model predictive control optimization problem is established using a disturbance observer state equation and a black start prediction model, including:

[0015] Based on the disturbance observer state equation and the black start prediction model, the estimated value is determined; based on the preset objective function and constraints, the robust model predictive control optimization problem is established using the estimated value and the black start prediction model.

[0016] In an optional implementation, determining the estimated value based on the disturbance observer state equation and the black start prediction model includes:

[0017] Based on the disturbance observer state equation and the black start prediction model, the estimation error dynamic equation is established; the preset unconstrained linear quadratic regulator problem is solved to obtain the target feedback gain; the target matrix is ​​determined based on the target feedback gain; and the estimated value is determined based on the estimation error dynamic equation and the target matrix.

[0018] In an optional implementation, based on a preset objective function and constraints, before establishing a robust model predictive control optimization problem using a disturbance observer state equation and a black start prediction model, the method further includes:

[0019] Obtain the reliability index and economic index of the grid-type wind-storage system; determine the preset objective function based on the reliability index and economic index.

[0020] The black start power control method of the grid-type wind-storage system provided by the present invention determines the corresponding optimization objective function by considering the reliability and economic requirements of the grid-type wind-storage system.

[0021] In a second aspect, the present invention provides a black start power control device for a grid-type wind-storage system, the device comprising:

[0022] The first acquisition module is used to take the power change of the grid-type wind-storage system during the black start process as the uncertain disturbance of the grid-type wind-storage system, and obtain the black start prediction model of the grid-type wind-storage system; the construction module is used to construct the disturbance observer state equation based on the black start prediction model; the establishment module is used to establish a robust model predictive control optimization problem based on the preset objective function and constraints using the disturbance observer state equation and the black start prediction model; the solution module is used to solve the robust model predictive control optimization problem to obtain the black start power control result of the grid-type wind-storage system.

[0023] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the black start power control method for a grid-connected wind storage system according to the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0024] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the black start power control method for a grid-type wind-storage system according to the first aspect or any corresponding embodiment thereof.

[0025] In a fifth aspect, the present invention provides a computer program product, including computer instructions, which are used to enable a computer to execute the black start power control method for a grid-type wind-storage system according to the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0027] Figure 1 1 is a flow chart of a black start power control method for a grid-type wind-storage system according to an embodiment of the present invention;

[0028] Figure 2 is a structural schematic diagram of a grid-type wind storage system according to an embodiment of the present invention;

[0029] Figure 3 is a schematic diagram of the structure of a disturbance observer according to an embodiment of the present invention;

[0030] Figure 4 is a flow chart of another black start power control method for a grid-type wind-storage system according to an embodiment of the present invention;

[0031] Figure 5 is a flow chart of another black start power control method for a grid-type wind-storage system according to an embodiment of the present invention;

[0032] Figure 6 is a structural block diagram of a black start power control device for a grid-type wind-storage system according to an embodiment of the present invention;

[0033] Figure 7 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0035] An embodiment of the present invention provides a black start power control method for a grid-type wind-storage system, which achieves a stable and fast black start through a robust model predictive control optimization method. At the same time, the power change of the grid-type wind-storage system during the black start process is regarded as an uncertain disturbance, and its change is estimated through a constructed disturbance observer state equation to achieve the effect of improving control accuracy.

[0036] According to an embodiment of the present invention, an embodiment of a black start power control method for a grid-type wind-storage system is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0037] In this embodiment, a black start power control method for a grid-type wind-storage system is provided, which can be used in electronic devices such as computers, mobile phones, tablet computers, etc. Figure 1 is a flow chart of a black start power control method for a grid-connected wind-storage system according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0038] Step S101, taking the power change of the grid-following photovoltaic system during the black start process as the uncertain disturbance of the grid-connected wind-storage system, and obtaining a black start prediction model of the grid-connected wind-storage system.

[0039] Since the grid-building and grid-following units in new energy stations are often mixed in a certain proportion, the output power of the grid-following units may fluctuate during the black start process. Therefore, the power of the grid-building units needs to change adaptively to coordinate with them in order to maintain the stability of the grid frequency and voltage.

[0040] Specifically, considering that there are grid-connected new energy units of a certain capacity in the grid-connected wind-storage system (taking grid-connected photovoltaic as an example in this embodiment), the power change in the black start process is regarded as an uncertain disturbance of the grid-connected wind-storage system, and a black start prediction model of the grid-connected wind-storage system is further obtained.

[0041] Among them, the structure of the grid-type wind storage system is as follows: Figure 2 As shown. Among them, K Bi Represents the frequency offset factor; ACE i represents the regional control deviation; Δβ represents the pitch angle change; ΔT g Indicates the change in generator torque; ΔP di represents the controllable load variation of the grid-type wind-storage system; Δf i Indicates the change in grid frequency; ΔP B Indicates the change in energy storage output power; ΔP PV Indicates the change in grid-following photovoltaic output power; KPi represents the power system gain; T ESS Represents the time constant of the energy storage system; K vi Indicates the virtual inertia control gain.

[0042] Furthermore, the black start prediction model can be expressed as the following equation (1):

[0043]

[0044] Among them, the state variable x, control variable u, disturbance variable w and output variable y are expressed as the following equations (2) to (5):

[0045] x=[Δf i E B ΔP B Δω g Δω r Δθ] T (2)

[0046] u=[ΔβΔT g ] T (3)

[0047] w=[ΔP di ΔP PV ] T (4)

[0048] y=ACE i (5)

[0049] Where: E B Indicates the real-time energy storage capacity; Δω g Indicates the change in generator speed; Δω r It indicates the change of the fan rotor speed; Δθ indicates the change of the transmission shaft torsion angle.

[0050] Furthermore, the system matrices A, B, F, and C are expressed as the following equations (6) to (9):

[0051]

[0052]

[0053]

[0054] C=[K Bi 00000](9)

[0055] Where: η g represents the generator power conversion efficiency; η represents the energy storage conversion efficiency; D vi represents the virtual damping constant; K θIndicates the stiffness coefficient of the fan transmission system; N g Indicates the gearbox speed ratio; T r Indicates the fan torque; T pi Represents the power system time constant; T g,0 Indicates the rated torque of the generator corresponding to the predicted wind speed; ΔT B Indicates the conversion coefficient of energy storage power to energy; B θ Indicates the damping coefficient of the fan transmission system; J g Indicates the moment of inertia of the high-speed shaft; J r Represents the moment of inertia of the low-speed shaft; ω g,0 Indicates the rated speed of the generator corresponding to the predicted wind speed.

[0056] Step S102: constructing a disturbance observer state equation based on the black start prediction model.

[0057] Specifically, according to the black start prediction model shown in the above relationship (1), a disturbance observer form, that is, a disturbance observer state equation, can be designed, as shown in the following relationship (10):

[0058]

[0059]

[0060] Where: L and H represent the observer gain matrix; Indicates the corresponding load change P in the disturbance matrix F di The block matrix of; D represents the block matrix corresponding to the photovoltaic power disturbance in the disturbance matrix F; and Respectively represent the estimated values ​​of system state quantity and output quantity; represents the estimated value of the PV power disturbance.

[0061] Furthermore, the structure of the disturbance observer is as follows: Figure 3 shown.

[0062] Step S103, based on the preset objective function and constraints, using the disturbance observer state equation and the black start prediction model, establish a robust model predictive control optimization problem.

[0063] Among them, the preset objective function can reflect the reliability and economy requirements of the grid-type wind-storage system during the black start process.

[0064] Furthermore, there are certain physical constraints in the actual operation of wind turbines and battery energy storage, that is, the constraint conditions are expressed as the following relationship (11):

[0065] 0≤ω g ≤ω g,max ,0≤β≤90,0≤Tg ≤T g,max ,SOC min ≤SOC≤SOC max (11)

[0067] Where: SOC represents the battery state of charge; SOC min Indicates the minimum battery state of charge value; SOC max Indicates the maximum battery state of charge value.

[0068] Furthermore, under the constraints, taking the objective function as the optimization goal, the corresponding robust model predictive control optimization problem can be established by combining the disturbance observer state equation and the black start prediction model.

[0069] Step S104, solving the robust model predictive control optimization problem to obtain the black start power control result of the grid-connected wind-storage system.

[0070] Specifically, the three elements of model predictive control are prediction model, rolling optimization and feedback correction. At time k, the prediction sequence in the prediction time domain is obtained through the prediction model. Then, the optimal problem in the control time domain is solved online according to the prediction sequence, and the optimal control sequence of wind, solar and storage black start in the control time domain is obtained. Only the optimal control command of the first time interval in the control time domain is executed. Finally, the actual value is used as feedback to update the state variables of the system and repeat the above process until the black start power control result of the grid-type wind and storage system is obtained.

[0071] The black start power control method of the grid-type wind-storage system provided in this embodiment can adjust the output power of the wind-storage system online and adaptively according to the change of the black start load by adopting a robust model predictive control optimization method, maintain the supply and demand balance of active power, and achieve a stable and fast black start. At the same time, the power change of the grid-type photovoltaic during the black start process is regarded as an uncertain disturbance, and its change is estimated by the constructed disturbance observer state equation, which improves the accurate tracking of the power of the grid-type wind-storage system, has a fast response speed, does not need to rely on the accurate prediction of photovoltaic power, and improves the control accuracy.

[0072] In this embodiment, a black start power control method for a grid-type wind-storage system is provided, which can be used in electronic devices such as computers, mobile phones, tablet computers, etc. Figure 4 is a flow chart of a black start power control method for a grid-connected wind-storage system according to an embodiment of the present invention. Figure 4 As shown, the process includes the following steps:

[0073] Step S401, taking the power change of the grid-following photovoltaic system during the black start process as the uncertain disturbance of the grid-connected wind-storage system, and obtaining a black start prediction model of the grid-connected wind-storage system.

[0074] Specifically, the above step S401 includes:

[0075] Step S4011, obtaining a linearized model of a wind turbine generator system of a grid-type wind-storage system.

[0076] Specifically, the dynamic characteristics of a wind turbine are usually described by a series of nonlinear equations. The linearization process is to perform Taylor series expansion on these nonlinear equations near the operating point and ignore high-order terms to obtain linearized equations.

[0077] For example, the mechanical power output of a wind turbine can be expressed as a function of variables such as wind speed and pitch angle. At a specific operating point, it is linearized to obtain a linear relationship with respect to small changes in these variables.

[0078] Furthermore, the linearized model of the wind turbine in the grid-type wind-storage system represents a model obtained by linearly approximating the complex nonlinear dynamic characteristics of the wind turbine near a specific working point.

[0079] In some optional implementations, the above step S4011 includes:

[0080] Step a1, obtaining the operation mechanism of the wind turbine generator set of the grid-type wind storage system.

[0081] Step a2: establishing a linear model of the wind turbine generator set based on the operation mechanism of the wind turbine generator set.

[0082] Specifically, according to the operating mechanism of the wind turbine, a linearized model of the wind turbine under a certain wind speed can be established. The wind turbine adopts a combination of torque control and pitch angle control to achieve output power regulation; the energy storage system adopts virtual inertia control technology, so that it can change the output power in response to changes in grid frequency.

[0083] Step S4012, taking the power change of the grid-following photovoltaic system during the black start process as the uncertain disturbance of the grid-connected wind-storage system, and establishing a black start prediction model of the grid-connected wind-storage system based on the linearized model of the wind turbine.

[0084] Specifically, considering that there are grid-connected new energy units of a certain capacity in the grid-connected wind-storage system (taking grid-connected photovoltaic as an example in this embodiment), the power change during the black start process is regarded as an uncertain disturbance of the grid-connected wind-storage system.

[0085] Furthermore, the linear model of the wind turbine is part of the black start prediction model, and the black start prediction model also includes the state quantity of the energy storage system. Therefore, the black start prediction model of the grid-type wind-storage system shown in the above relationship (1) can be established by combining the linear model of the wind turbine and the state quantity of the energy storage system.

[0086] Step S402: construct a disturbance observer state equation based on the black start prediction model. Figure 1 Step S102 of the illustrated embodiment will not be described in detail here.

[0087] Step S403, based on the preset objective function and constraints, using the disturbance observer state equation and the black start prediction model, establish a robust model predictive control optimization problem.

[0088] Specifically, the above step S403 includes:

[0089] Step S4031, determining an estimated value based on the disturbance observer state equation and the black start prediction model.

[0090] Specifically, the error dynamics can be estimated and the corresponding estimated values ​​can be determined by combining the disturbance observer state equation and the black start prediction model.

[0091] In some optional implementations, the above step S4031 includes:

[0092] Step b1, establishing an estimation error dynamic equation based on the disturbance observer state equation and the black start prediction model.

[0093] Step b2, solving the preset unconstrained linear quadratic regulator problem to obtain the target feedback gain.

[0094] Step b3, determining the target matrix based on the target feedback gain.

[0095] Step b4, determining the estimated value based on the estimated error dynamic equation and the target matrix.

[0096] Specifically, the state estimation error is defined as and disturbance estimation error

[0097] Further, according to the black start prediction model shown in the above relation (1) and the disturbance observer state equation shown in the above relation (10), the estimation error dynamics can be expressed as the following relations (12) and (13):

[0098]

[0099] Furthermore, we define the augmented error Then, from the above equations (12) and (13), we can obtain the following equation (14):

[0100]

[0101] in:

[0102]

[0103]

[0104]

[0105] Furthermore, in order to make the estimated value of the observer gradually approach the true value, the estimation error needs to decay over time, that is, it is necessary to find a suitable matrix Make Stablize.

[0106] Furthermore, given the matrix The transposed matrix of is as follows (18):

[0107]

[0108] Furthermore, since matrix transposition does not change the eigenvalues, we can solve the matrix Determine the matrix The value of .

[0109] Specifically, the transposed matrix Can be regarded as a system The state feedback matrix can make the error shown in the above relationship (5) dynamically stable feedback gain It can be obtained by solving the unconstrained linear quadratic regulator (LQR) problem shown in the following equation (19):

[0110]

[0111] Where: The solution of the Riccati equation is shown in the following relation (20):

[0112]

[0113] Where:

[0114] Furthermore, the corresponding photovoltaic power estimation value can be obtained in real time through the above process.

[0115] Step S4032, based on the preset objective function and constraints, using the estimated value and the black start prediction model, establish a robust model predictive control optimization problem.

[0116] Specifically, the robust model predictive control optimization problem is expressed as follows:

[0117]

[0118] stx min ≤x≤x max ,u min ≤u≤u max (twenty two)

[0119] x(k+1)=Ax(k)+Bu(k)+Fw(k)(23)

[0120] Among them, l e (k) represents the preset objective function; N p Represents the prediction time domain; N c represents the control time domain; x 0 Indicates the reference value of the state quantity; Q i and R i The weighted matrices represent the state quantity and the control quantity, respectively. Furthermore, in addition to the control target corresponding to the state quantity, the preset objective function also limits the size of the control increment to reduce the mechanical fatigue loss of the wind turbine. w(k) contains the photovoltaic power estimation value obtained in real time by the disturbance observer By introducing real-time estimated information of photovoltaic power changes, the controller can feedback and correct the predicted value of the wind storage system state during the black start process, thereby improving the anti-interference performance of the control system.

[0121] Step S404, solve the robust model predictive control optimization problem to obtain the black start power control result of the grid-connected wind-storage system. Figure 1 Step S104 of the illustrated embodiment will not be described in detail here.

[0122] The black start power control method for the grid-type wind-storage system provided in this embodiment regards the power change of the grid-type photovoltaic during the black start process as an uncertain disturbance, and can establish a corresponding black start prediction model in combination with the linearized model of the wind turbine of the grid-type wind-storage system. Further, a disturbance observer state equation is constructed in combination with the black start prediction model and a robust model predictive control optimization problem is further established. Finally, the black start power control result of the grid-type wind-storage system can be obtained by solving the robust model predictive control optimization problem. By adopting the robust model predictive control optimization method, the output power of the wind-storage system can be adjusted online and adaptively according to the change of the black start load, the supply and demand balance of active power can be maintained, and a stable and rapid black start can be achieved. At the same time, the power change of the grid-type photovoltaic during the black start process is regarded as an uncertain disturbance, and its change is estimated by the constructed disturbance observer state equation, which improves the accurate tracking of the power of the grid-type wind-storage system, has a fast response speed, does not need to rely on the accurate prediction of photovoltaic power, and improves the control accuracy.

[0123] In this embodiment, a black start power control method for a grid-type wind-storage system is provided, which can be used in electronic devices such as computers, mobile phones, tablet computers, etc. Figure 5is a flow chart of a black start power control method for a grid-connected wind-storage system according to an embodiment of the present invention. Figure 5 As shown, the process includes the following steps:

[0124] Step S501, taking the power change of the grid-following photovoltaic system during the black start process as the uncertain disturbance of the grid-connected wind-storage system, and obtaining the black start prediction model of the grid-connected wind-storage system. Figure 4 Step S401 of the illustrated embodiment will not be described in detail here.

[0125] Step S502: construct a disturbance observer state equation based on the black start prediction model. Figure 1 Step S102 of the illustrated embodiment will not be described in detail here.

[0126] Step S503, obtaining reliability index and economic index of the grid-type wind-storage system.

[0127] Specifically, by coordinating the output power of wind turbines and battery energy storage, the supply and demand balance of power and load during the black start process is maintained, and the reliability index l that characterizes this performance is e1 The specific form of is shown in the following relation (24):

[0128] l e1 =Q e1 (Δf i (k)) 2 (twenty four)

[0129] Where: Q e1 Indicates the weight coefficient of frequency deviation.

[0130] Furthermore, for the sake of battery safety, battery energy storage cannot be fully charged and discharged. Excessive discharge depth will accelerate battery aging and reduce battery life. Taking 0.5 as the initial value of the battery state of charge, it is required that the battery state of charge during the frequency modulation process is as close to its initial value as possible. The economic index that characterizes this performance is l e2 The specific form is shown in the following relation (25):

[0131] l e2 =Q e2 (SOC(k)-SOC 0 ) 2 (25)

[0132] Where: SOC(k) represents the current state of charge of the energy storage unit at time k; SOC 0 Indicates the initial charge state of the energy storage unit; Q e2 The weight factor representing the state of charge.

[0133] Furthermore, corresponding to the system model, SOC(k)=EB (k) / E B0 , which is the ratio of the current power of the energy storage to the rated capacity.

[0134] Step S504: determining a preset objective function based on the reliability index and the economic index.

[0135] Specifically, the preset objective function l e It is expressed as the following relation (26):

[0136] l e = l e1 +l e2 (26)

[0137] Step S505: Based on the preset objective function and constraints, a robust model predictive control optimization problem is established using the disturbance observer state equation and the black start prediction model. Figure 4 Step S403 of the illustrated embodiment will not be described in detail here.

[0138] Step S506, solve the robust model predictive control optimization problem to obtain the black start power control result of the grid-connected wind-storage system. Figure 1 Step S104 of the illustrated embodiment will not be described in detail here.

[0139] The black start power control method for the grid-type wind-storage system provided in this embodiment determines a preset objective function by considering the reliability and economy requirements of the grid-type wind-storage system during the black start process. Furthermore, a robust model predictive control optimization problem is established with the preset objective function as the target. Furthermore, by adopting the robust model predictive control optimization method, the output power of the wind-storage system can be adaptively adjusted online according to the change of the black start load, the supply and demand balance of active power can be maintained, and a stable and rapid black start can be achieved. At the same time, the power change of the grid-type photovoltaic during the black start process is regarded as an uncertain disturbance, and its change is estimated by the constructed disturbance observer state equation, which improves the accurate tracking of the power of the grid-type wind-storage system, has a fast response speed, does not need to rely on the accurate prediction of photovoltaic power, and improves the control accuracy.

[0140] In this embodiment, a black start power control device for a grid-type wind storage system is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and will not be repeated hereafter. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0141] This embodiment provides a black start power control device for a grid-type wind-storage system, such as Figure 6 As shown, the device comprises:

[0142] The first acquisition module 601 is used to use the power change of the grid-following photovoltaic system during the black start process as the uncertain disturbance of the grid-connected wind-storage system, and to obtain a black start prediction model of the grid-connected wind-storage system.

[0143] The construction module 602 is used to construct a disturbance observer state equation based on the black start prediction model.

[0144] Establishing module 603 is used to establish a robust model predictive control optimization problem based on a preset objective function and constraints using a disturbance observer state equation and a black start prediction model.

[0145] The solution module 604 is used to solve the robust model predictive control optimization problem and obtain the black start power control result of the grid-connected wind-storage system.

[0146] In some optional implementations, the first acquisition module 601 includes:

[0147] The acquisition submodule is used to obtain the linearized model of the wind turbine in the grid-type wind storage system.

[0148] The first submodule is used to establish a black start prediction model for the grid-connected wind storage system based on the power change of the grid-connected photovoltaic system during the black start process as the uncertain disturbance of the grid-connected wind storage system and based on the linearized model of the wind turbine.

[0149] In some optional implementations, the acquisition submodule includes:

[0150] The acquisition unit is used to acquire the operation mechanism of the wind turbine generator set of the grid-type wind storage system.

[0151] The first establishing unit is used to establish a linear model of the wind turbine generator set based on the operating mechanism of the wind turbine generator set.

[0152] In some optional implementations, the establishing module 603 includes:

[0153] The determination submodule is used to determine the estimated value based on the disturbance observer state equation and the black start prediction model.

[0154] The second submodule is used to establish a robust model predictive control optimization problem based on a preset objective function and constraints using estimated values ​​and a black start prediction model.

[0155] In some optional implementations, the determining submodule includes:

[0156] The second establishing unit is used to establish an estimation error dynamic equation based on the disturbance observer state equation and the black start prediction model.

[0157] The solving unit is used to solve the preset unconstrained linear quadratic regulator problem to obtain the target feedback gain.

[0158] The first determining unit is used to determine a target matrix based on a target feedback gain.

[0159] The second determination unit is used to determine the estimated value based on the estimation error dynamic equation and the target matrix.

[0160] In some optional embodiments, the device comprises:

[0161] The second acquisition module is used to obtain the reliability index and economic index of the grid-type wind storage system.

[0162] The determination module is used to determine a preset objective function based on reliability indicators and economic indicators.

[0163] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0164] The black start power control device of the grid-type wind storage system in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0165] The embodiment of the present invention also provides a computer device having the above Figure 6 The black start power control device of the grid-type wind storage system is shown.

[0166] See also Figure 7 , Figure 7 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Figure 7 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 7A processor 10 is taken as an example.

[0167] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0168] The memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes the method shown in the above embodiment.

[0169] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0170] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0171] The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0172] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0173] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

[0174] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A black start power control method for a grid-type wind-storage system, characterized in that: The method comprises: Taking the power change of the grid-following photovoltaic system during the black start process as the uncertain disturbance of the grid-connected wind-storage system, and obtaining the black start prediction model of the grid-connected wind-storage system; Constructing a disturbance observer state equation based on the black start prediction model; Based on the preset objective function and constraints, a robust model predictive control optimization problem is established using the disturbance observer state equation and the black start prediction model; The robust model predictive control optimization problem is solved to obtain the black start power control result of the grid-type wind storage system.

2. The method according to claim 1, characterized in that The power change of the grid-following photovoltaic system during the black start process is used as the uncertain disturbance of the grid-connected wind-storage system, and a black start prediction model of the grid-connected wind-storage system is obtained, including: Obtaining a linearized model of a wind turbine generator set of the grid-type wind storage system; The power change of the grid-following photovoltaic system during the black start process is taken as the uncertain disturbance of the grid-connected wind storage system, and based on the linearized model of the wind turbine set, the black start prediction model of the grid-connected wind storage system is established.

3. The method according to claim 2, characterized in that Obtaining a linearized model of a wind turbine generator set of the grid-type wind storage system includes: Obtaining the operation mechanism of the wind turbine generator set of the grid-type wind storage system; A linearized model of the wind turbine generator set is established based on the operating mechanism of the wind turbine generator set.

4. The method according to claim 1, characterized in that: Based on the preset objective function and constraint conditions, a robust model predictive control optimization problem is established by using the disturbance observer state equation and the black start prediction model, including: Determining an estimated value based on the disturbance observer state equation and the black start prediction model; Based on the preset objective function and the constraint conditions, the robust model predictive control optimization problem is established using the estimated value and the black start prediction model.

5. The method according to claim 4, characterized in that Determining an estimated value based on the disturbance observer state equation and the black start prediction model includes: Based on the disturbance observer state equation and the black start prediction model, establishing an estimation error dynamic equation; Solve the preset unconstrained linear quadratic regulator problem and obtain the target feedback gain; determining a target matrix based on the target feedback gain; Based on the estimation error dynamic equation and the target matrix, the estimation value is determined.

6. The method according to claim 1, characterized in that Based on the preset objective function and constraints, before establishing the robust model predictive control optimization problem using the disturbance observer state equation and the black start prediction model, the method further includes: Obtaining reliability index and economic index of the grid-type wind storage system; Based on the reliability index and the economic index, the preset objective function is determined.

7. A black start power control device for a grid-type wind-storage system, characterized in that: The device comprises: A first acquisition module is used to use the power change of the grid-following photovoltaic system during the black start process as the uncertain disturbance of the grid-connected wind-storage system, and to acquire a black start prediction model of the grid-connected wind-storage system; A construction module, used for constructing a disturbance observer state equation based on the black start prediction model; Establishing a module for establishing a robust model predictive control optimization problem based on a preset objective function and constraints using the disturbance observer state equation and the black start prediction model; A solution module is used to solve the robust model predictive control optimization problem to obtain a black start power control result of the grid-type wind storage system.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the black start power control method for a grid-type wind storage system according to any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the black start power control method for a grid-type wind-storage system according to any one of claims 1 to 6.

10. A computer program product, characterized in that It includes computer instructions, which are used to enable a computer to execute the black start power control method of the grid-type wind-storage system according to any one of claims 1 to 6.

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