A robust frequency control method for microgrid based on wind turbines and electric vehicles

Abstract

The present invention relates to a microgrid robust frequency control method based on wind turbines and electric vehicles, comprising the following steps: establishing a small signal model of wind turbines participating in frequency modulation based on load shedding control of wind turbines; constructing a unified control model of electric vehicles based on energy aggregators; establishing a microgrid dynamic frequency response model based on the small signal model of wind turbines and the unified control model of electric vehicles; determining a performance weighting function and a control weighting function according to a design goal, and solving H based on a linear matrix inequality method. ∞ Robust frequency controller. This method fully exploits the frequency regulation potential of wind turbines and electric vehicles, while using H ∞ Robust control suppresses the adverse effects of uncertainty and external disturbances on frequency modulation, and reduces the frequency disturbance of the microgrid under power fluctuations.

Description

Technical Field

[0001] The invention relates to a microgrid robust frequency control method based on a wind turbine generator set and an electric vehicle, and belongs to the technical field of microgrid frequency control. Background Art

[0002] A microgrid is a small power generation and distribution system consisting of distributed power sources, loads, and monitoring and protection devices. The introduction of the microgrid concept provides a new way for new energy sources such as wind power to access the power system. The power generated by wind turbines is volatile, and they are connected to the microgrid through power electronic converters. The mechanical rotor speed is decoupled from the system frequency, which will have an adverse effect on the frequency regulation of the microgrid. Therefore, it is urgent to involve wind turbines in the system frequency regulation. Electric vehicles have the characteristics of energy storage, and there have been studies on the application of electric vehicles in microgrid frequency regulation.

[0003] The existing technology does not consider how to overcome the impact of wind power fluctuations and randomness of electric vehicle charging and discharging on frequency modulation, nor does it take into account the adverse effects of wind turbines participating in frequency modulation on the wind turbines themselves, such as aggravating subsynchronous oscillations, increasing mechanical wear, shortening wind turbine life, etc. Therefore, it is extremely practical to study how to coordinate frequency modulation control of wind turbines and electric vehicles while reducing the adverse effects of uncertainty and external disturbances. Summary of the invention

[0004] In order to overcome the above problems, the present invention provides a microgrid robust frequency control method based on wind turbines and electric vehicles. The method fully taps the frequency modulation potential of wind turbines and electric vehicles and utilizes H ∞ Robust control suppresses the adverse effects of uncertainty and external disturbances on frequency modulation, and reduces the frequency disturbance of the microgrid under power fluctuations.

[0005] The technical solution of the present invention is as follows:

[0006] A robust frequency control method for a microgrid based on a wind turbine and an electric vehicle comprises the following steps:

[0007] A small signal model of wind turbines participating in frequency regulation is established based on load shedding control of wind turbines;

[0008] Constructing a unified control model for electric vehicles based on energy aggregators;

[0009] A dynamic frequency response model of the microgrid is established based on the small signal model of the wind turbine and the unified control model of the electric vehicle;

[0010] According to the design objectives, the performance weighting function and the control weighting function are determined, and the H is solved based on the linear matrix inequality method. ∞ Robust frequency controller.

[0011] Furthermore, the load shedding control based on the wind turbine generator set establishes a small signal model of the wind turbine generator set participating in the frequency modulation, specifically:

[0012] The rotor motion equation of the fan operating in the load reduction mode is established as follows:

[0013]

[0014] P e =K del ω 3 +P reg

[0015]

[0016] Where ρ is the air density; A is the windward swept area of ​​the wind turbine blade; C p (λ,0) is the wind energy utilization coefficient, where λ is the tip speed ratio; v w is the actual wind speed; P m , P e , C pref , ω and ν are the per unit values ​​of the wind turbine’s mechanical power, electromagnetic power, rotor angular velocity, wind energy utilization coefficient and external wind speed respectively; K del H is the power reduction factor of the fan; t is the inertia time constant of the fan; t is time; P B is the reference power; K p is the proportionality coefficient; P reg is the per unit value of the compensation power used to adjust the system frequency;

[0017] The rotor motion equation of the fan operating in the load reduction mode is linearized by using a small signal analysis method to obtain the following equation:

[0018] ΔP m =A1·Δβ+A2·Δω+A3·Δv

[0019]

[0020]

[0021]

[0022] ΔP e =3K del ω 2 Δω+ΔP reg

[0023] ΔP m -ΔP e =2H t ωdΔω / dt;

[0024] Where ΔP m , Δβ, Δω, Δv are the changes of mechanical power, pitch angle, rotor angular velocity and external wind speed of the wind turbine respectively; A1, A2, A3 are the intermediate variables used in the derivation; K λ C pref The partial derivative with respect to λ, K β C pref Partial derivative with respect to β;

[0025] According to the linearized rotor motion equation, the small signal model of the variable speed wind turbine under overspeed load reduction state is obtained by eliminating the intermediate variables as follows:

[0026]

[0027] ΔP e =3K del ω 2 Δω+ΔP reg ;

[0028] Where ΔP e , ΔP reg are the changes of fan electromagnetic power and compensation power respectively.

[0029] Furthermore, the unified control model of electric vehicles is constructed based on the energy aggregator, specifically:

[0030] The first-order dynamic model of a single electric vehicle is established as follows:

[0031]

[0032] Where ΔP EV,i The active power generated by the i-th electric vehicle; ΔP rev,i is the power reference received by the controller of the i-th electric vehicle; i is the access status signal of the i-th electric vehicle. When the electric vehicle is connected to the grid, its value is 1, otherwise it is 0; SOC i The charging and discharging status of the i-th electric vehicle; K EV,i and T EV,i are the gain coefficient and inertia time constant of the i-th electric car respectively; s is the Laplace operator.

[0033] According to the first-order dynamic model of the single electric vehicle, the active power ΔP of all the electric vehicle energy aggregators that can be connected is obtained. EV ,as follows:

[0034]

[0035] Among them, n is the maximum number of electric vehicles that can be connected;

[0036] Let the inertia time constant of all electric vehicles be equal to T EV ;

[0037] Power reference value ΔP of energy aggregator rev as follows:

[0038]

[0039] The unified control model of electric vehicles is obtained as follows:

[0040]

[0041] Among them, K EV is the equivalent electric vehicle gain coefficient, expressed as follows:

[0042]

[0043] Furthermore, in the microgrid dynamic frequency response model established based on the small signal model of the wind turbine and the unified control model of the electric vehicle, after the wind turbine and the electric vehicle coordinate the frequency modulation, the state space modeling method is used to establish the microgrid dynamic frequency response model including the wind turbine, photovoltaic, diesel engine and electric vehicle:

[0044]

[0045] y = Cx;

[0046] Where A is the state matrix, B1 and B2 are the disturbance input matrix and control input matrix respectively, and C is the output matrix. External disturbance d = [ΔP PV ΔP L Δv] T , control input u=[ΔP reg ΔP rev ] T , measured output y = Δf, state variable x = [Δf ΔP DG X G ACE Δω ΔP EV ] T ; ΔP DG , ΔP PV , and ΔP L is the power fluctuation of diesel engine, photovoltaic and load; Δf is the frequency deviation of the system; X G is the speed regulator output; ACE is the ACE control output; ΔP L is the load power fluctuation. The specific expressions of A, B1, B2, and C are as follows:

[0047]

[0048]

[0049] C = [1 0 0 0 0 0];

[0050] M and D are the inertia constant and damping coefficient of the system, respectively, and are perturbation parameters; T t is the time constant of the diesel generator; T g is the speed regulator time constant; R is the speed regulation coefficient; K E is the integral control gain.

[0051] Furthermore, the design goal is to find a feasible controller in the presence of external disturbances to reasonably coordinate the output of variable-speed wind turbines and electric vehicles, so that the closed-loop control system is stable and the frequency fluctuation of the microgrid under external disturbances is reduced.

[0052] Furthermore, the input signal includes a disturbance signal and a reference input signal. The reference signal is subtracted from the sum of the disturbance signal and the measured output signal to obtain an error signal. The error signal is input into the designed controller to obtain a control output signal. The controller input signal is calculated by a microgrid state space model to obtain a measured output signal. The product of the measured output signal and a performance weighted function is added to the product of the control output signal and the control weighted function to obtain a performance output signal.

[0053] Furthermore, the performance weighting function W e and W u The control weighting function is as follows:

[0054]

[0055]

[0056] Furthermore, H ∞ The problem of solving the robust frequency controller is transformed into the following solution of H ∞ Norm optimization problem:

[0057] ||T zw (s)|| ∞ ≤1;

[0058] The H ∞ The norm optimization problem is transformed into finding a symmetric positive definite matrix P that satisfies:

[0059]

[0060] The above inequality is transformed into a linear matrix inequality by using the substitution method to obtain the H ∞ Solution of the robust frequency controller.

[0061] The present invention has the following beneficial effects:

[0062] This method fully exploits the frequency regulation potential of wind turbines and electric vehicles, while using H ∞ Robust control suppresses the adverse effects of uncertainty and external disturbances on frequency modulation, and reduces the frequency disturbance of the microgrid under power fluctuations. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 This is a diagram of a dynamic frequency response model of a microgrid according to an embodiment of the present invention.

[0064] Figure 2 The figure is a block diagram of a closed-loop control system according to an embodiment of the present invention.

[0065] Figure 3 This is a frequency fluctuation diagram of a microgrid according to an embodiment of the present invention. DETAILED DESCRIPTION

[0066] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0067] Embodiment 1

[0068] A robust frequency control method for a microgrid based on a wind turbine and an electric vehicle comprises the following steps:

[0069] A small signal model of wind turbines participating in frequency regulation is established based on load shedding control of wind turbines;

[0070] Constructing a unified control model for electric vehicles based on energy aggregators;

[0071] A dynamic frequency response model of the microgrid is established based on the small signal model of the wind turbine and the unified control model of the electric vehicle;

[0072] According to the design objectives, the performance weighting function and the control weighting function are determined, and the H is solved based on the linear matrix inequality method. ∞ Robust frequency controller.

[0073] This method fully exploits the frequency regulation potential of wind turbines and electric vehicles, while using H ∞ Robust control suppresses the adverse effects of uncertainty and external disturbances on frequency modulation, and reduces the frequency disturbance of the microgrid under power fluctuations.

[0074] Embodiment 2

[0075] refer to Figure 1-2 , a microgrid robust frequency control method based on wind turbines and electric vehicles, based on the first embodiment, the load reduction control based on the wind turbines establishes a small signal model of the wind turbines participating in the frequency modulation, specifically:

[0076] The rotor motion equation of the fan operating in the load reduction mode is established as follows:

[0077]

[0078] P e =K del ω 3 +P reg

[0079]

[0080] Where ρ is the air density; A is the windward swept area of ​​the wind turbine blade; C p (λ,0) is the wind energy utilization coefficient, where λ is the tip speed ratio; v w is the actual wind speed; P m , P e , C pref , ω and ν are the per unit values ​​of the wind turbine’s mechanical power, electromagnetic power, rotor angular velocity, wind energy utilization coefficient and external wind speed respectively; K del H is the power reduction factor of the fan; t is the inertia time constant of the fan; t is time; P B is the reference power; K p is the proportionality coefficient; P reg is the per unit value of the compensation power used to adjust the system frequency;

[0081] The rotor motion equation of the fan operating in the load reduction mode is linearized by using a small signal analysis method to obtain the following equation:

[0082] ΔP m =A1·Δβ+A2·Δω+A3·Δv

[0083]

[0084]

[0085]

[0086] ΔP e =3K del ω 2 Δω+ΔP reg

[0087] ΔP m -ΔP e =2H t ωdΔω / dt;

[0088] Where ΔP m , Δβ, Δω, Δv are the changes of mechanical power, pitch angle, rotor angular velocity and external wind speed of the wind turbine respectively; A1, A2, A3 are the intermediate variables used in the derivation; K λ Cpref The partial derivative with respect to λ, K β C pref Partial derivative with respect to β;

[0089] According to the linearized rotor motion equation, the small signal model of the variable speed wind turbine under overspeed load reduction state is obtained by eliminating the intermediate variables as follows:

[0090]

[0091] ΔP e =3K del ω 2 Δω+ΔP reg ;

[0092] Where ΔP e , ΔP reg are the changes of fan electromagnetic power and compensation power respectively.

[0093] In one embodiment of the present invention, the unified control model of electric vehicles is constructed based on the energy aggregator, specifically:

[0094] The first-order dynamic model of a single electric vehicle is established as follows:

[0095]

[0096] Where ΔP EV,i The active power generated by the i-th electric vehicle; ΔP rev,i is the power reference received by the controller of the i-th electric vehicle; i is the access status signal of the i-th electric vehicle. When the electric vehicle is connected to the grid, its value is 1, otherwise it is 0; SOC i The charging and discharging status of the i-th electric vehicle; K EV,i and T EV,i are the gain coefficient and inertia time constant of the i-th electric car respectively; s is the Laplace operator.

[0097] According to the first-order dynamic model of the single electric vehicle, the active power ΔP of all the electric vehicle energy aggregators that can be connected is obtained. EV ,as follows:

[0098]

[0099] Among them, n is the maximum number of electric vehicles that can be connected;

[0100] Let the inertia time constant of all electric vehicles be equal to T EV , that is, T EV =T EV,i,i=1,2,…,n; Since the time constants of different electric vehicles are basically the same, the control efficiency can be improved by this method;

[0101] Power reference value ΔP of energy aggregator rev as follows:

[0102]

[0103] That is, the power reference value ΔP of the energy aggregator rev It is the sum of all electric vehicle power reference values;

[0104] The unified control model of electric vehicles is obtained as follows:

[0105]

[0106] Among them, K EV is the equivalent electric vehicle gain coefficient, expressed as follows:

[0107]

[0108] In one embodiment of the present invention, in the microgrid dynamic frequency response model established based on the small signal model of the wind turbine and the unified control model of the electric vehicle, after the wind turbine and the electric vehicle coordinate frequency modulation, the state space modeling method is used to establish the microgrid dynamic frequency response model including the wind turbine, photovoltaic, diesel engine and electric vehicle:

[0109]

[0110] y = Cx;

[0111] Where A is the state matrix, B1 and B2 are the disturbance input matrix and control input matrix respectively, and C is the output matrix. External disturbance d = [ΔP PV ΔP L Δv] T , control input u=[ΔP reg ΔP rev ] T , measured output y = Δf, state variable x = [Δf ΔP DG X G ACE Δω ΔP EV ] T ; ΔP DG , ΔP PV , and ΔP L is the power fluctuation of diesel engine, photovoltaic and load; Δf is the frequency deviation of the system; X G is the speed regulator output; ACE is the ACE control output; ΔP Lis the load power fluctuation. The specific expressions of A, B1, B2, and C are as follows:

[0112]

[0113]

[0114] C = [1 0 0 0 0 0];

[0115] M and D are the inertia constant and damping coefficient of the system, respectively, and are perturbation parameters; T t is the time constant of the diesel generator; T g is the speed regulator time constant; R is the speed regulation coefficient; K E is the integral control gain.

[0116] Embodiment 3

[0117] A robust frequency control method for a microgrid based on wind turbines and electric vehicles is designed based on Example 2. The design goal is to find a feasible controller in the presence of external disturbances to reasonably coordinate the output of variable-speed wind turbines and electric vehicles, so that the closed-loop control system is stable and the frequency fluctuation of the microgrid under external disturbances is reduced.

[0118] In one embodiment of the present invention, the input signal includes a disturbance signal and a reference input signal. The reference signal is subtracted from the sum of the disturbance signal and the measured output signal to obtain an error signal. The error signal is input into the designed controller to obtain a control output signal. The controller input signal is calculated through a microgrid state space model to obtain a measured output signal. The product of the measured output signal and a performance weighted function is added to the product of the control output signal and the control weighted function to obtain a performance output signal.

[0119] In one embodiment of the present invention, the performance weighting function W e and W u The control weighting function is as follows:

[0120]

[0121]

[0122] Furthermore, H ∞ The problem of solving the robust frequency controller is transformed into the following solution of H ∞ Norm optimization problem:

[0123] ||T zw (s)|| ∞ ≤1;

[0124] The H ∞ The norm optimization problem is transformed into finding a symmetric positive definite matrix P that satisfies:

[0125]

[0126] The above inequality is transformed into a linear matrix inequality by using the substitution method to obtain the H ∞ Robust frequency controller K ∞ (s):

[0127]

[0128] in:

[0129]

[0130] Simulation scenario: The external wind speed is constant at 0.9 (pu), and the load power decreases by 0.1 (pu) every 50 seconds.

[0131] The simulation results are as follows Figure 3 As shown, it can be seen that the method proposed in the present invention can reduce the frequency fluctuation of the microgrid under power changes.

[0132] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure made using the contents of the present invention's specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A robust frequency control method for a microgrid based on wind turbines and electric vehicles, characterized in that: The following steps are involved: A small signal model of wind turbines participating in frequency regulation is established based on the load shedding control of wind turbines; Constructing a unified control model for electric vehicles based on energy aggregators; A dynamic frequency response model of the microgrid is established based on the small signal model of the wind turbine and the unified control model of the electric vehicle; According to the design objectives, the performance weighting function and the control weighting function are determined, and the H is solved based on the linear matrix inequality method. ∞ Robust frequency controller; The small signal model of wind turbines participating in frequency modulation is established based on the load reduction control of wind turbines, specifically: The rotor motion equation of the fan operating in the load reduction mode is established as follows: Where ρ is the air density; A is the windward swept area of ​​the wind turbine blade; C p (λ,0) is the wind energy utilization coefficient, where λ is the tip speed ratio; v w is the actual wind speed; P m , P e , C pref , ω and ν are the per unit values ​​of the wind turbine’s mechanical power, electromagnetic power, rotor angular velocity, wind energy utilization coefficient and external wind speed respectively; K del H is the power reduction factor of the fan; t is the inertia time constant of the fan; t is time; P B is the reference power; K p is the proportionality coefficient; P reg is the per unit value of the compensation power used to adjust the system frequency; The rotor motion equation of the fan operating in the load reduction mode is linearized by using a small signal analysis method to obtain the following equation: Among them, ΔP m , Δβ, Δω, Δv are the changes of mechanical power, pitch angle, rotor angular velocity and external wind speed of the wind turbine respectively; A1, A2, A3 are the intermediate variables used in the derivation; K λ C pref The partial derivative with respect to λ, K β C pref Partial derivative with respect to β; According to the linearized rotor motion equation, the small signal model of the variable speed wind turbine under overspeed load reduction state is obtained by eliminating the intermediate variables as follows: Where ΔP e , ΔP reg are the changes of electromagnetic power and compensation power of the fan respectively; The unified control model of electric vehicles is constructed based on the energy aggregator, specifically: The first-order dynamic model of a single electric vehicle is established as follows: Among them, ΔP EV,i The active power generated by the i-th electric vehicle; ΔP rev,i is the power reference received by the controller of the i-th electric vehicle; i is the access status signal of the i-th electric vehicle. When the electric vehicle is connected to the grid, its value is 1, otherwise it is 0; SOC i The charging and discharging status of the i-th electric vehicle; K EV,i and T EV,i are the gain coefficient and inertia time constant of the i-th electric vehicle respectively; s is the Laplace operator; According to the first-order dynamic model of the single electric vehicle, the active power ΔP of all the electric vehicle energy aggregators that can be connected is obtained. EV ,as follows: Where n is the maximum number of electric vehicles that can be connected; Let the inertia time constant of all electric vehicles be equal to T EV ; Power reference value ΔP of energy aggregator rev as follows: The unified control model of electric vehicles is obtained as follows: Among them, K EV is the equivalent electric vehicle gain coefficient, expressed as follows: In the microgrid dynamic frequency response model established based on the small signal model of the wind turbine and the unified control model of the electric vehicle, after the wind turbine and the electric vehicle coordinate the frequency modulation, the state space modeling method is used to establish the microgrid dynamic frequency response model including the wind turbine, photovoltaic, diesel engine and electric vehicle: Among them, A is the state matrix, B1 and B2 are the disturbance input matrix and control input matrix respectively, and C is the output matrix; the external disturbance d = [ΔP PV ΔP L Δv] T , control input u=[ΔP reg ΔP rev ] T , measured output y = Δf, state variable x = [ΔfΔP DG X G ACEΔωΔP EV ] T ; ΔP DG , ΔP PV , and ΔP L is the power fluctuation of diesel engine, photovoltaic and load; Δf is the frequency deviation of the system; X G is the speed regulator output; ACE is the ACE control output; ΔP L is the load power fluctuation; the specific expressions of A, B1, B2, and C are as follows: M and D are the inertia constant and damping coefficient of the system, respectively, and are perturbation parameters; T t is the time constant of the diesel generator; T g is the speed regulator time constant; R is the speed regulation coefficient; K E is the integral control gain; Performance weighting function W e and W u The control weighting function is as follows: H ∞ The problem of solving the robust frequency controller is transformed into the following solution of H ∞ Norm optimization problem: ||T zw (s)|| ∞ ≤1; The H ∞ The norm optimization problem is transformed into finding a symmetric positive definite matrix P that satisfies: The above inequality is transformed into a linear matrix inequality by using the substitution method to obtain the H ∞ Solution of the robust frequency controller.

2. The robust frequency control method for a microgrid based on a wind turbine and an electric vehicle according to claim 1, characterized in that: The design goal is to find a feasible controller in the presence of external disturbances to reasonably coordinate the output of variable-speed wind turbines and electric vehicles, so that the closed-loop control system is stable and the frequency fluctuation of the microgrid under external disturbances is reduced.

3. The robust frequency control method for a microgrid based on a wind turbine and an electric vehicle according to claim 2 is characterized in that: The input signal includes a disturbance signal and a reference input signal. The reference signal minus the sum of the disturbance signal and the measured output signal is used to obtain an error signal. The error signal is input into the designed controller to obtain a control output signal. The controller input signal is calculated by a microgrid state space model to obtain a measured output signal. The product of the measured output signal and a performance weighted function is added to the product of the control output signal and the control weighted function to obtain a performance output signal.

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