Control method of network-forming type optical storage converter based on model predictive control

Abstract

The invention provides a model predictive control-based control method for a network construction type optical storage converter, and the method comprises the steps: introducing a model predictive control link into a virtual synchronous generator control system of the optical storage converter, building and obtaining a discrete time prediction model, building and obtaining a prediction equation according to the discrete time prediction model, and carrying out the calculation of the prediction equation. And constructing a cost function about the frequency increment and the control quantity increment based on the prediction equation, and solving the cost function to obtain an optimal control sequence. Based on the control method, the controller can accurately predict the future change trend of the system, generates an optimal control sequence under the condition that the cost function is optimal, compensates the power vacancy of the VSG, and improves the frequency dynamic characteristics of the system.

Description

Technical Field

[0001] The present application belongs to the field of renewable energy power generation technology, and specifically, relates to a control method for a grid-connected photovoltaic storage inverter based on model predictive control. Background Art

[0002] In recent years, the penetration rate of new energy and power electronic equipment in my country has continued to increase, and the proportion of thermal power units has decreased year by year. Since power electronic converters cannot provide inertia similar to traditional synchronous generators, the strength of the power system has weakened and the stability problem has gradually become prominent. The converter based on the grid-building control strategy simulates the characteristics of the synchronous generator, making it appear as a voltage source externally, which can provide voltage and frequency support, enhance the stability of the power system, and meet the urgent needs of the large power grid for new energy to participate in system frequency regulation, voltage regulation, and damping regulation in the context of new energy generation gradually replacing thermal power units. As a mainstream grid-building control strategy, Virtual Synchronous Generator (VSG) control adopts a power synchronization strategy similar to that of synchronous generators, can automatically build the AC side output voltage, actively support the frequency and voltage regulation of the converter, and is often used in energy storage converters. It can also provide inertia and damping for the system.

[0003] When the system frequency changes too much or too fast, traditional VSG control cannot guarantee a good frequency response, and its frequency offset and frequency change rate may be too large, affecting the stability of the system. The fundamental reason is that the control only relies on the response of the VSG itself, but lacks real-time control and compensation. To address this problem, the most common solution is to introduce adaptive adjustment of virtual inertia coefficient and virtual damping coefficient to reduce frequency deviation. However, the method of adaptive adjustment of control parameters has its limitations: first, there is a delay in the adaptive adjustment parameters. When the disturbance is too fast or too large, it cannot fundamentally solve the problem of excessive frequency deviation at the moment of disturbance; second, the adaptive adjustment parameter method does not compensate for the system active power shortage on the basis of VSG control, but directly changes the parameter size in VSG. It does not have plug-and-play function. When the adaptive adjustment fails, it is difficult to directly remove the link to eliminate the fault, which increases the risk of system failure. Summary of the invention

[0004] The technical problem solved by the present application is: how to provide a control method for a grid-type photovoltaic storage inverter based on model predictive control that can accurately predict the change trend in real time and perform shortage compensation.

[0005] The present application provides a control method for a grid-type photovoltaic storage converter based on model predictive control, the control method comprising:

[0006] The model predictive control link is introduced into the virtual synchronous generator control system of the photovoltaic storage converter, and a discrete time prediction model is established.

[0007] A prediction equation is obtained according to the discrete time prediction model;

[0008] Constructing a cost function regarding frequency increment and control amount increment based on the prediction equation;

[0009] The cost function is solved to obtain the optimal control sequence.

[0010] Optionally, the method of introducing a model predictive control link into the virtual synchronous generator control system of the photovoltaic storage converter to establish a discrete time prediction model includes:

[0011] Establish a frequency characteristic model of the virtual synchronous generator control of the photovoltaic storage converter based on the model predictive control link:

[0012]

[0013] Where P e is the system active power, P ref is the active power rating, ω m ,ω 0 is the mechanical angular frequency and the rated angular frequency, J is the inertia coefficient, D is the damping coefficient, K ω is the frequency droop coefficient, P mpc For active power compensation;

[0014] The system state space equation is established, and the frequency characteristic model is discretized using the Euler method to obtain a discrete time prediction model:

[0015]

[0016] In the formula, ω=ω m -ω 0 , Ts is the sampling time, Δ(k) is the increment of the corresponding physical quantity in the kth step, P m Controllable input power for the system.

[0017] Optionally, the method for constructing a prediction equation according to the discrete time prediction model includes:

[0018] The step sizes of the prediction time domain and the control time domain are both set to 3, and the prediction equation is obtained according to the discrete time prediction model:

[0019] Y(k+1|k)=S A Δω(k)+S m ΔP m (k)+S c ΔPc (k)

[0020] in:

[0021] S A =[AA 2 A 3 ] T , S e =[B e AB e A 2 B e ] T .

[0022] Optionally, the method of constructing a cost function about frequency increment and control amount increment based on the prediction equation includes:

[0023] The control cost is selected as the frequency increment Δω(k) and the control amount increment ΔP m The cost function is a weighted sum of the two functions:

[0024]

[0025] The constraints are:

[0026]

[0027] Where: R(k+1) is a zero matrix with the same number of rows as the prediction step size and the same number of columns as 1; Γ y , Γ Pm , P m , Y min (k+1), Y max (k+1),ΔP m_min (k), ΔP m_max (k) are α, β, and y respectively min ,y max , ΔP m_min , ΔP m_max Column vector composed of .

[0028] Optionally, the method for solving the cost function to obtain the optimal control sequence includes:

[0029] The cost function is transformed into a quadratic optimization model:

[0030]

[0031] Substituting the prediction equation into the above formula, the quadratic optimization model is transformed into the following form:

[0032] F=ξ T ξ=(KΔP m (k)-b)T (KΔP m (k)-b)

[0033] E p (k+1|k)=R(k+1|k)-S A Δω(k)-S e ΔP e (k);

[0034] Build an optimized model And solve the extreme value solution of the optimization model under the extreme value condition, and use the extreme value solution as the optimal control sequence at time k.

[0035] Optionally, the control method further includes:

[0036] The optimal control sequence at time k is used to correct the power of the virtual synchronous generator control system of the photovoltaic storage converter.

[0037] Optionally, the control method further includes:

[0038] The corrected power at each time is used as the input power of the virtual synchronous generator control system of the light storage converter in the next iteration, and the optimal control sequence at multiple times is obtained by iterating multiple times.

[0039] The present application provides a control method for a grid-type photovoltaic storage converter based on model predictive control, which has the following technical effects:

[0040] Based on this control method, the controller can accurately predict the future change trend of the system and generate the optimal control sequence while optimizing the cost function, thereby compensating for the power shortage of the VSG and improving the frequency dynamic characteristics of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 A flow chart of the main steps of a control method of a grid-type photovoltaic storage converter based on model predictive control according to one or more embodiments;

[0042] Figure 2 The present invention is a complete flow chart of a control method of a grid-connected photovoltaic storage inverter based on model predictive control according to one or more embodiments. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0044] Before describing in detail each embodiment of the present application, the technical concept of the present application is briefly described first: Currently, due to the lack of real-time control and compensation for VSG control, an adaptive adjustment of the virtual inertia coefficient is usually used to reduce the frequency deviation, which results in adjustment delays, inaccurate adjustments, and other problems. For this reason, the present application provides a control method for a meshed photovoltaic storage inverter based on model predictive control, which introduces a model predictive control link (MPC) into the virtual synchronous generator control system of the photovoltaic storage inverter, establishes a discrete time prediction model, and further constructs a cost function for the frequency increment and the control quantity increment, solves the optimal control sequence, and corrects the power of the virtual synchronous generator control system of the photovoltaic storage inverter, and the prediction process of the method is timely and accurate. The specific principles of the control method for a meshed photovoltaic storage inverter based on model predictive control of the present application are described below in conjunction with more embodiments.

[0045] Specifically, Figure 1 As shown, the control method of the grid-type photovoltaic storage converter based on model predictive control in the first embodiment includes:

[0046] Step S10: introducing a model predictive control link into the virtual synchronous generator control system of the photovoltaic power storage converter to establish a discrete time prediction model;

[0047] Step S20: constructing a prediction equation based on a discrete time prediction model;

[0048] Step S30: constructing a cost function about the frequency increment and the control amount increment based on the prediction equation;

[0049] Step S40: Solve the cost function to obtain the optimal control sequence.

[0050] Specifically, in step S10, a frequency characteristic model of a photovoltaic storage converter virtual synchronous generator control system is first established, which is expressed as:

[0051]

[0052] Where P e is the system active power, P ref is the active power rating, ω m ,ω 0 is the mechanical angular frequency and the rated angular frequency, J is the inertia coefficient, D is the damping coefficient, K ω is the frequency droop coefficient. Considering active power compensation P mpc Based on the role of model predictive control, a frequency characteristic model of the virtual synchronous generator control of the photovoltaic storage converter is established:

[0053]

[0054] The system state space equation is established, and the frequency characteristic model is discretized using the Euler method to obtain the discrete time prediction model:

[0055]

[0056] In the formula, ω=ω m -ω 0 , Ts is the sampling time, Δ(k) is the increment of the corresponding physical quantity in the kth step, P m Controllable input power for the system.

[0057] Exemplarily, in step S20, in order to obtain good calculation accuracy and rate, the method of constructing a prediction equation according to a discrete time prediction model includes:

[0058] The step sizes of the prediction time domain and the control time domain are both set to 3, and the prediction equation is obtained according to the discrete time prediction model:

[0059] Y(k+1|k)=S A Δω(k)+S m ΔP m (k)+S c ΔP c (k)

[0060] in:

[0061] S A =[AA 2 A 3 ] T , S e =[B e AB e A 2 B e ] T .

[0062] Furthermore, in step S30, the method of constructing a cost function about the frequency increment and the control amount increment based on the prediction equation includes:

[0063] The control cost is selected as the frequency increment Δω(k) and the control amount increment ΔP m The cost function is a weighted sum of the two functions:

[0064]

[0065] The constraints are:

[0066]

[0067] Where: R(k+1) is a zero matrix with the same number of rows as the prediction step size and the same number of columns as 1; Γ y , Γ Pm , P m , Y min (k+1), Y max (k+1),ΔP m_min (k), ΔP m_max (k) are α, β, and y respectively min ,y max , ΔP m_min , ΔP m_max Column vector composed of .

[0068] Exemplarily, in step S40, the method of solving the cost function to obtain the optimal control sequence includes:

[0069] Convert the cost function into a quadratic optimization model:

[0070]

[0071] Substituting the prediction equation into the above formula, the quadratic optimization model is transformed into the following form:

[0072] F=ξ T ξ=(KΔP m (k)-b) T (KΔP m (k)-b)

[0073] E p (k+1|k)=R(k+1|k)-S A Δω(k)-S e ΔP e (k);

[0074] Build an optimized model And solve the extreme value solution of the optimization model under the extreme value condition, and use the extreme value solution as the optimal control sequence at time k.

[0075] Among them, by T The extreme value condition of ξ can obtain the extreme value solution of the model as:

[0076]

[0077] By 2 (ξ T ξ) / dz 2 =2K TK>0 shows that this extreme value solution is the minimum value solution, that is, the optimal control sequence at time k. Further, the optimal control sequence at time k is used to correct the power of the virtual synchronous generator control system of the photovoltaic storage converter. The corrected power is used as the input power of the virtual synchronous generator control system of the photovoltaic storage converter at the next iteration, and iterates multiple times to obtain the optimal control sequence at multiple times.

[0078] For example, take The first element in the virtual synchronous generator control system is the compensation power P mpc The input system is optimized in a rolling manner to obtain the global optimal solution, and finally this optimal control sequence is applied to the photovoltaic storage integrated inverter. At this time, the converter obtains active power deficiency compensation, and the reference power is updated to P ref +P mpc , the system frequency dynamic characteristics are improved.

[0079] like Figure 2 As shown in FIG. 1 , the complete process of the control method of the grid-connected photovoltaic storage converter based on model predictive control is as follows:

[0080] Step 1 Initialize the system;

[0081] Step 2 establishes a discrete time prediction model based on the frequency characteristics of the virtual synchronous generator control system;

[0082] Step 3: Design the cost function of frequency increment and control amount increment;

[0083] Step 4: Convert the cost function into a quadratic optimization model and substitute it into the prediction equation;

[0084] Step 5: Obtain the extreme value solution of the quadratic optimization model, i.e. the optimal solution at time k;

[0085] Step 6 The power correction of the virtual synchronous generator control system is P ref +P mpc ;

[0086] Step 7 enters iteration;

[0087] Step8 Scrolling optimization;

[0088] Step 9 obtains the global optimal solution and ends.

[0089] The control method of the grid-type photovoltaic storage converter based on model predictive control provided in this embodiment applies MPC to the grid-type photovoltaic storage integrated converter, and the prediction model can be obtained by discretizing the frequency characteristic model of the virtual synchronous generator control system, and then the size of the control quantity is changed in real time according to the optimization target based on the prediction of future changes in the system. The MPC controller of this embodiment can accurately predict the future change trend of the system, and generate the optimal control sequence while optimizing the cost function, compensate for the active power shortage of the VSG, and improve the dynamic characteristics of the system frequency.

[0090] The specific implementation methods of the present application are described in detail above. Although some embodiments have been shown and described, those skilled in the art should understand that these embodiments can be modified and improved without departing from the principles and spirit of the present application whose scope is defined by the claims and their equivalents. These modifications and improvements should also be within the scope of protection of the present application.

Claims

1. A control method for a grid-type photovoltaic storage converter based on model predictive control, characterized in that: The control method comprises: The model predictive control link is introduced into the virtual synchronous generator control system of the photovoltaic storage converter, and a discrete time prediction model is established. A prediction equation is obtained according to the discrete time prediction model; Constructing a cost function regarding frequency increment and control amount increment based on the prediction equation; The cost function is solved to obtain the optimal control sequence.

2. The control method of the grid-type photovoltaic storage converter based on model predictive control according to claim 1 is characterized in that: The method of introducing a model predictive control link into the virtual synchronous generator control system of the photovoltaic storage converter to establish a discrete time prediction model includes: Establish a frequency characteristic model of the virtual synchronous generator control of the photovoltaic storage converter based on the model predictive control link: Where P e is the system active power, P ref is the active power rating, ω m ω0 is the mechanical angular frequency and the rated angular frequency, J is the inertia coefficient, D is the damping coefficient, K ω is the frequency droop coefficient, P mpc For active power compensation; The system state space equation is established, and the frequency characteristic model is discretized using the Euler method to obtain the discrete time prediction model: In the formula, ω=ω m -ω0, Ts is the sampling time, Δ(k) is the increment of the corresponding physical quantity in the kth step, P m Controllable input power for the system.

3. The control method of the grid-type photovoltaic storage converter based on model predictive control according to claim 2 is characterized in that: The method for constructing a prediction equation according to the discrete time prediction model comprises: The step sizes of the prediction time domain and the control time domain are both set to 3, and the prediction equation is obtained according to the discrete time prediction model: Y(k+1|k)=S A Δω(k)+S m ΔP m (k)+S c ΔP c (k) in: S A =[A A 2 A 3 ] T , S e =[B e AB e A 2 B e ] T 。 4. The control method of the grid-type photovoltaic storage converter based on model predictive control according to claim 3 is characterized in that: The method for constructing a cost function about frequency increment and control amount increment based on the prediction equation includes: The control cost is selected as the frequency increment Δω(k) and the control amount increment ΔP m The cost function is a weighted sum of the two functions: The constraints are: Where: R(k+1) is a zero matrix with the same number of rows as the prediction step size and the same number of columns as 1; Γ y , P m , Y min (k+1), Y max (k+1),ΔP m_min (k), ΔP m_max (k) are α, β, and y respectively min ,y max , ΔP m_min , ΔP m_max Column vector composed of .

5. The control method of a grid-connected photovoltaic storage converter based on model predictive control according to claim 1, characterized in that: The method for solving the cost function to obtain the optimal control sequence includes: The cost function is transformed into a quadratic optimization model: F=ξ T ξ, Substituting the prediction equation into the above formula, the quadratic optimization model is transformed into the following form: F=ξ T ξ=(KΔP m (k)-b) T (KΔP m (k)-b) E p (k+1|k)=R(k+1|k)-S A Δω(k)-S e ΔP e (k); Build an optimized model And solve the extreme value solution of the optimization model under the extreme value condition, and use the extreme value solution as the optimal control sequence at time k.

6. The control method of a grid-connected photovoltaic storage converter based on model predictive control according to claim 1, characterized in that: The control method further comprises: The optimal control sequence at time k is used to correct the power of the virtual synchronous generator control system of the photovoltaic storage converter.

7. The control method of a grid-connected photovoltaic storage converter based on model predictive control according to claim 1, characterized in that: The control method further comprises: The corrected power at each time is used as the input power of the virtual synchronous generator control system of the light storage converter in the next iteration, and the optimal control sequence at multiple times is obtained by iterating multiple times.

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