Microgrid voltage control method based on feedback linearization predictive control and storage medium

Abstract

The invention discloses a microgrid voltage control method based on feedback linearization predictive control and a storage medium. The method comprises the following steps: establishing a single-machine model of a voltage source converter in a micro-grid; determining a micro-grid control model based on the single-machine model; performing feedback linearization processing on the micro-grid control model to obtain an input and output linear decoupling feedback control model; and generating a voltage control instruction based on a model prediction control algorithm and the feedback control model according to the current operation state and the current voltage instruction of the microgrid. According to the method, the dynamic characteristics of the power generation main body in the micro-grid are considered, and the voltage control effect of the micro-grid under the multi-main-body time-varying operation condition can be achieved.

Description

Technical Field

[0001] The present invention relates to the field of electric power, and in particular to a microgrid voltage control method and storage medium based on feedback linearization predictive control. Background Art

[0002] The park microgrid has a variety of heterogeneous flexible resources such as distributed power generation, loads and energy storage. By coordinating the operation of these flexible resources, the quality of power supply in the park can be improved, and when the main grid fails and cannot supply power, it can switch to the isolated grid operation mode to improve the power supply reliability of the park's key loads. With the access of distributed resources such as wind power and photovoltaic power in the form of power electronics, the randomness and volatility of wind and solar resources have led to prominent voltage fluctuation problems in the park, affecting the safety and economy of the operation of the park's electric microgrid.

[0003] At present, the voltage control method of park microgrid is still mainly based on model-free proportional integral (PI) control. The proportional integral (PI) control method lacks the dynamic characteristics of the power generation entities in the park, and it is difficult to ensure the voltage control effect under the time-varying operating conditions of multiple entities. There is a safety risk of instability caused by the interaction of multi-agent voltage control. Summary of the invention

[0004] In order to solve the above problems, the inventors have made the present invention, and through specific implementation methods, provide a microgrid voltage control method and storage medium based on feedback linearization predictive control.

[0005] On the one hand, an embodiment of the present invention provides a microgrid voltage control method based on feedback linearization predictive control, comprising:

[0006] Establish a single-machine model of the voltage source converter in the microgrid;

[0007] Based on the single machine model, determining a microgrid control model;

[0008] Performing feedback linearization processing on the microgrid control model to obtain a feedback control model with input-output linear decoupling;

[0009] According to the current operating state of the microgrid and the current voltage command, a voltage control command is generated based on a model predictive control algorithm and the feedback control model.

[0010] Furthermore, the constraints of the microgrid control model include:

[0011] The voltage vector of the converter terminal nodes, the voltage vector of the grid connection point and the output current vector constraints;

[0012] Microgrid active power and reactive power constraints;

[0013] Electromagnetic transient constraints in microgrids.

[0014] Furthermore, the expression of the single machine model is:

[0015]

[0016] Where: i is the frequency of the ith converter; V i is the voltage amplitude of the i-th converter; P i is the active power after filtering of the i-th converter; Q i is the active power and reactive power after filtering of the i-th converter; is the active power setting value of the i-th converter; is the active power setting value and reactive power setting value of the i-th converter; ω n is the rated reference value of frequency; V n is the rated reference value of the voltage amplitude, k p is the droop coefficient of active power; k q is the droop coefficient of reactive power.

[0017] Furthermore, the voltage vector of the converter terminal node, the voltage vector of the grid connection point and the output current vector are constrained as follows:

[0018] Define the voltage vector v of the ith converter terminal node in the dq coordinate system: oi , the voltage vector ν at the grid connection point bi and the output current vector i oi ,

[0019]

[0020] Where: is the d-axis component of the voltage vector of the i-th converter terminal node; is the q-axis component of the voltage vector at the i-th converter terminal node; is the d-axis component of the voltage vector at the grid connection point of the i-th converter; is the q-axis component of the voltage vector at the grid connection point of the i-th converter, is the d-axis component of the output current vector of the i-th converter; is the q-axis component of the output current vector of the i-th converter;

[0021] The active power and reactive power constraints are:

[0022]

[0023] Where: c is the cutoff frequency of the first-order filter, and the matrix J is a two-dimensional rotation matrix;

[0024] The electromagnetic transient constraints of the microgrid are:

[0025]

[0026] Where: Matrix Z i is the line impedance matrix between the ith converter terminal node and the grid connection point, r i and l i are the line resistance and inductance between the i-th converter terminal node and the grid connection point, respectively.

[0027] Furthermore, the constraints of the stand-alone model and the microgrid control model are combined to obtain the expression of the microgrid control model:

[0028]

[0029] In the formula: x is the state variable of the microgrid control model; u is the input variable of the microgrid control model; y is the output variable of the microgrid control model; f is the state transfer function; g is the input function; h is the output function; is the state variable of the i-th converter; is the input variable of the i-th converter; is the output variable of the i-th converter; is the output variable of the PCC node; is the d-axis component of the PCC node voltage; is the q-axis component of the PCC node voltage.

[0030] Further, feedback linearization processing is performed on the microgrid control model to obtain a feedback control model with input-output linear decoupling, including the following steps:

[0031] The output function of the microgrid control model is continuously derived until the derivative result includes at least one input variable, and the following is obtained:

[0032]

[0033] Where: r i is the output variable y i The relative degree of y i ,h i The subscript i in L indicates the serial number of the output variable; f h is the Lie derivative operator, defined as The higher-order Lie derivative is expressed as

[0034] The microgrid control model is subjected to variable replacement, wherein the expression of the new variable is:

[0035]

[0036] Based on the Lie derivative definition and the microgrid control model, a feedback control model with linear decoupling of input and output is obtained, wherein the expression of the feedback control model is:

[0037]

[0038] Where: Matrix A and Matrix B are the state transfer matrix and input matrix respectively; x is the state variable of the feedback linear model, and v is the input variable of the feedback linear model;

[0039] The expression of the feedback control model at time k is:

[0040] χ k+1 =A k χ k +B k v k .

[0041] Further, according to the current system state and control input of the microgrid, based on the model predictive control algorithm and the feedback control model, generating a voltage control instruction includes the following steps:

[0042] According to the current system state and control input of the microgrid, based on the feedback control model, objective function, constraint conditions and optimization objective function, an optimization solution is performed to obtain a solution result;

[0043] A voltage control instruction is generated according to the solution result.

[0044] Furthermore, the expression of the model predictive control algorithm is:

[0045]

[0046] Where: T is the prediction time domain, parameter matrix Q k , R k , Q T is a semi-positive coefficient matrix, and the parameter vector q k , r k ,q T is the objective function vector.

[0047] Furthermore, the expression of the optimization objective function of the model predictive control algorithm is:

[0048]

[0049] The operator represents the square of the vector norm, parameter ρ is the balance coefficient between the microgrid voltage command and the state stability target, T is the prediction time domain, χ k is the feedback linear state vector.

[0050] On the other hand, an embodiment of the present invention provides a computer storage medium, wherein the computer storage medium stores computer executable instructions, and the computer executable instructions implement the above-mentioned microgrid voltage control method when executed.

[0051] The beneficial effects of the above technical solution provided by the embodiment of the present invention include at least:

[0052] A single-machine model of a voltage source converter in a microgrid is established; a microgrid control model is determined based on the single-machine model; feedback linearization is performed on the microgrid control model to obtain a feedback control model with linear decoupling of input and output; and a voltage control command is generated based on a model predictive control algorithm and the feedback control model according to the current operating state of the microgrid and the current voltage command, taking into account the dynamic characteristics of the power generation entities in the park, the voltage control effect of the microgrid under multi-subject time-varying operating conditions can be achieved.

[0053] Other features and advantages of the present invention will be described in the following description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0054] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0056] Figure 1 is a flow chart of a method in an embodiment of the present invention;

[0057] Figure 2 It is a principle block diagram of the method in an embodiment of the present invention. DETAILED DESCRIPTION

[0058] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0059] The park microgrid has a variety of heterogeneous flexible resources such as distributed power generation, loads and energy storage. By coordinating the operation of these flexible resources, the quality of power supply in the park can be improved, and when the main grid fails and cannot supply power, it can switch to the isolated grid operation mode to improve the power supply reliability of the park's key loads. With the access of distributed resources such as wind power and photovoltaic power in the form of power electronics, the randomness and volatility of wind and solar resources have led to prominent voltage fluctuation problems in the park, affecting the safety and economy of the operation of the park's electric microgrid.

[0060] At present, the voltage control method of park microgrid is still mainly based on model-free proportional integral (PI) control. The proportional integral (PI) control method lacks the dynamic characteristics of the power generation entities in the park, and it is difficult to ensure the voltage control effect under the time-varying operating conditions of multiple entities. There is a safety risk of instability caused by the interaction of multi-agent voltage control.

[0061] Model predictive control is a modern control method that can predict the state trajectory of a period of time in the future based on the dynamic model of the control object, thereby obtaining the optimal control instructions based on the solution of the optimization problem. However, most of the flexible resources in the park microgrid are connected to the microgrid through power electronic converters, which contain high-order complex nonlinear dynamic characteristics. Directly using nonlinear dynamic modeling will result in the final predictive control optimization problem being a nonlinear optimization problem. This type of problem usually cannot guarantee the optimal solution, and the solution complexity is difficult to meet the needs of online rolling optimization.

[0062] In order to solve the problems existing in the prior art, an embodiment of the present invention provides a microgrid voltage control method and a storage medium based on feedback linearization predictive control.

[0063] The embodiment of the present invention provides a microgrid voltage control method based on feedback linearization predictive control, and the process is as follows: Figure 1 As shown, the principle is as Figure 2 As shown, the following steps are included:

[0064] Step S10: establishing a single-machine model of a voltage source converter in a microgrid, and determining a microgrid control model based on the single-machine model.

[0065] Among them, for a radial microgrid containing N converters, it can be assumed that all converters have a common grid connection point, and the main goal of the flexibility resources in the microgrid is to provide voltage support to the common grid connection point (Point of Common Coupling, PCC) while maintaining the stability of the local voltage. The converter stand-alone model is used to represent the frequency and voltage amplitude of the converter, the active power and reactive power after the converter is filtered, the active power set value and reactive power set value, the rated reference value of the frequency and voltage amplitude, and the droop coefficient of the active power and reactive power.

[0066] Step S20: performing feedback linearization processing on the microgrid control model to obtain a feedback control model with input-output linear decoupling.

[0067] Among them, the microgrid control model is nonlinear, and feedback linearization processing is performed on the microgrid control model to linearize the microgrid control model to obtain a feedback control model with input-output linear decoupling.

[0068] Step S30: Generate a voltage control instruction based on a model predictive control algorithm and the feedback control model according to the current operating state of the microgrid and the current voltage instruction.

[0069] In the above method of this embodiment, the expression of the single machine model is:

[0070]

[0071] Where: i is the frequency of the ith converter; V i is the voltage amplitude of the i-th converter; P i is the active power after filtering of the i-th converter; Q i is the active power and reactive power after filtering of the i-th converter; is the active power setting value of the i-th converter; is the active power setting value and reactive power setting value of the i-th converter; ω n is the rated reference value of frequency; V n is the rated reference value of the voltage amplitude, k p is the droop coefficient of active power; k q is the droop coefficient of reactive power.

[0072] In the above method of this embodiment, the voltage vector of the converter terminal node, the voltage vector of the grid connection point and the output current vector are constrained as follows:

[0073] Define the voltage vector v of the ith converter terminal node in the dq coordinate system: oi , the voltage vector v at the grid connection point bi and the output current vector i oi ,

[0074]

[0075] Where: is the d-axis component of the voltage vector of the i-th converter terminal node; is the q-axis component of the voltage vector at the i-th converter terminal node; is the d-axis component of the voltage vector at the grid connection point of the i-th converter; is the q-axis component of the voltage vector at the grid connection point of the i-th converter, is the d-axis component of the output current vector of the i-th converter; is the q-axis component of the output current vector of the i-th converter;

[0076] The active power and reactive power constraints are:

[0077]

[0078] Where: c is the cutoff frequency of the first-order filter, and the matrix J is a two-dimensional rotation matrix;

[0079] The electromagnetic transient constraints of the microgrid are:

[0080]

[0081] Where: Matrix Z i is the line impedance matrix between the ith converter terminal node and the grid connection point, r i and l i are the line resistance and inductance between the i-th converter terminal node and the grid connection point, respectively.

[0082] In the above method of this embodiment, the constraint conditions of the stand-alone model and the microgrid control model are combined to obtain the expression of the microgrid control model:

[0083]

[0084] In the formula: x is the state variable of the microgrid control model; u is the input variable of the microgrid control model; y is the output variable of the microgrid control model; f is the state transfer function; g is the input function; h is the output function; is the state variable of the i-th converter; is the input variable of the i-th converter; is the output variable of the i-th converter; is the output variable of the PCC node; is the d-axis component of the PCC node voltage; is the q-axis component of the PCC node voltage.

[0085] In the above method of this embodiment, feedback linearization processing is performed on the microgrid control model to obtain a feedback control model with input-output linear decoupling, which includes the following steps:

[0086] The output function of the microgrid control model is continuously derived until the derivative result includes at least one input variable, and the following is obtained:

[0087]

[0088] Where: ri is the output variable y i The relative degree of y i ,h i The subscript i in L indicates the serial number of the output variable; f h is the Lie derivative operator, defined as The higher-order Lie derivative is expressed as

[0089] The microgrid control model is subjected to variable replacement, wherein the expression of the new variable is:

[0090]

[0091] Based on the Lie derivative definition and the microgrid control model, a feedback control model with linear decoupling of input and output is obtained, wherein the expression of the feedback control model is:

[0092]

[0093] Where: Matrix A and Matrix B are the state transfer matrix and input matrix respectively; x is the state variable of the feedback linear model, and ν is the input variable of the feedback linear model;

[0094] The expression of the feedback control model at time k is:

[0095] χ k+1 =A k xk + B k ν k .

[0096] In the above method of this embodiment, the expression of the model predictive control algorithm is:

[0097]

[0098] Where: T is the prediction time domain, parameter matrix Q k , R k , Q T is a semi-positive coefficient matrix, and the parameter vector q k , r k ,q T is the objective function vector.

[0099] In the above method of this embodiment, the expression of the optimization objective function of the model predictive control algorithm is:

[0100]

[0101] where operator represents the square of the vector norm, parameter ρ is the balance coefficient between the microgrid voltage command and the state stability target, T is the prediction time domain, χk is the feedback linear state vector.

[0102] In the above method of this embodiment, a single-machine model of the voltage source converter in the microgrid is established; based on the single-machine model, a microgrid control model is determined; the microgrid control model is feedback linearized to obtain a feedback control model with input-output linear decoupling; according to the current operating state of the microgrid and the current voltage command, a voltage control command is generated based on the model predictive control algorithm and the feedback control model, taking into account the dynamic characteristics of the power generation entities in the park, and achieving the voltage control effect of the microgrid under multi-subject time-varying operating conditions.

[0103] Those skilled in the art can change the above sequence without departing from the protection scope of the present disclosure.

[0104] Based on the same inventive concept, an embodiment of the present invention further provides a computer storage medium, wherein the computer storage medium stores computer executable instructions, and when the computer executable instructions are executed by a processor, the aforementioned microgrid voltage control method is implemented.

[0105] Any modifications, supplements and equivalent substitutions made within the scope of the principles of the present invention shall still fall within the patent scope of the present invention.

[0106] Unless otherwise stated, the "connection" mentioned above refers to a logical relationship of current transmission, and does not necessarily mean a direct electrical connection. At the same time, "first", "second", etc. do not indicate a sequence, but are only used to identify related units, devices, etc.

[0107] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A microgrid voltage control method based on feedback linearization predictive control, characterized in that: The following steps are involved: Establish a single-machine model of the voltage source converter in the microgrid; Based on the single machine model, determining a microgrid control model; Performing feedback linearization processing on the microgrid control model to obtain a feedback control model with input-output linear decoupling; According to the current operating state of the microgrid and the current voltage command, a voltage control command is generated based on a model predictive control algorithm and the feedback control model.

2. The method according to claim 1, characterized in that The constraints of the microgrid control model include: The voltage vector of the converter terminal nodes, the voltage vector of the grid connection point and the output current vector constraints; Microgrid active power and reactive power constraints; Electromagnetic transient constraints in microgrids.

3. The method according to claim 2, characterized in that The expression of the single machine model is: Where: i is the frequency of the ith converter; V i is the voltage amplitude of the i-th converter; P i is the active power after filtering of the i-th converter; Q i is the active power and reactive power after filtering of the i-th converter; is the active power setting value of the i-th converter; is the active power setting value and reactive power setting value of the i-th converter; ω n is the rated reference value of frequency; V n is the rated reference value of the voltage amplitude, k p is the droop coefficient of active power; k q is the droop coefficient of reactive power.

4. The method according to claim 3, characterized in that The voltage vector of the converter terminal node, the voltage vector of the grid connection point and the output current vector are constrained as follows: With d q The coordinate system defines the voltage vector v of the ith converter terminal node oi , the voltage vector v at the grid connection point bi and the output current vector i oi , Where: is the d-axis component of the voltage vector of the i-th converter terminal node; is the q-axis component of the voltage vector at the i-th converter terminal node; is the d-axis component of the voltage vector at the grid connection point of the i-th converter; is the q-axis component of the voltage vector at the grid connection point of the i-th converter, is the d-axis component of the output current vector of the i-th converter; is the q-axis component of the output current vector of the i-th converter; The active power and reactive power constraints are: Where: c is the cutoff frequency of the first-order filter, and the matrix J is a two-dimensional rotation matrix; The electromagnetic transient constraints of the microgrid are: Where: Matrix Z i is the line impedance matrix between the ith converter terminal node and the grid connection point, r i and l i are the line resistance and inductance between the i-th converter terminal node and the grid connection point, respectively.

5. The method according to claim 4, characterized in that By combining the constraints of the stand-alone model and the microgrid control model, the expression of the microgrid control model is obtained: In the formula: x is the state variable of the microgrid control model; u is the input variable of the microgrid control model; y is the output variable of the microgrid control model; f is the state transfer function; g is the input function; h is the output function; is the state variable of the i-th converter; is the input variable of the i-th converter; is the output variable of the i-th converter; is the output variable of the PCC node; is the d-axis component of the PCC node voltage; is the q-axis component of the PCC node voltage.

6. The method according to claim 1, characterized in that Feedback linearization is performed on the microgrid control model to obtain a feedback control model with input-output linear decoupling, comprising the following steps: The output function of the microgrid control model is continuously derived until the derivative result includes at least one input variable, and the following is obtained: Where: r i is the output variable y i The relative degree of y i ,h i The subscript i in L indicates the serial number of the output variable; f h is the Lie derivative operator, defined as The higher-order Lie derivative is expressed as The microgrid control model is subjected to variable replacement, wherein the expression of the new variable is: Based on the Lie derivative definition and the microgrid control model, a feedback control model with linear decoupling of input and output is obtained, wherein the expression of the feedback control model is: Where: Matrix A and Matrix B are the state transfer matrix and input matrix respectively; χ is the state variable of the feedback linear model, and v is the input variable of the feedback linear model; The expression of the feedback control model at time k is: x k+1 =A k x k +B k v k 。 7. The method according to claim 1, characterized in that According to the current system state and control input of the microgrid, a voltage control instruction is generated based on a model predictive control algorithm and the feedback control model, including the following steps: According to the current system state and control input of the microgrid, based on the feedback control model, objective function, constraint conditions and optimization objective function, an optimization solution is performed to obtain a solution result; A voltage control instruction is generated according to the solution result.

8. The method according to claim 1, characterized in that The expression of the model predictive control algorithm is: Where: T is the prediction time domain, parameter matrix Q k , R k , Q T is a semi-positive coefficient matrix, and the parameter vector q k , r k ,q T is the objective function vector.

9. The method according to claim 7, characterized in that The expression of the optimization objective function of the model predictive control algorithm is: Among them, the operator represents the square of the vector norm, parameter ρ is the balance coefficient between the microgrid voltage command and the state stability target, T is the prediction time domain, χ k is the feedback linear state vector.

10. A computer storage medium, characterized in that: The computer storage medium stores computer executable instructions, and when the computer executable instructions are executed, the microgrid voltage control method described in any one of claims 1 to 9 is implemented.

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