Micro-grid coordination control method based on nonlinear system behavior trajectory prediction control

Abstract

The invention relates to the technical field of micro-grids, in particular to a micro-grid coordination control method based on nonlinear system behavior trajectory predictive control, which comprises the following steps of: exciting dynamic response of a micro-grid unit by injecting an excitation signal, collecting historical operation data and real-time operation data, and calculating the dynamic response of the micro-grid unit according to the historical operation data and the real-time operation data; respectively constructing a historical behavior description and a real-time behavior description for representing the nonlinear behavior of the unit; based on the historical behavior description and the real-time behavior description, generating an optimal control law by solving an optimization problem including a system stability target, and superposing a control instruction into a unit control ring; according to the dynamic harmonic characteristics of the grid-connected points of the units, screening the units needing behavior description updating, and periodically updating the historical behavior data of the units to adapt to the dynamic change of the system.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrids, and in particular to a microgrid coordinated control method based on nonlinear system behavior trajectory predictive control. Background Art

[0002] Microgrids can fully promote the large-scale access of renewable energy, realize highly reliable supply to local loads, and make the transition from traditional power grids to smart grids, and have made great progress. Due to the dynamic interaction between new energy, weak power grids and other elements, high-frequency oscillation and other stability problems are prone to occur, which seriously affects the safe and stable operation of microgrids and seriously restricts the absorption capacity of new energy.

[0003] At present, microgrid coordinated control methods can be divided into three categories: optimizing control parameters, additional control methods and replacing the original control.

[0004] (1) The principle of the control parameter optimization method is that the stability of the microgrid is closely related to the control parameters. By optimizing the control parameters of different control objects and different control levels, the effect of stabilizing the system operation can be achieved. (2) The additional control method is to introduce additional control on the basis of the original control method and control structure of the equipment, and adjust the control characteristics of the equipment through additional control to achieve stable operation of the system; (3) Replacing the original control means replacing the original control strategy with a new one, so that the equipment presents new control characteristics, thereby eliminating the stability problems caused by the original control.

[0005] Among the existing microgrid coordinated control methods, the optimization control parameter method and the additional control method require detailed modeling of the unit, and do not consider the differences between the actual unit and the theoretical aggregation model. When the system parameters change greatly, the system cannot operate stably and the robustness is weak. Replacing the original control requires completely replacing the controller, which has certain difficulties in practical applications, and there is a possibility of new stability problems after replacement. Summary of the invention

[0006] In view of the above-mentioned prior art, the present invention provides a microgrid coordinated control method based on nonlinear system behavior trajectory predictive control, which mainly solves the technical problems existing in the above-mentioned background technology.

[0007] To achieve the above object, the technical solution of the embodiment of the present invention is implemented as follows: A microgrid coordinated control method based on nonlinear system behavior trajectory predictive control, the control method comprising the following steps: By injecting excitation signals to stimulate the dynamic response of the microgrid unit, historical operation data and real-time operation data are collected to construct historical behavior description and real-time behavior description to characterize the nonlinear behavior of the unit respectively; Based on the historical behavior description and the real-time behavior description, the optimal control law is generated by solving the optimization problem including the system stability objective, and the control instructions are superimposed on the unit control loop; According to the dynamic harmonic characteristics of the unit's grid-connected point, the units whose behavior descriptions need to be updated are selected, and their historical behavior data are periodically updated to adapt to the dynamic changes of the system.

[0008] Optionally, the dynamic response of the microgrid unit is stimulated by injecting an excitation signal, specifically including: At this moment, the input signal to the nonlinear system of the unit is The injected noise power per unit is White noise signal to trigger the nonlinear system output Continuous excitation based on the sampling time right , Sampling is performed to obtain a length of The nonlinear historical measurement sequence of The nonlinear historical measurement sequence includes the input sequence and the output sequence ; in represents the input data at time i, and the input data also includes the d-axis current additional reference value signal of the converter and q-axis current additional reference value signal , represents the input sequence of data of length T starting at time i, Represents the output data at time i, Represents the output sequence of data of length T starting at time i.

[0009] Optionally, based on the historical behavior description and the real-time behavior description, before establishing the nonlinear behavior trajectory prediction model, it also includes constructing a nonlinear system of the new energy unit, and describing the constructed nonlinear system of the new energy unit as follows:

[0010] The nonlinear system of the new energy unit is linearized and described as:

[0011] in, and is the vector domain of the new energy unit, represents the time instance of the continuous time function of the renewable energy unit, Represents a state sequence consisting of data of length T, Represents an input sequence consisting of data of length T, represents the increment of the state sequence at time k, represents the increment of the input sequence at time k, represents the increment of the state sequence at time k+1, represents the increment of the input sequence at time k+1, Indicates in sequence and sequence The derivative of x with respect to f is, Indicates in sequence and sequence The derivative of x with respect to h is, Indicates in sequence and sequence The derivative of u with respect to f is, Indicates in sequence and sequence The derivative of u with respect to h, where represents the state quantity of the nonlinear system at time k, represents the output of the nonlinear system at time k, Represents the increment of the output sequence at time k+1.

[0012] Optionally, obtain historical and real-time behavior descriptions of nonlinear systems, including: Based on the input sequence and the output sequence, construct an input Hankel matrix and an output Hankel matrix, each of which has L rows and T-L+1 columns; The input Hankel matrix and the output Hankel matrix are divided horizontally into two parts. The first N rows are used to implicitly estimate the initial state of the system, and the last N rows are used to predict the future trajectory of the system. The division results are:

[0013] in, , , , is the submatrix after the corresponding Hankel matrix is ​​divided, and the superscript Indicates the initial recording time of the data sequence, represents the input Hankel matrix, Represents the output Hankel matrix; Historical behavior data matrix at each moment for: , taking the historical behavior data matrix as the historical behavior of the nonlinear system; Record the unit operation data starting at time k. The length of time for recording the data is , d-axis current additional reference value signal and q-axis current additional reference value signal , active power of the unit grid connection point and reactive power , construct the real-time behavior data matrix at time k as: ,in as well as They start at time k and have a length of The input sequence is as well as is a data sequence of length N to be predicted, and a real-time behavior data matrix is ​​used as the real-time behavior description.

[0014] Optionally, the optimization problem is expressed as:

[0015]

[0016] in, represents the nonlinear system input obtained by the optimization problem at time k, represents the nonlinear system output obtained by the optimization problem at time k, Represents the time from time i to time i+j-1 The vector formed, represents the time from time i to time i+j-1 The vector formed, Enter the set value for the nonlinear system at time k, is the set value of the nonlinear system output at time k, o represents the row vector, represents a column vector of length n+1, is the control cost matrix, and are the output cost matrices, Indicates the output response value that the system should finally reach. is an auxiliary slack variable, and is the regularization parameter, g is the decision variable of the above optimization problem, represents the Kronecker product.

[0017] Optionally, the MATLAB solver is used, or a program is used to solve the optimization problem and obtain the optimal control law. , and inject the converter dq axis current reference value and , realizing microgrid oscillation suppression.

[0018] Optionally, based on the dynamic harmonic characteristics of the unit's grid connection point, select the units that need to update the behavior description, specifically including: calculating the proportion of harmonic content at the grid connection point of each new energy unit, and selecting the units for the timed update algorithm according to the cumulative and threshold truncation method.

[0019] Optionally, after calculating the harmonic content proportion of the i-th new energy unit, sort the harmonic content proportions of all new energy units from large to small to obtain a sorted list, and perform cumulative calculation on the sorted harmonic content proportions. When the cumulative calculation result is ≥50%, the units included in the cumulative calculation process are selected as the set of units for which the algorithm needs to be updated.

[0020] Optionally, after Tupdate time has passed, the optimal control law and the set of units that need to update the algorithm are recalculated.

[0021] The beneficial effects of the present invention are: based on the microgrid behavior trajectory data, a nonlinear system behavior trajectory predictor is constructed, and an additional controller is constructed based on the control target to achieve coordinated control and stable operation of the microgrid. The nonlinear system behavior trajectory predictor in the invention is constructed based on the microgrid operation data, does not require accurate system modeling, avoids the reliance on detailed unit modeling in traditional methods, can adapt to the dynamic changes of system parameters, and has strong robustness. The microgrid controller in the invention only superimposes a certain disturbance on the original controller. After the system runs stably, the controller output will be set to zero, so there is no need to completely replace the original controller, which reduces the implementation complexity and cost, and avoids new stability problems that may be caused by replacing the controller. Through the cumulative and threshold truncation method of the harmonic content ratio, the units that have the greatest impact on system stability can be efficiently screened out, and their historical behavior data can be updated first to improve control efficiency. It can significantly suppress microgrid oscillations and improve system stability. It is suitable for coordinated control of microgrids in scenarios with high penetration of new energy, and has broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a flow chart of the coordinated control method of microgrid based on the predictive control of nonlinear system behavior trajectory; Figure 2 It is a control structure diagram of the microgrid coordinated control method and energy storage converter based on nonlinear system behavior trajectory predictive control; Figure 3 It is a topological diagram of the simulation system in the embodiment; Figure 4 This is a diagram showing the effect of a microgrid coordinated control method based on behavior trajectory predictive control, taking oscillation suppression as an example. DETAILED DESCRIPTION

[0023] The technical solution of the present invention is further elaborated in detail below in conjunction with the drawings and specific embodiments of the specification. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention. In the following description, the expression "some embodiments" is related to a subset of all possible embodiments, but it should be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict.

[0024] In the following description, a large number of specific details are provided to provide a more thorough understanding of the present invention. However, it is apparent to those skilled in the art that the present invention can be implemented without one or more of these details. In other examples, in order to avoid confusion with the present invention, some technical features well known in the art are not described.

[0025] It should be understood that the present invention can be implemented in different forms and should not be interpreted as being limited to the embodiments proposed herein. On the contrary, providing these embodiments will make the disclosure thorough and complete, and the scope of the present invention will be fully conveyed to those skilled in the art. And the purpose of the terms used herein is only to describe specific embodiments and is not intended to be a limitation of the present invention. When used herein, the singular forms of "one", "one" and "said / the" are also intended to include plural forms, unless the context clearly indicates another way. It should also be understood that the terms "compose" and / or "include" when used in this specification determine the presence of the features, integers, steps, operations, elements and / or parts, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, parts and / or groups. When used herein, the term "and / or" includes any and all combinations of the relevant listed items.

[0026] It should also be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be a central element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be a central element at the same time. The terms "vertical", "horizontal", "inside", "outside", "left", "right" and similar expressions used herein are for illustrative purposes only and are not intended to be the only implementation method.

[0027] In order to fully understand the present invention, a detailed structure will be proposed in the following description to illustrate the technical solution proposed by the present invention. The optional embodiments of the present invention are described in detail as follows, but in addition to these detailed descriptions, the present invention may also have other implementations.

[0028] Please refer to the attached Figure 1 The present application provides a microgrid coordinated control method based on nonlinear system behavior trajectory predictive control, the control method comprising the following steps: S1. Stimulate the dynamic response of the microgrid unit by injecting an excitation signal, collect historical operation data and real-time operation data, and construct a historical behavior description and a real-time behavior description to characterize the nonlinear behavior of the unit respectively; S2. Based on the historical behavior description and the real-time behavior description, the optimal control law is generated by solving the optimization problem including the system stability target, and the control command is superimposed into the unit control loop; S3. According to the dynamic harmonic characteristics of the unit grid connection point, select the units that need to update the behavior description, and periodically update their historical behavior data to adapt to the dynamic changes of the system.

[0029] In an optional implementation, step S1 specifically includes continuously injecting full-band white noise into the control loop of the microgrid unit to stimulate the behavior trajectory data of the microgrid unit, obtain the historical behavior description of the unit's black box model, collect the operating status data of the microgrid unit in real time, and obtain the real-time behavior description of the unit's black box model.

[0030] Furthermore, based on the historical behavior description and real-time behavior description, before establishing the nonlinear behavior trajectory prediction model, it also includes building a nonlinear system of new energy units, see Figure 2 , Figure 2 is the control block diagram of a typical new energy unit, where C 1 is the DC side capacitance, L f , R f , C f For filter inductance, resistance and capacitance, L g is the inductance of the transmission line, i d,ref is the current control loop reference value, i d , i q are the dq axis currents of the current control loop respectively, u d , u q are the voltage command values ​​of the control loop of the new energy unit, PI represents the proportional integral controller, and the constructed nonlinear system of the new energy unit is described as:

[0031] The nonlinear system of the new energy unit is linearized and described as:

[0032] in, and is the vector domain of the new energy unit, represents the time instance of the continuous time function of the renewable energy unit, Represents a state sequence consisting of data of length T, Represents an input sequence consisting of data of length T, represents the increment of the state sequence at time k, represents the increment of the input sequence at time k, represents the increment of the state sequence at time k+1, represents the increment of the input sequence at time k+1, Indicates in sequence and sequence The derivative of x with respect to f is, Indicates in sequence and sequence The derivative of x with respect to h is, Indicates in sequence and sequence The derivative of u with respect to f is, Indicates in sequence and sequence The derivative of u with respect to h, where represents the state quantity of the nonlinear system at time k, represents the output of the nonlinear system at time k, Represents the increment of the output sequence at time k+1.

[0033] Furthermore, the historical behavior description of the unit black box model is obtained, including: At this moment, the input signal to the nonlinear system of the unit is The injected noise power per unit is White noise signal to trigger the nonlinear system output Continuous excitation based on the sampling time right , Sampling is performed to obtain a length of The nonlinear historical measurement sequence includes the input sequence and the output sequence ; in represents the input data at time i, and the input data also includes the d-axis current additional reference value signal of the converter and q-axis current additional reference value signal , represents the input sequence of data of length T starting at time i, Represents the output data at time i, Represents the output sequence of data of length T starting at time i.

[0034] Furthermore, the historical behavior and real-time behavior description of the nonlinear system are obtained, including: Based on the input sequence and the output sequence, an input Hankel matrix and an output Hankel matrix are constructed respectively, each with L rows and T-L+1 columns. The jth column of the Hankel matrix is ​​the jth column of the corresponding sequence. k 0+ j -1 time to k 0+ L + j -2 time data, where L ≤ T , j ≤ T - L +1, taking the input Hankel matrix as an example:

[0035] The input Hankel matrix and the output Hankel matrix are divided horizontally into two parts. The first row is used to implicitly estimate the initial state of the system, denoted by the subscript P, and the next N rows are used to predict the future trajectory of the system, denoted by the subscript F. The division result is:

[0036] in, , , , is the submatrix after the corresponding Hankel matrix is ​​divided, and the superscript Indicates the initial recording time of the data sequence, represents the input Hankel matrix, Represents the output Hankel matrix; Historical behavior data matrix at each moment for: , taking the historical behavior data matrix as the historical behavior of the nonlinear system; Record the unit operation data starting at time k. The length of time for recording the data is , d-axis current additional reference value signal and q-axis current additional reference value signal , active power of the unit grid connection point and reactive power , construct the real-time behavior data matrix at time k as: ,in as well as They start at time k and have a length of The input sequence is as well as is a data sequence of length N to be predicted, and a real-time behavior data matrix is ​​used as the real-time behavior description.

[0037] Furthermore, the optimization problem is expressed as:

[0038]

[0039] in, represents the nonlinear system input obtained by the optimization problem at time k, represents the nonlinear system output obtained by the optimization problem at time k, represents the time from time i to time i+j-1 The vector formed, represents the time from time i to time i+j-1 The vector formed, Enter the set value for the nonlinear system at time k, is the set value of the nonlinear system output at time k, o represents the row vector, represents a column vector of length n+1, is the control cost matrix, and are the output cost matrices, Indicates the output response value that the system should finally reach. is an auxiliary slack variable, and is the regularization parameter, g is the decision variable of the above optimization problem, represents the Kronecker product.

[0040] In an optional embodiment, the MATLAB solver is used, or a program is used to solve the optimization problem and obtain the optimal control law , and inject the converter dq axis current reference value and , realizing microgrid oscillation suppression.

[0041] Furthermore, after obtaining the latest output data of the microgrid unit, the real-time behavior data matrix is ​​updated, and the real-time optimal control law is calculated again to achieve microgrid oscillation suppression.

[0042] Furthermore, based on the dynamic harmonic characteristics of the unit's grid connection point, the units that need to update their behavior descriptions are screened, specifically including: calculating the proportion of harmonic content at the grid connection point of each new energy unit, and selecting the units for the timed update algorithm according to the cumulative and threshold truncation method.

[0043] Among them, when calculating the proportion of harmonic content at the grid connection point of each new energy unit, the real-time measurement of the grid connection point voltage of the new energy unit is x(m), and the signal is processed using a 4-term 5th-order Nuttall window function and Fourier decomposition is performed as follows:

[0044] in k FFT is the frequency index, X( k FFT ) is the frequency after Fourier transformation k FFT The signal of time; is the window function, w is the variable of the window function; j is the imaginary unit; e is the natural base; M is the length of the voltage signal; FFT is the sampling point index of the voltage signal in the time domain; For the i-th new energy unit, the proportion of harmonic content at its grid connection point is calculated as:

[0045] Among them, F all is the collection of all frequency indices.

[0046] After calculating the harmonic content ratio of the i-th new energy unit, the harmonic content ratios of all new energy units are sorted from large to small to obtain a sorted list, and the sorted harmonic content ratios are cumulatively calculated. When the cumulative calculation result is ≥50%, the units included in the cumulative calculation process are selected as the set of units that need to update the algorithm. For example, there are 7 units in the formed sorted list. After accumulating the harmonic content ratios of the first 5 units, if the sum is greater than ≥50%, the 5 units are selected as the units that need to be updated.

[0047] Furthermore, after Tupdate time has passed, the optimal control law and the set of units that need to update the algorithm are recalculated, where Tupdate is generally set to 1 minute.

[0048] According to some embodiments of the present invention, based on the above method, the present invention designs a microgrid oscillation suppression system, which includes: The nonlinear system historical behavior data matrix construction module is used to collect the historical measurement sequence of the nonlinear system of the microgrid unit under continuous excitation and construct the historical behavior data matrix; The nonlinear system real-time behavior data matrix building module is used to collect the measurement sequence of the nonlinear system of the microgrid unit in real time and establish the real-time behavior data matrix; The microgrid coordinated control module is used to construct a behavior trajectory prediction controller suitable for microgrid coordinated control based on the historical behavior data matrix and real-time behavior data matrix of the nonlinear system, solve the optimization problem, and obtain the optimal control law to achieve microgrid oscillation suppression; The unit update selection module is used to select the unit with the appropriate timing update algorithm based on the cumulative and total results of the harmonic content proportion at the grid connection point, and then update its historical behavior data matrix.

[0049] In order to verify the solution in this application, the method of the present invention is verified by using a simulation model of an IEEE 9-node system. The test system replaces some units in the original IEEE 9-node system with wind turbines and energy storage, such as Figure 3 As shown in the figure, the test system's bus #1 is connected to a 12MW energy storage and a 12MW wind turbine, and bus #3 is connected to a 12MW energy storage. The converters are all grid-following control.

[0050] The effect diagram of the microgrid coordinated control method based on behavior trajectory predictive control is shown in the figure Figure 4 As shown, the upper figure is the three-phase voltage of bus #1, and the blue, yellow and red curves are the voltage curves of phase A, phase B and phase C respectively; the lower figure is the three-phase voltage of bus #1, and the blue, yellow and red curves are the voltage curves of phase A, phase B and phase C respectively. At the initial moment, 110Hz oscillation was detected in the three-phase voltage measurement signals of bus #1 and bus #3 in the system. Comparing the active power curves of the system under different controls, for the original system without any control during the period of 0~1.9 seconds, the active power output of new energy continued to oscillate, and the system was at risk of instability; at 1.9 seconds, the behavior trajectory prediction control technology proposed in the present invention was adopted, and the oscillation phenomenon in the three-phase voltage was effectively suppressed.

[0051] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. The protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A microgrid coordinated control method based on nonlinear system behavior trajectory predictive control, characterized in that: The control method comprises the following steps: By injecting excitation signals to stimulate the dynamic response of the microgrid unit, historical operation data and real-time operation data are collected to construct historical behavior description and real-time behavior description to characterize the nonlinear behavior of the unit respectively; Based on the historical behavior description and the real-time behavior description, the optimal control law is generated by solving the optimization problem including the system stability objective, and the control instructions are superimposed on the unit control loop; According to the dynamic harmonic characteristics of the unit's grid-connected point, the units whose behavior descriptions need to be updated are selected, and their historical behavior data are periodically updated to adapt to the dynamic changes of the system.

2. A microgrid coordinated control method based on nonlinear system behavior trajectory predictive control according to claim 1, characterized in that: The dynamic response of the microgrid unit is stimulated by injecting excitation signals, including: At this moment, the input signal to the nonlinear system of the unit is The injected noise power per unit is White noise signal to trigger the nonlinear system output Continuous excitation based on the sampling time right , Sampling is performed to obtain a length of The nonlinear historical measurement sequence includes the input sequence and the output sequence ; in represents the input data at time i, and the input data also includes the d-axis current additional reference value signal of the converter and q-axis current additional reference value signal , represents the input sequence of data of length T starting at time i, Represents the output data at time i, Represents the output sequence of data of length T starting at time i.

3. A microgrid coordinated control method based on nonlinear system behavior trajectory predictive control according to claim 2, characterized in that: Based on the historical behavior description and the real-time behavior description, before establishing the nonlinear behavior trajectory prediction model, it also includes constructing a nonlinear system of new energy units, and describing the constructed nonlinear system of new energy units as follows: The nonlinear system of the new energy unit is linearized and described as: in, and is the vector domain of the new energy unit, represents the time instance of the continuous time function of the renewable energy unit, Represents a state sequence consisting of data of length T, Represents an input sequence consisting of data of length T, represents the increment of the state sequence at time k, represents the increment of the input sequence at time k, represents the increment of the state sequence at time k+1, represents the increment of the input sequence at time k+1, Indicates in sequence and sequence The derivative of x with respect to f is, Indicates in sequence and sequence The derivative of x with respect to h is, Indicates in sequence and sequence The derivative of u with respect to f is, Indicates in sequence and sequence The derivative of u with respect to h, where represents the state quantity of the nonlinear system at time k, represents the output of the nonlinear system at time k, Represents the increment of the output sequence at time k+1.

4. A microgrid coordinated control method based on nonlinear system behavior trajectory predictive control according to claim 3, characterized in that: Obtain historical and real-time descriptions of nonlinear system behavior, including: Based on the input sequence and the output sequence, construct an input Hankel matrix and an output Hankel matrix, each of which has L rows and T-L+1 columns; The input Hankel matrix and the output Hankel matrix are divided horizontally into two parts. The first N rows are used to implicitly estimate the initial state of the system, and the last N rows are used to predict the future trajectory of the system. The division results are: in, , , , is the submatrix after the corresponding Hankel matrix is ​​divided, and the superscript Indicates the initial recording time of the data sequence, represents the input Hankel matrix, Represents the output Hankel matrix; Historical behavior data matrix at each moment for: , taking the historical behavior data matrix as the historical behavior of the nonlinear system; Record the unit operation data starting at time k. The length of time for recording the data is , d-axis current additional reference value signal and q-axis current additional reference value signal , active power of the unit grid connection point and reactive power , construct the real-time behavior data matrix at time k as: ,in as well as They start at time k and have a length of The input sequence is as well as is a data sequence of length N to be predicted, and a real-time behavior data matrix is ​​used as the real-time behavior description.

5. A microgrid coordinated control method based on nonlinear system behavior trajectory predictive control according to claim 4, characterized in that: The optimization problem is expressed as: in, represents the nonlinear system input obtained by the optimization problem at time k, represents the nonlinear system output obtained by the optimization problem at time k, Represents the time from time i to time i+j-1 The vector formed, Represents the time from time i to time i+j-1 The vector formed, Enter the set value for the nonlinear system at time k, is the set value of the nonlinear system output at time k, o represents the row vector, represents a column vector of length n+1, is the control cost matrix, and are the output cost matrices, Indicates the output response value that the system should finally reach. is an auxiliary slack variable, and is the regularization parameter, g is the decision variable of the above optimization problem, represents the Kronecker product.

6. A microgrid coordinated control method based on nonlinear system behavior trajectory predictive control according to claim 5, characterized in that: Use MATLAB solver, or use programming to solve the optimization problem and obtain the optimal control law , and inject the converter dq axis current reference value and , to achieve microgrid oscillation suppression.

7. A microgrid coordinated control method based on nonlinear system behavior trajectory predictive control according to claim 6, characterized in that: According to the dynamic harmonic characteristics of the unit's grid-connected point, the units that need to update their behavior descriptions are screened, including: calculating the proportion of harmonic content at the grid-connected point of each new energy unit, and selecting the units for the timed update algorithm according to the cumulative and threshold truncation method.

8. A microgrid coordinated control method based on nonlinear system behavior trajectory predictive control according to claim 7, characterized in that: After calculating the harmonic content proportion of the i-th new energy unit, the harmonic content proportions of all new energy units are sorted from large to small to obtain a sorted list, and the sorted harmonic content proportions are cumulatively calculated. When the cumulative calculation result is ≥50%, the units included in the cumulative calculation process are selected as the set of units that need to update the algorithm.

9. A microgrid coordinated control method based on nonlinear system behavior trajectory predictive control according to claim 8, characterized in that: After Tupdate time has passed, the optimal control law and the set of units that need to update the algorithm are recalculated.

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