Hybrid micro-grid control method based on MPC

Abstract

The invention discloses a hybrid micro-grid control method based on MPC, and belongs to the technical field of power grid control, and the method comprises the steps: S1, building a state-space equation of a hybrid micro-grid based on a variable quantity representing the stability of the hybrid micro-grid and an influence variable influencing the stability of the hybrid micro-grid; s2, acquiring an output equation based on the variable quantity, and discretizing the state-space equation and the output equation based on a forward Euler method to acquire a prediction equation; and S3, constructing a performance index function of the MPC according to the prediction equation, solving the performance index function by taking the minimum value of the performance index function as a control optimization target to obtain a final value of a control variable, and controlling the hybrid microgrid based on the final value of the control variable. The problems of insufficient direct-current voltage support, dynamic coupling oscillation and steady-state power distribution imbalance are synchronously solved by considering the stability of both sides of alternating current and direct current, so that the technical problem that the stability of the hybrid microgrid is difficult to comprehensively improve in the prior art is solved.

Description

Technical Field

[0001] This invention relates to the field of power grid control technology, specifically to a hybrid microgrid control method based on MPC. Background Technology

[0002] With the widespread use of new energy sources, microgrid power generation technology has ushered in a new development opportunity. Hybrid microgrids integrate the advantages of DC and AC microgrids. Compared with single microgrids, they achieve complementary advantages between traditional and new energy sources, expand the energy supply range, and significantly improve energy utilization. In a hybrid microgrid system, DC and AC microgrids are connected through interconnecting converters. Currently, in islanded operation of hybrid microgrids, interconnecting converters mostly adopt double-droop VSG control, which can enhance AC-side inertia, maintain AC-side frequency stability, and coordinate the output power of AC and DC subgrids in steady state. However, it provides insufficient support for DC-side voltage, resulting in large fluctuations or even exceeding limits during load disturbances. Furthermore, the connection between AC and DC microgrids through interconnecting converters leads to mutual coupling in their dynamic processes, reducing system damping and causing power oscillations. In steady state, the load power of AC and DC microgrids cannot be fully distributed according to the capacity of each subgrid, resulting in steady-state errors. These bottlenecks directly restrict the overall stability of hybrid microgrids. Therefore, how to comprehensively improve the stability of hybrid microgrids has become a core technical challenge that urgently needs to be overcome. Summary of the Invention

[0003] To address the technical problem that existing technologies struggle to comprehensively improve the stability of hybrid microgrids, this invention provides a hybrid microgrid control method based on MPC. This method establishes a state-space equation by characterizing the changes in hybrid microgrid stability and the variables influencing its stability. The state-space equation and output equation are discretized using the forward Euler method to obtain prediction equations. Based on these prediction equations, a performance index function for MPC is constructed. The performance index function is then solved with the goal of minimizing its value to obtain the final values ​​of the control variables. These final values ​​are then used to control the hybrid microgrid. By considering both AC and DC stability, this method simultaneously solves the problems of insufficient DC voltage support, dynamic coupling oscillations, and steady-state power distribution imbalance, thus addressing the technical challenge of comprehensively improving the stability of hybrid microgrids using existing technologies.

[0004] To address the aforementioned technical problems, this invention provides a hybrid microgrid control method based on MPC, comprising the following steps: S1: Establish the state-space equation of the hybrid microgrid based on the changes in the stability of the hybrid microgrid and the variables that affect the stability of the hybrid microgrid; S2: Obtain the output equation based on the change, and obtain the prediction equation by discretizing the state space equation and the output equation respectively based on the forward Euler method; S3: Construct the performance index function of MPC based on the prediction equation, solve the performance index function with the minimum value of the performance index function as the control optimization objective to obtain the final value of the control variable, and control the hybrid microgrid based on the final value of the control variable.

[0005] Preferably, S1 includes: S11: Establish the DC microgrid energy storage in the hybrid microgrid as vi control, establish the AC microgrid energy storage in the hybrid microgrid as droop control to obtain the equivalent model of the hybrid microgrid, and obtain the power balance relationship equation of the AC microgrid and the DC microgrid, and the power loop equation of the equivalent model. S12: Perform small-signal processing on the power loop equation and power balance equation, and obtain the state-space equation by taking the changes as state variables and the influence variables as control variables.

[0006] Preferably, in S1, the changes include the voltage change of the DC bus in the hybrid microgrid, the frequency change of the AC bus in the hybrid microgrid, and the power factor angle change; the influencing variable is the VSG reference power change.

[0007] Preferably, in S2, obtaining the output equation based on the change includes: The voltage and frequency changes are obtained and normalized. The difference between the normalized voltage and frequency changes is used as the output change to obtain the output equation.

[0008] Preferably, the difference between the normalized voltage change and the frequency change is used as the output change to obtain the normalized voltage change in the output equation. , This indicates the amount of voltage change that has been obtained. Indicates the range of voltage variation at the DC bus; Frequency change after normalization , This indicates the amount of frequency change obtained. Indicates the rated frequency variation range of the AC bus; The output equations include: ; ; in, This indicates the amount of change in the output. This represents the first output coefficient matrix. A change matrix representing the change in quantity.

[0009] Preferably, in S2, the step of discretizing the state-space equation and the output equation based on the forward Euler method to obtain the prediction equation includes: The first linear discrete equation is obtained by discretizing the state-space equation using the forward Euler method, and the second linear discrete equation is obtained by discretizing the output equation using the forward Euler method. The prediction equation is obtained based on the first linear discrete equation, the second linear discrete equation, and the preset conditions in MPC.

[0010] Preferably, the first linear discrete equation is: ,in, Represents the change matrix of state variables Step value, The discrete matrix representing the coefficients of the state variables. Represents the change matrix of state variables Step value, The discrete matrix representing the coefficients of the control variables. This represents the k-step value of the control variable. The coefficient discrete matrix representing the disturbance quantity. This represents the k-step value of the matrix representing the change in the disturbance amount; The second linear discrete equation is: ,in, This represents the k-step value of the output change in the output equation. This represents the k-1 step value of the output change in the output equation; The prediction equation is: ;in, This represents the output change matrix. The coefficient matrix representing the state variables. This represents the second output coefficient matrix. The coefficient matrix representing the control variables. This represents the matrix showing the changes in the control variables. The coefficient matrix represents the amount of disturbance.

[0011] Preferably, in S3, the step of constructing the performance index function of MPC based on the prediction equation includes: The voltage value of the DC bus and the frequency value of the AC bus in the hybrid microgrid are obtained. The obtained voltage and frequency values ​​are normalized, and the difference between the normalized voltage and frequency values ​​is used as the initial prediction value. The initial prediction value is input into the prediction equation to obtain the prediction process value. The initial performance index function is obtained based on the difference between the prediction process value and the expected output value of the prediction equation, as well as the influencing variables. The initial performance index function is then converted into the standard form of quadratic programming to obtain the performance index function.

[0012] Preferably, the expression for normalizing the acquired voltage and frequency values ​​is as follows: ; The k-step value represents the normalized value of the DC bus voltage. This represents the k-step sample value of the DC bus voltage. The k-step value representing the normalized value of the VSG frequency. This represents the k-step value of the VSG frequency sampling value; The predicted initial value .

[0013] Preferably, in S3, the step of solving the performance index function to obtain the final value of the control variable with the goal of minimizing the value of the performance index function includes: The goal of control optimization is to minimize the value of the performance index function. The change value of the control variable is obtained by solving the performance index function using the interior point method. The sum of the change value of the control variable and the initial value of the control variable is taken as the final value of the control variable.

[0014] By adopting the above technical solution, the present invention has the following advantages: State-space equations are established by measuring the voltage changes of the DC bus, the frequency changes of the AC bus, and the VSG reference power changes. Output equations are obtained from these changes. The forward Euler method is used to discretize the state-space equations and the output equations to obtain predictive equations that can predict future changes in DC voltage and AC frequency. Then, the performance index function of MPC is constructed from the predictive equations. The performance index function is solved with the minimum value of the performance index function as the control optimization objective. This achieves synchronous regulation on both AC and DC sides, taking into account the stability of both AC and DC sides. It simultaneously solves the problems of insufficient DC voltage support, dynamic coupling oscillation, and steady-state power distribution imbalance, thus solving the technical problem that existing technologies cannot comprehensively improve the stability of hybrid microgrids. Attached Figure Description

[0015] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings. The drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings.

[0016] Figure 1 This is a flowchart illustrating the hybrid microgrid control method based on MPC according to the present invention. Figure 2 This is a diagram of the double drooping VSG control topology using MPC in the hybrid microgrid control method based on MPC of this invention. Figure 3 This is a schematic diagram illustrating the effect of dual droop VSG control in the MPC-based hybrid microgrid control method of the present invention. Figure 4 This is a schematic diagram illustrating the effect of MPC-based dual-droop VSG control in the hybrid microgrid control method of this invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only one preferred embodiment of this invention and are only used to explain this invention. They do not limit the scope of protection of this invention. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0018] Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe the operations (or steps) as sequential processes, many of the operations (or steps) can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but it may also have additional steps not included in the figures; the process may correspond to a method, function, procedure, subroutine, subroutine, etc.

[0019] Example 1: like Figure 1 As shown, the hybrid microgrid control method based on MPC includes the following steps: S1: Establish the state-space equation of the hybrid microgrid based on the changes in the stability of the hybrid microgrid and the variables that affect the stability of the hybrid microgrid.

[0020] In this embodiment, the hybrid microgrid is specifically an islanded hybrid microgrid. The AC / DC side of the islanded hybrid microgrid contains distributed power sources, energy storage, and loads, and the AC / DC microgrids are connected by interconnected converters.

[0021] S1 includes: S11: Establish the DC microgrid energy storage in the hybrid microgrid as vi control, establish the AC microgrid energy storage in the hybrid microgrid as droop control to obtain the equivalent model of the hybrid microgrid, and obtain the power balance relationship equation of the AC microgrid and the DC microgrid, and the power loop equation of the equivalent model. S12: Perform small-signal processing on the power loop equation and power balance equation, and obtain the state-space equation by taking the changes as state variables and the influence variables as control variables.

[0022] VI control refers to voltage-current control, and its equivalent model uses the MPC double droop VSG control topology, such as... Figure 2 As shown, the MPC receives the voltage change of the DC bus. and frequency change of the AC bus Combined with MPC to solve for the VSG reference power change This enables coordinated predictive control of both AC and DC sides, that is, by adjusting... This approach suppresses AC-side frequency oscillations and indirectly coordinates DC-side voltage stability, addressing issues such as AC / DC coupling and dynamic oscillations in hybrid microgrids. In this embodiment, the power loop equations include: ; ; ; in, This is the power supply voltage. and These are the sampled values ​​of the rated DC bus voltage and the DC bus voltage, respectively. / , / These are the AC / DC side power supply droop factor and the double droop factor, respectively. , , , , These are, respectively, the DC source output power, the AC reference power, the AC source output power, the VSG reference power, and the VSG output power. , , These are the AC bus frequency, VSG frequency sample value, and rated frequency, respectively. and These represent the VSG inertia and damping coefficient, respectively. The power balance equations include: ; ; ; ; in, This represents the DC bus capacitance, X is the line impedance, and δ is the power factor angle. and The AC and DC side loads are respectively, and E and U are the VSG output voltage and the common coupling point voltage, respectively. The state-space equations include: ; in, , , These represent the change matrix of the state variables, the change in VSG reference power, and the change in disturbance, respectively. The derivative matrix of the change matrix of the state variables; , , These are the derivatives of the change in DC bus voltage sampling value, the derivative of the change in VSG frequency, and the derivative of the change in power factor angle, respectively. , , These are the coefficient matrices for the state variables, the control variables, and the disturbance variables, respectively.

[0023] Specifically, in S1, the changes include the voltage change of the DC bus in the hybrid microgrid, the frequency change of the AC bus in the hybrid microgrid, and the power factor angle change; the influencing variable is the VSG reference power change.

[0024] S2: Obtain the output equation based on the change, and obtain the prediction equation by discretizing the state space equation and the output equation respectively based on the forward Euler method.

[0025] In some embodiments, S2, obtaining the output equation based on the change amount includes: The voltage and frequency changes are obtained and normalized. The difference between the normalized voltage and frequency changes is used as the output change to obtain the output equation.

[0026] Specifically, the difference between the normalized voltage change and the frequency change is used as the output change to obtain the normalized voltage change in the output equation. , This indicates the amount of voltage change that has been obtained. Indicates the range of voltage variation at the DC bus; Frequency change after normalization , This indicates the amount of frequency change obtained. Indicates the rated frequency variation range of the AC bus; The output equations include: ; ; in, This indicates the amount of change in the output. This represents the first output coefficient matrix. This represents the change matrix of the variable. The magnitude of the output change directly reflects the inertia distribution of the AC / DC subnet and the magnitude of the system damping. The first output coefficient matrix is ​​the output coefficient matrix of the output equation.

[0027] In some other embodiments, S2, the step of discretizing the state-space equation and the output equation based on the forward Euler method to obtain the prediction equation includes: The first linear discrete equation is obtained by discretizing the state-space equation using the forward Euler method, and the second linear discrete equation is obtained by discretizing the output equation using the forward Euler method. The prediction equation is obtained based on the first linear discrete equation, the second linear discrete equation, and the preset conditions in MPC.

[0028] The forward Euler method is a numerical method for solving ordinary differential equations. Its core idea is to approximate the state at the next time step using the rate of change at the current time step, achieving discretization of the continuous dynamic process through linear recursion. By discretizing the state-space equations and output equations using the forward Euler method, the prediction equations obtained through the first linear discretization equation, the second linear discretization equation, and the preset conditions in the MPC (Multi-Process Control) mechanism possess the ability to predict future changes in DC voltage and AC frequency, thus enabling the active adjustment to maintain the stability of the hybrid microgrid. Specifically, the preset conditions in the MPC refer to setting the prediction time domain to P, the control time domain to L, and ensuring that after the control time domain L, the control quantity remains unchanged, while the value of the disturbance quantity change matrix remains unchanged after k.

[0029] Specifically, the first linear discrete equation is: ,in, Represents the change matrix of state variables Step value, The discrete matrix representing the coefficients of the state variables. Represents the change matrix of state variables Step value, The discrete matrix representing the coefficients of the control variables. This represents the k-step value of the control variable. The coefficient discrete matrix representing the disturbance quantity. This represents the k-step value of the matrix representing the change in the disturbance amount; The second linear discrete equation is: ,in, This represents the k-step value of the output change in the output equation. This represents the k-1 step value of the output change in the output equation; The prediction equation is: ;in, This represents the output change matrix. The coefficient matrix representing the state variables. This represents the second output coefficient matrix. The coefficient matrix representing the control variables. This represents the matrix showing the changes in the control variables. The coefficient matrix represents the amount of disturbance.

[0030] Discrete matrix of coefficients of state variables , Indicates the sampling time. Represents the third-order identity matrix, the discrete matrix of the coefficients of the control variables. Discrete matrix of the coefficients of the disturbance The second output coefficient matrix is ​​specifically the output coefficient matrix of the prediction equation.

[0031] S3: Construct the performance index function of MPC based on the prediction equation, solve the performance index function with the minimum value of the performance index function as the control optimization objective to obtain the final value of the control variable, and control the hybrid microgrid based on the final value of the control variable.

[0032] In some embodiments, S3, the step of constructing the performance metric function of MPC based on the prediction equation includes: The voltage value of the DC bus and the frequency value of the AC bus in the hybrid microgrid are obtained. The obtained voltage and frequency values ​​are normalized, and the difference between the normalized voltage and frequency values ​​is used as the initial prediction value. The initial prediction value is input into the prediction equation to obtain the prediction process value. The initial performance index function is obtained based on the difference between the prediction process value and the expected output value of the prediction equation, as well as the influencing variables. The initial performance index function is then converted into the standard form of quadratic programming to obtain the performance index function.

[0033] Specifically, the expression for normalizing the acquired voltage and frequency values ​​is as follows: ; The k-step value represents the normalized value of the DC bus voltage. This represents the k-step sample value of the DC bus voltage. The k-step value representing the normalized value of the VSG frequency. This represents the k-step value of the VSG frequency sampling value; The predicted initial value .

[0034] In this embodiment, the initial performance index function is specifically as follows: ; in, For the desired output matrix, To output the weighted coefficient matrix of the change, This is the weighting coefficient matrix for the control variables. The performance index function is as follows: ; Where b is the prediction difference matrix, The smaller the function value J, the smaller the difference between the predicted process value (i.e., the output variable value) and the expected output value, based on minimizing the control variables. In this case, the hybrid microgrid will have higher accuracy in allocating inertia according to each microgrid, better system damping, and smaller steady-state error.

[0035] In S3, the step of solving the performance index function to obtain the final value of the control variable with the goal of minimizing the value of the performance index function includes: The goal of control optimization is to minimize the value of the performance index function. The change value of the control variable is obtained by solving the performance index function using the interior point method. The sum of the change value of the control variable and the initial value of the control variable is taken as the final value of the control variable.

[0036] The interior-point method is a numerical algorithm for solving constrained optimization problems. Its core principle is to iteratively search for the optimal solution within the feasible region. By introducing a barrier function, it avoids touching the constraint boundary during the iteration process. In this embodiment, the interior-point method is used to solve the performance index function to obtain the change value of the control variable at time k+1. And the change value of the control variable at time k+1 With the initial value of the control variable The sum of these values ​​serves as the final value of the control variables. By controlling the hybrid microgrid using these final values, synchronous regulation on both the AC and DC sides is achieved, ensuring stability on both sides. In this embodiment, to improve the adaptability of the final control variable values, the voltage value of the DC bus and the frequency value of the AC bus are continuously collected to update the performance index function. This obtains control variable values ​​that are highly adapted to the hybrid microgrid state, improving both the adaptability of the control variable values ​​and the stability of the hybrid microgrid.

[0037] The parameter values ​​for the hybrid microgrid are shown in Table 1: Table 1. Parameter Values ​​for Hybrid Microgrids If 5kW is applied to the DC and AC sides at times 1s and 2s respectively, what are the DC bus voltage values ​​using dual-droop VSG control and MPC-based dual-droop VSG control? The normalized value of the frequency of the AC bus, i.e., the VSG frequency. like Figure 3 and Figure 4 As shown, by Figure 3 and Figure 4Therefore, compared to standard dual-droop VSG control, MPC-based dual-droop VSG control can support DC voltage during dynamic load loading, distribute inertia according to inertia requirements, and eliminate oscillations during dynamic processes. Furthermore, in steady state, MPC-based dual-droop VSG control offers more precise power distribution and eliminates oscillations during dynamic processes. and The steady-state error.

[0038] The substantial effects of this invention are: State-space equations are established by measuring the voltage changes of the DC bus, the frequency changes of the AC bus, and the VSG reference power changes. Output equations are obtained from these changes. The forward Euler method is used to discretize the state-space equations and the output equations to obtain predictive equations that can predict future changes in DC voltage and AC frequency. Then, the performance index function of MPC is constructed from the predictive equations. The performance index function is solved with the minimum value of the performance index function as the control optimization objective. This achieves synchronous regulation on both AC and DC sides, takes into account the stability of both AC and DC sides, and simultaneously solves the problems of insufficient DC voltage support, dynamic coupling oscillation, and steady-state power distribution imbalance. Thus, it solves the technical problem that existing technologies cannot comprehensively improve the stability of hybrid microgrids. The performance index function of MPC is optimized with future predictions as the core, and the predictions directly incorporate DC voltage changes and AC frequency changes. These two types of variables can accurately characterize the inertia distribution state and system oscillation trend of the hybrid microgrid, providing comprehensive state feedback for dynamic control. The performance index function establishes a bridge between DC voltage changes, AC frequency changes and control variables, thereby achieving reasonable allocation of hybrid microgrid inertia and effective suppression of oscillations by obtaining the optimal control variable values.

[0039] The specific embodiments described above are preferred embodiments of the hybrid microgrid control method based on MPC of the present invention, and are not intended to limit the specific scope of the present invention. The scope of the present invention includes but is not limited to the specific embodiments described above. All equivalent changes made in accordance with the shape and structure of the present invention are within the protection scope of the present invention.

Claims

1. A method for MPC-based hybrid microgrid control, characterized in that, The method comprises the following steps: S1: establishing a state space equation of the hybrid micro-grid based on a variation quantity representing stability of the hybrid micro-grid and an influence variable affecting stability of the hybrid micro-grid; S2: obtaining an output equation based on the variation quantity, and discretely processing the state space equation and the output equation based on a forward Euler method to obtain a prediction equation; S3: constructing a performance index function of the MPC according to the prediction equation, solving the performance index function to obtain a final value of a control variable with a minimum value of the performance index function as a control optimization target, and controlling the hybrid micro-grid based on the final value of the control variable.

2. The MPC-based hybrid microgrid control method of claim 1, wherein, The S1 comprises: S11: establishing energy storage in a DC micro-grid in the hybrid micro-grid as v-i control, establishing energy storage in an AC micro-grid in the hybrid micro-grid as droop control to obtain an equivalent model of the hybrid micro-grid, and obtaining a power balance equation of the AC micro-grid and the DC micro-grid and a power ring equation of the equivalent model; S12: performing small signal processing on the power ring equation and the power balance equation, and obtaining the state space equation with the variation quantity as a state variable and the influence variable as a control variable.

3. The MPC-based hybrid microgrid control method of claim 1, wherein, In the S1, the variation quantity comprises a voltage variation quantity of a DC bus in the hybrid micro-grid, a frequency variation quantity of an AC bus in the hybrid micro-grid, and a power factor angle variation quantity; and the influence variable is a VSG reference power variation quantity.

4. The MPC-based hybrid microgrid control method of claim 3, wherein, In the S2, the output equation is obtained based on the variation quantity, comprising: obtaining the voltage variation quantity and the frequency variation quantity, and performing normalization processing on the voltage variation quantity and the frequency variation quantity, and obtaining an output variation quantity as a difference between the voltage variation quantity and the frequency variation quantity after the normalization processing to obtain the output equation.

5. The MPC-based hybrid microgrid control method of claim 4, wherein, The difference between the normalized voltage variation and the frequency variation is obtained as an output variation , represents the voltage variation obtained, represents the range of variation of the voltage of the DC bus; frequency variation amount after normalization processing , indicates the acquired frequency variation amount, indicates the frequency rated variation range of the AC bus The output equation comprises: ； ； wherein denotes an output change, denotes a first output coefficient matrix, denotes a change matrix of the change.

6. The MPC-based hybrid microgrid control method of claim 5, wherein, In the S2, the prediction equation is obtained by discretely processing the state space equation and the output equation based on the forward Euler method, comprising: discretely processing the state space equation based on the forward Euler method to obtain a first linear discrete equation, and discretely processing the output equation based on the forward Euler method to obtain a second linear discrete equation; obtaining the prediction equation based on the first linear discrete equation, the second linear discrete equation, and a preset condition in the MPC.

7. The MPC-based hybrid microgrid control method of claim 6, wherein, said first linear discrete equation is: wherein, denotes the k-step value of the change matrix of the state variable, denotes the k-step value of the change matrix of the state variable, denotes the coefficient discrete matrix of the state variable, denotes the k-step value of the change matrix of the state variable, denotes the k-step value of the change matrix of the state variable, denotes the coefficient discrete matrix of the control variable, denotes the k-step value of the control variable, denotes the coefficient discrete matrix of the disturbance variable, denotes the k-step value of the change matrix of the disturbance variable; The second linear discrete equation is: wherein, represents the k-step value of the output change in the output equation, represents the k-1 step value of the output change in the output equation; The prediction equation is: ; wherein represents an output change matrix, represents a coefficient matrix of state variables, represents a second output coefficient matrix, represents a coefficient matrix of control variables, represents a change matrix of control variables, represents a coefficient matrix of disturbance quantities.

8. The MPC-based hybrid microgrid control method of claim 5, wherein, In the S3, the performance index function of the MPC is constructed according to the prediction equation, comprising: obtaining a voltage value of a DC bus and a frequency value of an AC bus in the hybrid micro-grid, performing normalization processing on the obtained voltage value and frequency value, and obtaining a prediction initial value as a difference between the voltage value and the frequency value after the normalization processing; inputting the prediction initial value into the prediction equation to obtain a prediction process value, obtaining an initial performance index function based on a difference between the prediction process value and an expected output value of the prediction equation and the influence variable, converting the initial performance index function into a quadratic programming standard form, and then obtaining the performance index function.

9. The MPC-based hybrid microgrid control method of claim 8, wherein, The expression for the normalization processing on the obtained voltage value and frequency value is: ； The k-step value represents the normalized value of the DC bus voltage. This represents the k-step sampled value of the DC bus voltage. The k-step value representing the normalized value of the VSG frequency. This represents the k-step value of the VSG frequency sampling value; The predicted initial value .

10. The MPC-based hybrid microgrid control method of claim 1, wherein, In the S3, the final value of the control variable is obtained by solving the performance index function with a minimum value of the performance index function as the control optimization target, comprising: Taking the minimum value of the performance index function as the control optimization target, the performance index function is solved by the interior point method to obtain the change value of the control variable, and the sum of the change value of the control variable and the initial value of the control variable is taken as the final value of the control variable.

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