A method for transient energy demand calculation and parameter optimization of virtual synchronous machine system

Abstract

The application discloses a kind of transient energy demand calculation and parameter optimization method of virtual synchronous machine system, and the application proposes the VSG parameter optimization method of optimal transient energy demand with the constraint condition of allowed power grid frequency maximum deviation.The model established by the application is close to the real working condition of actual VSG system grid-connected operation, and the numerical solution of VSG transient energy demand can be quickly solved under given conditions using the method, providing a reference for energy storage setting of VSG system.The application proposes an optimization scheme of VSG parameters and transient energy demand based on optimization algorithm on the transient energy demand calculation method.The scheme can quickly give the optimization parameters that minimize the transient energy demand of VSG system under the constraint condition of given system maximum frequency deviation requirement.Using the VSG transient energy demand calculation and parameter optimization method proposed by the application, a reference can be provided for the optimal design of VSG system energy storage cost.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of virtual synchronous machines, and particularly relates to a transient energy demand calculation and parameter optimization method for a virtual synchronous machine system. BACKGROUND

[0002] New energy power generation is usually connected to the power grid through a grid-connected inverter, and the traditional grid-connected inverter cannot provide voltage support, inertia support and primary frequency modulation capability for the power grid like a synchronous generator. In order to ensure the stable operation of the power grid, the proportion of the traditional new energy grid-connected inverter in the power grid is limited.

[0003] The traditional new energy grid-connected inverter usually works in a constant output power or maximum power point tracking mode and does not participate in grid frequency modulation. The increase in the installed capacity proportion of such power sources in the power system means a relative decrease in the installed capacity of power sources participating in frequency modulation, which is not conducive to the stability of the system. The virtual synchronous generator (VSG) technology is an effective method to solve the above problems. By simulating the swing equation and speed regulator (droop control) of a synchronous generator in the grid-connected inverter control system, the VSG can provide effective inertia support and primary frequency modulation for the power grid. At the same time, this characteristic requires the VSG to absorb or release additional energy when the grid frequency fluctuates. Therefore, compared with the traditional new energy power generation system, the VSG needs a larger energy storage device to meet the energy buffer demand.

[0004] For the transient energy required by the VSG to provide inertia support and primary frequency modulation, the main implementation schemes can be divided into two categories: one is to directly equip energy storage devices, such as battery energy storage devices, super capacitor energy storage devices or battery and super capacitor combined energy storage devices, etc.; the other is to use new energy power generation devices to provide energy backup, for example, in a photovoltaic power generation system, the photovoltaic operating point is controlled to deviate from the maximum power point to reserve part of the energy as transient energy for frequency adjustment.

[0005] By simulating the speed regulator model of a synchronous generator, the VSG has primary frequency modulation capability. Usually, without considering transient energy optimization, there is enough transient energy to support primary frequency modulation, and the speed regulator can be regarded as a linear model to simplify the control. However, when considering transient energy optimization, the energy storage provides transient energy and cannot continuously work in the primary frequency modulation mode, so the speed regulator model with a dead zone is needed to make the VSG stop primary frequency modulation within the allowable fluctuation range of the grid frequency.

[0006] In order to optimize the energy storage of VSG system and reduce the investment cost, a method to determine the transient energy demand of VSG system is needed. Literature [1] analyzes the transient energy demand of VSG when the input command power of VSG buffer changes suddenly. However, it does not analyze the energy demand when the load of the grid side changes, and it does not consider the governor model with dead zone. The overall model is not complete, and the effects of each parameter are not clear.

[0007] Literature [2] calculates the compensation power required by the energy storage unit when the load steps change by using the time constant of the generator. The difference between the load power step curve and the system generation power curve is the compensation power required by the energy storage unit. However, this scheme only considers the case where the generation system works in island mode, and does not consider the grid side demand in grid-connected mode. It also does not clearly show the relationship between VSG parameters and energy storage, and cannot be used as a universal transient energy demand calculation scheme.

[0008] Literature [3] analyzes the relationship between VSG droop coefficient, damping coefficient, and inertia constant and energy storage capacity based on inertia support and primary frequency modulation. It proposes a configuration method for VSG energy storage unit power and capacity. However, this research is based on the analysis of system frequency step response, and does not consider the actual response of the system frequency modulation process and the detailed physical model of the system itself. Moreover, this research does not consider the effect of the dead zone in the governor model on the transient energy demand. Literature [4] considers the model of the system itself. However, it only considers the inertia support of VSG and does not consider the primary frequency modulation. Therefore, compared to the case where inertia support and primary frequency modulation are considered simultaneously, this problem is greatly simplified and does not conform to the actual application scenario of VSG.

[0009] Based on the analysis of the related energy storage calculation schemes of VSG system, so far, there is no VSG transient energy demand calculation and parameter optimization method that comprehensively considers the inertia, damping, droop coefficient, and droop dead zone width of VSG, as well as the equivalent synchronous machine model, prime mover model, and primary and secondary frequency modulation model of the grid.

[0010] [1] Zeng Z, Shao W H, Ran L, Lv Z P, Li R. Model of virtual synchronous machine and optimal configuration of energy storage unit [J]. Power System Automation, 2015, 39(13): 22-31.

[0011] [2]HSHlaing, J.Liu, Y.Miura, H.Bevrani, and T.Ise, "Enhanced performance of a stand-alone gas-engine generator using virtual synchronous generator and energy storage system," IEEE Access, vol.7, pp.176960–176970, 2019.

[0012] [3] Zhang Bo, Zhang Xiaolei, Jia Jiaoxin, Zeng Yamin, Yan Xiangwu. VSG energy storage unit configuration method based on inertia support and primary frequency regulation requirements [J]. Automation of Electric Power Systems, 2019, 43(23): 202-209.

[0013] [4]Fang J, Li H, Tang Y, Blaabjerg F. Distributed Power System VirtualInertia Implemented by Grid-Connected Power Converters[J]. IEEE Transactionson Power Electronics, 2018, 33(10):8488-8499. Summary of the Invention

[0014] The purpose of the present invention is to overcome the above-mentioned shortcomings and provide a method for calculating the transient energy demand and optimizing the parameters of a virtual synchronous machine system. Taking the maximum grid frequency deviation (MGFD) as the constraint condition, a VSG parameter optimization method with the optimal transient energy demand is proposed.

[0015] In order to achieve the above object, the present invention comprises the following steps:

[0016] S1, establish an equivalent model of the virtual synchronous machine system in grid-connected mode;

[0017] S2, establish the small signal state space model of the equivalent model;

[0018] S3, discretize the speed regulator model with dead zone to obtain the virtual rotor angular frequency ω m_vsg Governor equations outside and inside the deadband;

[0019] S4, respectively establishes the discretized space state equations inside and outside the dead zone of the speed regulator model, combines the small signal state space model, and iteratively calculates the active power ΔP of the disturbance load.load The grid rotor angular frequency Δω caused by m_sg and the virtual synchronous machine output power ΔP out_vsg trajectory;

[0020] S5, according to the load active power ΔP load The rotor angular frequency Δω caused by m_sg and ΔP out_vsg The trajectory of the virtual synchronous machine system is obtained as W TED and the maximum frequency deviation of the power grid ω MGFD ;

[0021] S6, using single constraint conditions and multiple constraint conditions to calculate the transient energy demand W of the virtual synchronous machine system TED Optimize.

[0022] In S2, the expression of the small signal state space model is as follows:

[0023]

[0024] in,

[0025] u m =[ΔP 0_sg ΔP 0_vsg ] T

[0026] w m =ΔP load

[0027] y m =[Δω m_sg Δω m_vsg ΔP out_sg ΔP out_vsg ] T

[0028] A m is the state matrix, B m is the control input matrix, E m is the perturbation input matrix, x m State variable matrix, C m is the output matrix, F m is the perturbation output matrix, ΔP 0_sg is the active power command disturbance of the synchronous generator equivalent to the grid equivalent model, ΔP 0_vsg is the virtual synchronous machine active power command disturbance, ΔP load is the load active power command disturbance, Δω m_sg is the synchronous generator rotor angular frequency disturbance, Δω m_vsg is the virtual synchronous machine rotor angular frequency disturbance, ΔP out_sgis the active power disturbance output by the synchronous generator, ΔP out_vsg Output active power disturbance for virtual synchronous machine

[0029] In S3, the expression of the speed regulator model with dead zone is as follows:

[0030]

[0031] Among them, P0 is the active power command, k p is the active power-frequency droop coefficient, ω db is the angular frequency dead zone width, ω0 is the rated angular frequency, ω m_vsg is the virtual rotor angular frequency.

[0032] In S3, the virtual rotor angular frequency ω m_vsg The speed regulator equations outside and inside the dead band are as follows:

[0033]

[0034]

[0035] The subscript d represents the discretized state space equation, the subscript _in indicates that it is within the dead zone, and the subscript _out indicates that it is outside the dead zone.

[0036] In S6, a single constraint condition is used to calculate the transient energy demand W of the virtual synchronous machine system. TED The specific methods for optimization are as follows:

[0037] Based on the transient energy demand of the virtual synchronous machine system, the transient energy demand W of the virtual synchronous machine system is established with the virtual synchronous machine parameters as variables and the maximum load change value that the power grid needs to compensate as a given input. TED and the maximum frequency deviation of the power grid ω MGFD Solve function;

[0038] Taking the maximum frequency deviation setting value as the constraint condition, the penalty function is used to establish the fitness function of transient energy demand;

[0039] Execute optimization algorithms;

[0040] The optimal parameters of the virtual synchronous machine and the optimal transient energy demand are obtained.

[0041] Transient energy demand W TED and the maximum frequency deviation of the power grid ω MGFD The solution expression is as follows:

[0042] [ω MGFD ,W TED ]=h(X,ΔP load )

[0043]

[0044] Among them, ω MGFD is the maximum offset angular frequency, X is the virtual synchronous machine parameter vector to be optimized, M * , is the per-unit value of the virtual synchronous machine parameter to be optimized, ΔP under the single constraint condition load The maximum load change value that the power grid needs to compensate;

[0045] In S6, multiple constraints are used to calculate the transient energy demand W of the virtual synchronous machine system. TED The specific methods for optimization are as follows:

[0046] Establish the transient energy demand W of the virtual synchronous machine system with the virtual synchronous machine as the variable and n different load change values ​​of the system as the given input TED and the maximum frequency deviation of the power grid ω MGFD Solve function;

[0047] Taking the maximum frequency deviation setting value corresponding to different load conditions as the constraint condition, the penalty function is used to establish the fitness function of transient energy demand;

[0048] Execute optimization algorithms;

[0049] The optimal parameters of the virtual synchronous machine and the optimal transient energy demand are obtained.

[0050] Transient energy demand W TED and the maximum frequency deviation of the power grid ω MGFD The solution expression is as follows:

[0051] [ω MGFD (k),W TED (k)]=h(X,ΔP load (k)),k=1,2,…,n

[0052]

[0053] Among them, ω MGFD is the maximum offset angular frequency, X is the virtual synchronous machine parameter vector to be optimized, and k=1,2,…,n represents the physical quantity corresponding to the kth load change value.

[0054] Compared with the existing technology, the present invention takes the maximum allowable deviation of the grid frequency as a constraint condition and proposes a VSG parameter optimization method for the optimal transient energy demand. The model established by the present invention is close to the actual working conditions of the actual VSG system grid-connected operation. By using this method, the numerical solution of the VSG transient energy demand can be quickly obtained under given conditions, providing a reference for the energy storage setting of the VSG system. On the basis of the transient energy demand calculation method, the present invention proposes an optimization scheme for VSG parameters and transient energy demand based on an optimization algorithm. This scheme can quickly give the optimized parameters that minimize the transient energy demand of the VSG system under the constraint condition of the maximum frequency deviation requirement of a given system. The VSG transient energy demand calculation and parameter optimization method proposed by the present invention can provide a reference for the VSG system energy storage cost optimization design, thereby providing a scientific design basis for solving the bottleneck problem of large-scale commercialization of VSG. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is the equivalent model of the virtual synchronous machine grid-connected system;

[0056] Figure 2 The transient characteristic diagram of the virtual synchronous machine with dead-zone speed regulator model responding to a sudden load increase in the power grid;

[0057] Figure 3 This is the overall block diagram of the transient energy demand calculation method;

[0058] Figure 4 It is a specific implementation flow chart of the transient energy demand algorithm;

[0059] Figure 5 Flowchart of the virtual synchronous machine parameter optimization method based on particle swarm optimization algorithm when there is a single constraint condition;

[0060] Figure 6 Flowchart of the virtual synchronous machine parameter optimization method based on particle swarm optimization algorithm when there are multiple constraints. DETAILED DESCRIPTION

[0061] The present invention will be further described below with reference to the accompanying drawings.

[0062] Guided by the optimization of VSG transient energy demand, this paper proposes a numerical calculation method for the transient energy demand of the VSG system. On this basis, taking the maximum allowable deviation of the grid frequency as a constraint condition, a VSG parameter optimization method with optimal transient energy demand is proposed.

[0063] 1. Numerical calculation method of transient energy demand of VSG system.

[0064] See also Figure 1 and Figure 3In order to study the support of VSG to grid frequency, the grid model is equivalent to a synchronous generator model. The subscript vsg and sg in this model represent the parameters related to virtual synchronous generator and synchronous generator, respectively.

[0065] The small-signal state equation of the model is established by using the unified modeling method proposed in the literature “J. Liu, Y. Miura, H. Bevrani, and T. Ise, “A unified modeling method of virtual synchronous generator for multi-operation-mode analyses,” IEEE J. Emerg. Sel. Topics Power Electron., vol. 9, no. 2, pp. 2394–2409, April 2021.” Figure 1 The expression form is as follows,

[0066]

[0067] Wherein,

[0068]

[0069]

[0070] It is worth noting that in this model, in order to realize transient energy optimization, a speed governor model with dead zone is needed to make the VSG stop primary frequency modulation within the allowable fluctuation range of grid frequency. The speed governor model with dead zone can be expressed as follows:

[0071]

[0072] When ω m_vsg is in the dead zone, P in = P0, as long as the adjustment is followed by the power supply, the energy storage device of the VSG will not absorb or release energy for primary frequency modulation.

[0073] Figure 2The figure shows the transient characteristics of a VSG with a deadband governor model responding to a sudden load surge in the grid. When a sudden load surge occurs in the system, the virtual inertia immediately increases the VSG output power, while the governor only begins to respond when the system frequency deviation exceeds the deadband. The grid frequency then gradually returns to its rated value through secondary frequency regulation, and the VSG eventually ceases providing frequency support. The transient energy provided by the VSG is equal to the sum of the areas of Regions I and II. Region III represents the internal virtual energy exchange controlled by the VSG and is unrelated to the actual physical energy exchange. Region IV represents the energy absorbed from the grid by the VSG due to the increase in df / dt when the frequency returns to the deadband range. Because energy storage design should consider the maximum TED stress, the energy in Region VI should not be deducted when calculating the transient energy demand.

[0074] For the transient energy calculation of VSG, we can first calculate P out_vsg The transient trajectory of region I and region II is then integrated to obtain the corresponding transient energy. m_sg Maximum deviation of transient trajectory ω MGFD is another parameter that needs to be calculated, because the maximum frequency deviation of the power grid ω MGFD is a major constraint for VSG transient energy demand optimization. In other words, the goal of VSG transient energy demand optimization is to make ω MGFD When meeting the corresponding regulatory requirements, the minimum VSG transient energy and related VSG parameters are sought.

[0075] From equations (3) and (4), we can see that considering the actual load change scenario, ΔP load As the disturbance input, y m (1) = Δω m_sg ,y m (4) = ΔP out_vsg As output. Using the small signal state space model of formula (1), the disturbance ΔP can be calculated. load After Δω m_sg and ΔP out_vsg The trajectory of VSG transient energy demand W can be further obtained. TED and the maximum frequency deviation of the power grid ω MGFD .

[0076] However, due to the nonlinearity of the speed regulator model with dead zone shown in Equation (5), it is difficult to use this model to obtain its analytical solution. Therefore, the speed regulator model is first transformed into m_vsg Whether it is in the dead zone, the segmented processing is carried out and the corresponding state space equation is established respectively. Therefore, according to formula (1), after discretizing it using bilinear transformation (Tustin method) and other methods, ω can be obtained. m_vsgThe state space equations outside and inside the dead zone are as follows,

[0077]

[0078]

[0079] Among them, the subscript "d" represents the discretized state space equation, "in" and "out" represent being inside and outside the dead zone, respectively.

[0080] The speed regulator droop coefficients in equations (6) and (7) are set to -k p and 0. It should be noted that other parameters in these two cases may be set to different values, such as parameters related to the damping term will be set to different values ​​to maintain a constant inertia constant and a constant damping ratio. In addition, k in Eq. (5) p ω db The term will cause the working point to shift, which needs to be considered when building the model. This effect can be controlled by controlling the disturbance term ΔP 0_vsg To reflect,

[0081]

[0082] See also Figure 4 By establishing the discrete space state equations inside and outside the dead zone of the governor model, the disturbance ΔP can be obtained by iterative calculation. load After Δω m_sg and ΔP out_vsg The trajectory of VSG transient energy demand W can be further obtained. TED and the maximum frequency deviation of the power grid ω MGFD .

[0083] 2. Transient energy demand optimization method of VSG system.

[0084] To meet the system's maximum frequency offset requirements and minimize VSG transient energy demand, the present invention, based on the above-mentioned numerical calculation method for the transient energy demand of the VSG system, proposes a parameter optimization method based on an optimization algorithm. The optimization algorithm can adopt a particle swarm optimization algorithm (PSO), a genetic algorithm, or a simulated annealing algorithm. Generally, when the VSG and power grid parameters remain unchanged, the system frequency offset and the transient energy required by the VSG are directly proportional to the load change (or power generation change) that the grid needs to compensate for. That is, the greater the load change that the grid needs to compensate for, the greater the system frequency offset and the transient energy required by the VSG. Therefore, the optimization design is usually based on the maximum load change that the grid needs to compensate for. Given the frequency offset requirement that needs to be met in this situation, the optimal transient energy demand obtained can also meet the situation of smaller load changes. However, in actual applications, the VSG parameters optimized using the above-mentioned single constraint conditions may not produce a good frequency support effect under small load changes. In this case, it is necessary to consider adding one or more system frequency offset requirements under smaller load changes as optimization constraints. In this case, the frequency offset under multiple load change conditions is less than the corresponding maximum frequency offset setting value, thereby optimizing the transient energy demand and VSG parameters. Based on the two different constraints mentioned above, the transient energy demand optimization method of the VSG system proposed in this invention can be divided into: a single constraint optimization method and a multi-constraint optimization method.

[0085] See also Figure 5 , optimization method under single constraint:

[0086] When optimizing the transient energy demand of VSG, the constraint condition is that the frequency offset when the load change value that the grid needs to compensate is the largest is less than the set value of the system's maximum frequency offset.

[0087] Before executing the particle swarm optimization algorithm, W should be established according to the numerical calculation method of transient energy. TED and ω MGFD The solution function is obtained and the function to be optimized is established according to the constraints. TED and ω MGFD The solution function is expressed as follows:

[0088] [ω MGFD ,W TED ]=h(X,ΔP load ) (9)

[0089]

[0090] Among them, M * , is the per-unit value of the VSG parameter to be optimized, ΔP under the single constraint condition load Set to the maximum load (or power generation) change that the grid needs to compensate.

[0091] Taking the maximum frequency deviation setting value as the constraint condition, the penalty function method is used to establish the fitness function of transient energy demand, and its expression is as follows:

[0092]

[0093] Where c is the penalty factor of the penalty function, ω max_set The maximum frequency deviation allowed when the system's maximum load changes.

[0094] The particle swarm optimization algorithm is used to optimize the fitness function f(X) in formula (11) to solve the optimized VSG parameters that meet the constraints.

[0095] See also Figure 6 , optimization method under multiple constraints:

[0096] When the single constraint optimization method cannot produce a good frequency support effect under the condition of small load changes, it is necessary to appropriately add constraints for small load changes. The constraints at this time should be: the frequency offset of the system under multiple load change conditions is less than the corresponding system frequency offset setting value. Similarly, the W under multiple load change values ​​should be established based on the numerical calculation method of transient energy. TED and ω MGFD The solution function is as follows

[0097] [ω MGFD (k),W TED (k)]=h(X,ΔP load (k)),k=1,2,…,n (12)

[0098] Taking the maximum frequency deviation setting value corresponding to different load conditions as the constraint condition, multiple penalty functions are constructed to establish the fitness function of transient energy demand, which is expressed as follows:

[0099]

[0100] Where W TED_max It is the transient energy demand corresponding to the maximum change value of system load.

[0101] The particle swarm optimization algorithm is used to optimize the fitness function g(X) shown in formula (13), and the optimized VSG parameters that meet the corresponding frequency offset settings under different load change conditions can be solved.

[0102] The application is oriented to VSG transient energy demand optimization, proposes a numerical calculation method for transient energy demand of VSG system, and on this basis, proposes a VSG parameter optimization method with optimal transient energy demand as a constraint condition of the maximum deviation of the allowed power grid frequency. The frequency support of the power grid is the main advantage of the VSG system, and the energy storage equipped by the VSG is the key to the frequency support provided by the VSG, therefore, the calculation and optimization design of the transient energy demand of the VSG system for frequency support is the basis and key to the cost optimization of the VSG energy storage device. However, at present, there is no relevant research and scheme to propose a more specific calculation method for the transient energy demand of the VSG.

Claims

1. A method for calculating transient energy demand and optimizing parameters of a virtual synchronous machine system, characterized in that: The following steps are involved: S1, establish an equivalent model of the virtual synchronous machine system in grid-connected mode; S2, establish the small signal state space model of the equivalent model; the expression of the small signal state space model is as follows: in, A m is the state matrix, B m is the control input matrix, E m is the perturbation input matrix, The state variable matrix, is the output matrix, is the perturbation output matrix, is the active power command disturbance of the synchronous generator equivalent to the grid equivalent model, is the virtual synchronous machine active power instruction disturbance, is the load active power, is the synchronous generator rotor angular frequency disturbance, is the virtual rotor angular frequency disturbance, is the active power disturbance output by the synchronous generator, Output active power disturbance for virtual synchronous machine; S3, discretize the speed regulator model with dead zone to obtain the virtual rotor angular frequency The speed regulator equations outside and inside the dead zone; the speed regulator model with dead zone is expressed as follows: in, is the active power instruction, is the active power-frequency droop coefficient, is the angular frequency dead zone width, is the rated angular frequency, is the virtual rotor angular frequency; S4, respectively establishes the discrete space state equations in the dead zone and outside the dead zone of the speed regulator model, combines the small signal state space model, and iteratively calculates the load active power The synchronous generator rotor angular frequency disturbance caused by and virtual synchronous machine output active power disturbance trajectory; S5, according to the load active power The synchronous generator rotor angular frequency disturbance caused by and virtual synchronous machine output active power disturbance The trajectory of the virtual synchronous machine system is obtained Maximum frequency deviation from the grid ; S6, using single constraint and multiple constraint conditions to calculate the transient energy demand of the virtual synchronous machine system Optimize.

2. The method for calculating transient energy demand and optimizing parameters of a virtual synchronous machine system according to claim 1, characterized in that: In S3, the virtual rotor angular frequency The speed regulator equations outside and inside the dead band are as follows: Among them, the corner mark is the discretized state space equation, and the subscript Indicates that it is in the dead zone, the corner mark Indicates being outside the dead zone.

3. The method for calculating transient energy demand and optimizing parameters of a virtual synchronous machine system according to claim 1, characterized in that: In S6, a single constraint condition is used to calculate the transient energy demand of the virtual synchronous machine system. The specific methods for optimization are as follows: Establish the transient energy demand of the virtual synchronous machine system with the virtual synchronous machine parameters as variables and the maximum load change value that the power grid needs to compensate for as a given input Maximum frequency deviation from the grid Solve function; Taking the maximum frequency deviation setting value as the constraint condition, the penalty function is used to establish the fitness function of transient energy demand; Execute optimization algorithms; The optimal parameters of the virtual synchronous machine and the optimal transient energy demand are obtained.

4. The method for calculating transient energy demand and optimizing parameters of a virtual synchronous machine system according to claim 3, characterized in that: Transient energy demand Maximum frequency deviation from the grid The solution expression is as follows: in, ω MGFD is the maximum frequency deviation, is the virtual synchronous machine parameter vector to be optimized.

5. The method for calculating transient energy demand and optimizing parameters of a virtual synchronous machine system according to claim 1, characterized in that: In S6, multiple constraints are used to calculate the transient energy demand of the virtual synchronous machine system. The specific methods for optimization are as follows: Establish a virtual synchronous machine as a variable, system n Different load variation values ​​are used to calculate the transient energy demand of the virtual synchronous machine system for a given input. Maximum frequency deviation from the grid Solve function; Taking the maximum frequency deviation setting value corresponding to different load conditions as the constraint condition, the penalty function is used to establish the fitness function of transient energy demand; Execute optimization algorithms; The optimal parameters of the virtual synchronous machine and the optimal transient energy demand are obtained.

6. The method for calculating transient energy demand and optimizing parameters of a virtual synchronous machine system according to claim 5, characterized in that: Transient energy demand Maximum frequency deviation from the grid The solution expression is as follows: in, ω MGFD is the maximum frequency deviation, is the virtual synchronous machine parameter vector to be optimized, For the k The active power of the load, k= 1,2,…, n Indicates the k The physical quantity corresponding to a load change value.

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