A distribution network optimization scheduling method based on virtual synchronous generator technology and power flow analysis

Abstract

The present invention belongs to the technical field of distribution network operation, and specifically relates to a distribution network optimization dispatching method based on virtual synchronous generator technology and power flow analysis. The method comprises: a distribution network dispatching center performs optimal power flow calculation according to control parameters to obtain optimal control parameters; the virtual synchronous generator performs power dispatching according to the optimal control parameters; the optimal control parameters are corrected to obtain corrected control parameters; the virtual synchronous generator feeds the corrected control parameters back to the distribution network dispatching center to continue the next dispatching. The present invention reduces the number of iterations of the optimal power flow calculation, saves the optimal power flow calculation time, has high distribution network operation efficiency, and can produce good economic benefits.

Description

Technical Field

[0001] The present invention belongs to the technical field of distribution network operation, and in particular relates to a distribution network optimization scheduling method based on virtual synchronous generator technology and power flow analysis. Background Art

[0002] The proportion of renewable energy in current power grid systems is increasing. Most renewable energy sources require power electronic inverters for grid connection. Because inverters lack the inertia of synchronous generators, grid stability is reduced. To address this issue, virtual synchronous generator technology has emerged. Current virtual synchronous generator technology is mostly limited to single-machine, multi-machine, or microgrid applications. Little research has been conducted on how to dispatch virtual synchronous generators within larger-scale systems like distribution networks.

[0003] Existing virtual synchronous generator technology is mostly limited to single-machine, multi-machine, or microgrid applications. From a control perspective, the given control variables (typically active and reactive power) are constant or set by local controllers. Large-scale integration of such distributed power sources into the distribution network, without the control of a dispatching center, can negatively impact the economic operation of the network.

[0004] There is an urgent need for a distribution network optimization dispatching method based on virtual synchronous generator technology, which can enable each virtual synchronous generator to obey the dispatching of the dispatching center and realize the economic operation of the distribution network. Summary of the Invention

[0005] In view of the shortcomings of the existing technology, the present invention proposes a distribution network optimization scheduling method based on virtual synchronous generator technology and power flow analysis, which includes:

[0006] S1: The distribution network dispatching center performs optimal power flow calculation based on the control parameters to obtain the optimal control parameters;

[0007] S2: Virtual synchronous generators perform power dispatch according to optimal control parameters;

[0008] S3: Correct the optimal control parameters to obtain corrected control parameters;

[0009] S4: The virtual synchronous generator feeds back the corrected control parameters to the distribution network dispatching center, and returns to step S1.

[0010] Preferably, the control parameters include the active power of the node's load, the reactive power of the node's load, the active power output by the generator, and the reactive power output by the generator.

[0011] Preferably, the process of obtaining the optimal control parameters includes:

[0012] S11: Initialize preset variables of the power distribution network;

[0013] S12: Calculate the power flow of the distribution network to obtain initial control parameters;

[0014] S13: Determine whether the initial control parameters meet the range constraint conditions and the grid operation constraint conditions. If so, execute step S14; otherwise, return to step S11;

[0015] S14: Set the optimization objective function and determine whether the initial control parameters meet the optimal objective function. If so, output the initial control parameters as the optimal control parameters. Otherwise, return to step S11.

[0016] Furthermore, the preset variables of the distribution network include network topology and numbering, bus voltage, node load, initial solution of distributed generation output and current transmitted by the line.

[0017] Furthermore, the range constraint is expressed as:

[0018] V i min ≤V i ≤V i max

[0019] P i D,min ≤P i D ≤P i D,max

[0020]

[0021] P i G,min ≤P i G ≤P i G,max

[0022]

[0023] V i VSG,min ≤V i VSG ≤V i VSG,max

[0024]

[0025] Among them, V i represents the voltage of the i-th node, P i D represents the active power of the load at the i-th node, Represents the reactive power of the load at the i-th node, P i G represents the active power output by the generator at the i-th node, Represents the reactive power output by the i-th generator, I ij represents the current transmitted on the line from the i-th node to the j-th node; V i VSG represents the voltage of the i-th VSG node, represents the phase of the i-th VSG node; V i min 、V i max Represent the minimum and maximum voltage of the i-th node respectively; P i D,min 、P i D,max They represent the minimum and maximum active power of the load of the i-th node respectively; P i G,min 、P i G,max They represent the minimum and maximum active power output by the i-th generator respectively; They represent the minimum and maximum reactive power of the load at the i-th node respectively; They represent the minimum and maximum reactive power output by the i-th generator respectively; represents the maximum current transmitted on the line from the i-th node to the j-th node; V i VSG,min 、V i VSG ,max They represent the minimum and maximum voltages of the i-th VSG node respectively; They represent the minimum and maximum phase values ​​of the i-th VSG node respectively.

[0026] Furthermore, the grid operation constraints include the voltage and current constraint relationship, the relationship between node injection power and node voltage and current, and the balance constraint of active power and reactive power;

[0027] The constraint relationship between voltage and current in the power grid:

[0028] I ij =(V i -V j )Y ij

[0029] Among them, I ij represents the current transmitted on the line from the i-th node to the j-th node, V i Represents the voltage of the i-th node, V j represents the voltage of the jth node, Yij Expressed as the admittance of line i~j;

[0030] The relationship between node injection power and node voltage and current:

[0031]

[0032] Among them, S ij represents the power injected from the i-th generator to the j-th node;

[0033] Balance constraints of active power and reactive power:

[0034]

[0035] in, represents the power flow from the i-th node to the j-th node, i * Represents the imaginary unit, P i D represents the active power of the load at the i-th node, Represents the reactive power of the load at the i-th node, P i G represents the active power output by the generator at the i-th node, represents the reactive power output by the i-th generator.

[0036] Furthermore, the optimization objective function is:

[0037]

[0038] Where T represents the number of distributed generators in the line, I ij represents the current transmitted on the line from the i-th node to the j-th node, R ij represents the line impedance on the line from the i-th node to the j-th node, represents the maximum current transmitted on the line from the i-th node to the j-th node; C i represents the i-th thermal coefficient, P i G represents the active power output by the generator at the i-th node; represents the sum of active power and reactive power consumed by the jth VSG node, E represents the set of all lines in the power grid, G represents the set of all generator nodes in the power grid, and VSG represents the set of all virtual synchronous generator nodes in the power grid; ΔP i G Indicates the difference between the active power calculated by the power flow and the actual active power. It represents the difference between the reactive power calculated by the power flow and the actual reactive power. NA is the number of penalty function terms included in the objective function of the model. H rRepresents the penalty coefficient of active loss and reactive loss of the r-term penalty function, H r represents the penalty coefficient of the line loss of the r-term penalty function, ΔI represents the current change value; k represents the first weight coefficient, α represents the second weight coefficient, β represents the third weight coefficient, γ represents the fourth weight coefficient, λ represents the fifth weight coefficient, and δ represents the sixth weight coefficient.

[0039] Preferably, the process of correcting the optimal control parameters includes:

[0040] Calculate mechanical torque and excitation current based on optimal control parameters;

[0041] According to the mechanical torque and excitation current, the corrected control parameters are calculated using the virtual synchronous generator mathematical model.

[0042] The beneficial effects of the present invention are:

[0043] 1. In the context of a large number of distributed power sources connected to the distribution network, the present invention can ensure that the output power of each distributed power source meets the scheduling requirements and enables the distribution network to operate in the optimal power flow state.

[0044] 2. The dispatching center uses the P and Q values ​​of the virtual synchronous generators as the initial values ​​for the optimal power flow calculation. Compared with the traditional method of assigning initial values ​​for the optimal power flow calculation, it greatly reduces the number of iterations of the optimal power flow calculation, that is, saves the optimal power flow calculation time, and improves the operation efficiency of the distribution network.

[0045] 3. Based on the newly added VSG nodes in the power system, the active power and reactive power losses of this type of nodes are comprehensively considered in the objective function. In actual situations, the proportion of each small target can be considered by setting different weight coefficients.

[0046] 4. If the penalty function takes effect, the energy distribution and scheduling of the entire generator system can be made more reasonable. Moreover, due to the setting of the penalty function, the active and reactive power losses and line losses of the line can be limited to the optimal target.

[0047] 5. By introducing the minimum eigenvalue of the Jacobian matrix to judge the voltage stability, and then calculating the voltage stability margin of the system by setting the weight coefficient, the stability of the distribution network is easy to adjust, thereby improving the stability of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is a flow chart of the distribution network optimization scheduling method based on virtual synchronous generator technology and power flow analysis in the present invention.

[0049] Figure 2 This is a structural diagram of an optical fiber communication system between a dispatching center and a virtual synchronous generator in a preferred embodiment of the present invention;

[0050] Figure 3 Schematic diagram of the mathematical model of the virtual synchronous generator in the present invention. DETAILED DESCRIPTION

[0051] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0052] The present invention proposes a distribution network optimization scheduling method based on virtual synchronous generator technology and power flow analysis. Figure 1 As shown, the method includes the following contents:

[0053] S1: The distribution network dispatching center performs optimal power flow calculation based on the control parameters to obtain the optimal control parameters.

[0054] S11: Initialize preset variables of the power distribution network.

[0055] The preset variables of the distribution network include network topology and numbering, bus voltage, node load, initial solution of distributed power output, and P i G(0) and the line current I ij The load parameters of each node and the impedance parameters of each branch are normalized to per unit. The nodes include PV nodes (generator nodes), PQ nodes, and virtual synchronous generator nodes (VSG nodes). The initial value of the distribution network system uses the flat start method, setting the voltage phase angle to 0, setting the voltage amplitude of the PV nodes and virtual synchronous generator nodes to predetermined values, and setting the initial value of the voltage amplitude of the PQ node to 1.

[0056] S12: Calculate the power flow of the distribution network to obtain initial control parameters.

[0057] If a power grid system containing a virtual synchronous generator node-type DG has a total of n nodes, the first m nodes in the system are PQ nodes, nodes m+1 to m+k are k PV nodes, nodes m+k+1 to n are d virtual synchronous generator nodes, and node 1 is taken as the system voltage phase angle reference point (δ1 can be set to 0, δ1 represents the voltage phase angle of node 1).

[0058] When calculating the power flow of the distribution network, there are 2n-k unknown parameters in the polar coordinate system. According to the relevant equations, 2n-k equations are listed and the initial control parameters are obtained by solving the equations. The control parameters include: the active power of the node load, the reactive power of the node load, the active power output of the generator, and the reactive power output of the generator.

[0059] The power system node flow equation is generally expressed as:

[0060] F Pi =P i G -P i D +P i =0

[0061]

[0062] Among them, F Pi represents the sum of active power injected by the i-th node into other nodes, F Qi represents the sum of reactive power injected by the i-th node into other nodes, P i D represents the active power of the load at the i-th node, Represents the reactive power of the load at the i-th node, P i G represents the active power output by the generator at the i-th node, represents the reactive power output by the i-th generator, P i represents all active power injected into node i by other nodes, Q i represents all reactive power injected into node i by other nodes.

[0063] The node power equation of the power system is specifically expressed as:

[0064]

[0065] Among them, P is represents the active power of the generator at node i, G ij represents the real part of the node admittance matrix, δ ij represents the phase angle difference between node i and node j, B ij represents the imaginary part of the node admittance matrix, Q is represents the reactive power of the generator at node i.

[0066] The active and reactive powers injected into node i by the virtual synchronous generator droop control device are:

[0067]

[0068]

[0069] Among them, ω i0 represents the angular frequency of node i when running at no load, ω i Indicates the operating angular frequency value of node i, m pi represents the PW type droop control coefficient of node i, V i0 Represents the voltage amplitude of node i when it is running at no load, V i represents the voltage amplitude of the i-th node, n Qi represents the QV type droop control coefficient of node i, The active power injected into the virtual synchronous generator at node i, The reactive power injected into node i by the virtual synchronous generator.

[0070] The node power equation of the virtual synchronous generator can be expressed as:

[0071]

[0072] Through power flow calculation, the initial control parameters such as active power and reactive power of each node can be obtained.

[0073] S13: Determine whether the initial control parameters meet the range constraint conditions and the grid operation constraint conditions. If so, execute step S14; otherwise, return to step S11.

[0074] Range constraints are expressed as:

[0075] V i min ≤V i ≤V i max

[0076] P i D,min ≤P i D ≤P i D,max

[0077]

[0078] P i G,min ≤P i G ≤P i G,max

[0079]

[0080] V i VSG,min ≤V iVSG ≤V i VSG,max

[0081]

[0082] Among them, V i represents the voltage of the i-th node, P i D represents the active power of the load at the i-th node, Represents the reactive power of the load at the i-th node, P i G represents the active power output by the generator at the i-th node, Represents the reactive power output by the i-th generator, I ij represents the current transmitted on the line from the i-th node to the j-th node; V i VSG represents the voltage of the i-th VSG node, represents the phase of the i-th VSG node; V i min 、V i max Represent the minimum and maximum voltage of the i-th node respectively; P i D,min 、P i D,max They represent the minimum and maximum active power of the load of the i-th node respectively; P i G,min 、P i G,max They represent the minimum and maximum active power output by the i-th generator respectively; They represent the minimum and maximum reactive power of the load at the i-th node respectively; Respectively represent the minimum and maximum reactive power output by the i-th generator; I ij max represents the maximum current transmitted on the line from the i-th node to the j-th node; V i VSG,min 、V i VSG,max They represent the minimum and maximum voltages of the i-th VSG node respectively; They represent the minimum and maximum phase values ​​of the i-th VSG node respectively.

[0083] The grid operation constraints include the voltage and current constraint relationship, the relationship between node injection power and node voltage and current, and the balance constraint of active power and reactive power.

[0084] The constraint relationship between voltage and current in the power grid:

[0085] I ij =(V i -V j )Y ij

[0086] Among them, I ij represents the current transmitted on the line from the i-th node to the j-th node, V i Represents the voltage of the i-th node, V j represents the voltage of the jth node, Y ij Expressed as the admittance of lines i~j.

[0087] The relationship between node injection power and node voltage and current:

[0088]

[0089] Among them, S ij represents the power injected from the i-th generator to the j-th node, V i It is represented as the voltage at node i.

[0090] Balance constraints of active power and reactive power:

[0091]

[0092] in, represents the power flow from the i-th node to the j-th node, i * Represents an imaginary unit.

[0093] S14: Set the optimization objective function and determine whether the initial control parameters meet the optimal objective function. If so, output the initial control parameters as the optimal control parameters. Otherwise, return to step S11.

[0094] The optimization objective function is:

[0095]

[0096] Where T is the number of distributed generators in the line, k represents the first weight coefficient, α represents the second weight coefficient, β represents the third weight coefficient, γ represents the fourth weight coefficient, λ represents the fifth weight coefficient, and δ represents the sixth weight coefficient. The objective function consists of six components: the first component is line loss, the second component is generator cost, the third component is current margin, the fourth component is VSG loss, the fifth component is the penalty function when the required parameter standards are not met under the optimization objective, and the sixth component is the system voltage stability. The weight coefficients are set according to the requirements of each sub-objective in the specific optimization problem, and the objective function values ​​of the above five components are calculated according to the following method.

[0097] (1) Line loss

[0098] The first part of the equation represents the line loss, I ij represents the current transmitted on the line from the i-th node to the j-th node, R ij It represents the line impedance on the line from the i-th node to the j-th node. The total line loss is calculated by adding the weight coefficient and summing them up.

[0099] (2) Generator cost

[0100] The second part of the equation represents the cost function, G represents the set of all generator nodes in the power grid, and the thermal coefficient C 2i 、C 1i 、C 0i is a non-negative constant coefficient, P Gi Represents the cost of the generator, that is, the internal active power consumption of the generator; P i G is the active power output by the i-th generator, and the total generator cost is obtained by setting the weight coefficient summation function.

[0101] (3) Current margin

[0102] The third part of the equation represents the current margin, It represents the maximum current transmitted on the line from the i-th node to the j-th node, E represents the set of all lines in the power grid, and the total line current margin is calculated by setting the weight coefficient.

[0103] (4)VSG loss

[0104] The fourth part of the equation represents the VSG loss, where VSG represents the set of all virtual synchronous generator nodes in the power grid. represents the sum of active power and reactive power consumed by the jth VSG node, i0 represents the output current of VSG, Z V =R V +jωL V The virtual impedance introduced by VSG is calculated by setting the weight coefficient.

[0105] (5) Calculation of penalty function value

[0106] The formula in the fifth part represents the penalty function when the required parameter standards are not met under the optimization objective, ΔP i G Indicates the difference between the active power calculated by the power flow calculation and the actual active power. It represents the difference between the reactive power calculated by the power flow calculation and the actual reactive power. NA is the number of penalty function terms included in the model objective function. K rRepresents the penalty coefficient of active loss and reactive loss of the r-term penalty function, H r represents the penalty coefficient of the line loss of the r-term penalty function, ΔI represents the current change value; ΔP i G =P i Gmax (P i G )-P i G (P i Gmin ), Satisfy respectively:

[0107] P i G,min ≤P i G ≤P i G,max

[0108]

[0109] (6) Voltage stability margin

[0110] In a multi-node power system, as the normal operating point transitions toward the stability limit, the Jacobian matrix of the convergent power flow will shift toward singularity. This shift peaks when the system voltage reaches the critical point of the stability limit, and the Jacobian matrix will always have an eigenvalue that first passes through zero. This eigenvalue is called the minimum eigenvalue. Therefore, the minimum eigenvalue of the Jacobian matrix can be used as an indicator of the system's voltage stability. The larger the minimum eigenvalue of the Jacobian matrix, the more stable the voltage.

[0111] The equation in the sixth part represents the system voltage stability. J is the Jacobian matrix of the converged power flow, eig(J) represents the modulus of all eigenvalues ​​of the Jacobian matrix, and min|eig(J)| represents the modulus of the smallest eigenvalue of the Jacobian matrix. The system voltage stability margin is calculated by setting weight coefficients.

[0112] According to the requirements of each goal, the P that minimizes the objective function value is obtained. i G , is the optimal P i G , the control parameters at this time are the optimal control parameters.

[0113] S2: The virtual synchronous generator performs power dispatch according to the optimal control parameters.

[0114] S21: The dispatch center transmits the optimal control parameters to the virtual synchronous generator. Preferably, the optimal control parameters can be transmitted to the virtual synchronous generator using power line carrier communication or optical fiber communication.

[0115] For power line carrier communication, the process includes: converting the optimal control parameters into binary data; the encoder encodes the binary data using Hamming code; transmitting the data-encoded signal to the power terminal; the power terminal transmits the signal to the power line carrier communication circuit A; the power line carrier communication circuit A modulates the signal using a composite technology (OFDM) and transmits it to the power line carrier circuit B through its power line interface; the power line carrier circuit B demodulates the signal using a composite technology (OFDM) and transmits it to the power terminal through its power line interface; the power terminal transmits the signal to the decoder; the decoder decodes the signal into binary data; the virtual synchronizer converts the binary data into the required optimal control parameters.

[0116] For optical fiber communications, such as Figure 2 As shown, the optical fiber communication system includes a line compiler (for data encoding), an electrical terminal (for sending and receiving radio waves), an optical transmitter (for optical / electrical conversion), a repeater (for signal compensation), passive components (including optical fiber connectors, couplers, etc.), a line decoder (for signal decoding), an optical receiver (for optical / electrical conversion), and optical fiber (the communication medium between devices); its transmission process includes: the dispatching center sends an address code for address code verification, and the address code of each virtual synchronous generator is stored in its internal communication control chip. After the address code is verified to be correct, the communication command is executed; after confirming the communication command, the dispatching center converts the P and Q information (control parameters) obtained from the power flow calculation into binary data, transmits the data to the line encoder, and Manchester encodes the data; after the data is encoded, it is transmitted to the optical transmitter through the electrical terminal. The optical transmitter directly modulates the light wave emitted by the light source with the electrical signal to produce a modulated light wave, and transmits the modulated light signal to the optical fiber or optical cable for transmission; after receiving the light signal, the optical receiver converts the light signal into an electrical signal and amplifies it to a sufficient level through an amplifier, and transmits it to the electrical terminal; after receiving the electrical signal, the electrical terminal transmits it to the line decoder for decoding and transmits the decoded data to the virtual synchronous generator.

[0117] S22: The virtual synchronous generator generates a control instruction according to the optimal control parameters.

[0118] S23: The virtual synchronous generator performs power dispatching according to the control instructions.

[0119] S3: Correct the optimal control parameters to obtain corrected control parameters.

[0120] S31: Calculate mechanical torque and excitation current according to optimal control parameters;

[0121] Since the frequency change in the power system is relatively small, the controller can use the corresponding formula to directly convert the active power P and reactive power Q into the corresponding mechanical torque T mand excitation current M f i f Specifically, according to the angular frequency of the current virtual synchronous generator, the active power P is converted into mechanical torque; through an integral link, the parameters are adjusted to convert the reactive power Q into the excitation current;

[0122] S32: According to the mechanical torque and the excitation current, the corrected control parameters are calculated using the virtual synchronous generator mathematical model.

[0123] According to the mechanical torque and excitation current, the reverse electromotive force e is calculated through the mathematical model of the virtual synchronous generator; the mathematical model of the synchronous generator is as follows Figure 3 As shown. Specifically, the back electromotive force e is generated by the pulse width modulation PWM unit to generate a PWM pulse signal to drive the power semiconductor device so that the average value of the output voltage in one switching cycle is the same as e. At the same time, in the frequency loop, the angular frequency of the synchronous inverter can be controlled and the phase angle θ of the back electromotive force e can be generated. In addition, the main circuit inductor current can be regarded as the stator current of the virtual synchronous machine. Therefore, according to the calculation formula of active power: P = UIcosθ and the calculation formula of reactive power: Q = UIsinθ, the corrected active power and reactive power can be calculated, and the voltage frequency, active power and reactive power of the virtual synchronous generator can be controlled through the active power instruction Pset and the reactive power instruction Qset.

[0124] S4: The virtual synchronous generator feeds back the corrected control parameters to the distribution network dispatching center, and returns to step S1.

[0125] The process of the virtual synchronous generator feeding back the corrected control parameters to the distribution network dispatching center is the reverse transmission of the aforementioned dispatching center transmitting the optimal control parameters to the virtual synchronous generator. The process is similar and will not be repeated here.

[0126] The above embodiments further illustrate the purpose, technical solutions and advantages of the present invention in detail. It should be understood that the above embodiments are only preferred implementation plans of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made to the present invention within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A distribution network optimization scheduling method based on virtual synchronous generator technology and power flow analysis, characterized in that: include: S1: The distribution network dispatching center performs optimal power flow calculation based on the control parameters to obtain the optimal control parameters; The process of obtaining the optimal control parameters includes: S11: Initialize preset variables of the power distribution network; S12: Calculate the power flow of the distribution network to obtain initial control parameters; S13: Determine whether the initial control parameters meet the range constraint conditions and the grid operation constraint conditions. If so, execute step S14; otherwise, return to step S11; S14: Set the optimization objective function and determine whether the initial control parameters meet the optimal objective function. If so, output the initial control parameters as the optimal control parameters. Otherwise, return to step S11. The optimization objective function is: Where T represents the number of distributed generators in the line, I ij represents the current transmitted on the line from the i-th node to the j-th node, R ij represents the line impedance on the line from the i-th node to the j-th node, represents the maximum current transmitted on the line from the i-th node to the j-th node; C i represents the i-th thermal coefficient, represents the active power output by the generator at the i-th node; represents the sum of active power and reactive power consumed by the jth VSG node, E represents the set of all lines in the power grid, G represents the set of all generator nodes in the power grid, and VSG represents the set of all virtual synchronous generator nodes in the power grid; Indicates the difference between the active power calculated by the power flow and the actual active power. It represents the difference between the reactive power calculated by the power flow and the actual reactive power. NA is the number of penalty function terms included in the objective function of the model. K r Represents the penalty coefficient of active loss and reactive loss of the r-term penalty function, H r represents the penalty coefficient of the line loss of the r-term penalty function, ΔI represents the current change value; k represents the first weight coefficient, α represents the second weight coefficient, β represents the third weight coefficient, γ represents the fourth weight coefficient, λ represents the fifth weight coefficient, and δ represents the sixth weight coefficient; eig(J) represents the modulus of all eigenvalues ​​of the Jacobian matrix, and min|eig(J)| represents the modulus of the smallest eigenvalue of the Jacobian matrix; S2: Virtual synchronous generators perform power dispatch according to optimal control parameters; S3: Correct the optimal control parameters to obtain corrected control parameters; S4: The virtual synchronous generator feeds back the corrected control parameters to the distribution network dispatching center, and returns to step S1.

2. A distribution network optimization scheduling method based on virtual synchronous generator technology and power flow analysis according to claim 1, characterized in that: The control parameters include the active power of the node's load, the reactive power of the node's load, the active power output by the generator, and the reactive power output by the generator.

3. The distribution network optimization scheduling method based on virtual synchronous generator technology and power flow analysis according to claim 1 is characterized in that: The preset variables of the distribution network include network topology and numbering, bus voltage, node load, initial solution of distributed generation output and current transmitted by the line.

4. The distribution network optimization scheduling method based on virtual synchronous generator technology and power flow analysis according to claim 1 is characterized in that: Range constraints are expressed as: Among them, V i represents the voltage of the i-th node, represents the active power of the load at the i-th node, represents the reactive power of the load at the i-th node, represents the active power output by the generator at the i-th node, Represents the reactive power output by the i-th generator, I ij represents the current transmitted on the line from the i-th node to the j-th node; represents the voltage of the i-th VSG node, represents the phase of the i-th VSG node; Represent the minimum and maximum voltage of the i-th node respectively; They represent the minimum and maximum active power of the load of the i-th node respectively; They represent the minimum and maximum active power output by the i-th generator respectively; They represent the minimum and maximum reactive power of the load at the i-th node respectively; They represent the minimum and maximum reactive power output by the i-th generator respectively; It represents the maximum value of the current transmitted on the line from the i-th node to the j-th node; They represent the minimum and maximum voltages of the i-th VSG node respectively; They represent the minimum and maximum phase values ​​of the i-th VSG node respectively.

5. The method for optimizing the distribution network based on virtual synchronous generator technology and power flow analysis according to claim 1, characterized in that: The grid operation constraints include the relationship between voltage and current, the relationship between node injection power and node voltage and current, and the balance constraint between active power and reactive power; The constraint relationship between voltage and current in the power grid: I ij =(V i -V j )Y ij Among them, I ij Represents the current transmitted on the line from the i-th node to the j-th node, V i Represents the voltage of the i-th node, V j represents the voltage of the jth node, Y ij Expressed as the admittance of line i~j; The relationship between node injection power and node voltage and current: Among them, S ij represents the power injected from the i-th generator to the j-th node; Balance constraints of active power and reactive power: in, represents the power flow from the i-th node to the j-th node, i * represents the imaginary unit, represents the active power of the load at the i-th node, represents the reactive power of the load at the i-th node, represents the active power output by the generator at the i-th node, represents the reactive power output by the i-th generator.

6. The method for optimizing the distribution network based on virtual synchronous generator technology and power flow analysis according to claim 1, characterized in that: The process of correcting the optimal control parameters includes: Calculate mechanical torque and excitation current based on optimal control parameters; According to the mechanical torque and excitation current, the corrected control parameters are calculated using the virtual synchronous generator mathematical model.

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