Network-related parameter optimization method and system of distributed photovoltaic system

Abstract

The invention discloses a grid-related parameter optimization method and system for a distributed photovoltaic system. The method comprises the following steps: firstly, establishing a distributed photovoltaic power generation system small-signal mathematical model of a photovoltaic cell, a grid-related inverter, a transformer and a power grid; secondly, analyzing the characteristic values to obtain a system parameter boundary value under the condition that each characteristic value is stable; establishing an optimization objective function of the small interference stability, the damping ratio and the stability margin; and finally, optimizing the target function by using an improved index triangular optimization algorithm, thereby determining the optimal network-related parameters of the distributed photovoltaic system. And the network-related stability of the distributed photovoltaic system can be obviously improved, so that the distributed photovoltaic network-related system can operate stably.

Description

Technical Field

[0001] The present invention belongs to the technical field of grid-related parameter optimization, and in particular to a method and system for optimizing grid-related parameters of a distributed photovoltaic system. Background Art

[0002] At present, distributed photovoltaics have surpassed centralized ones. However, with the rapid increase in the number of distributed photovoltaics, it is difficult to effectively improve the transient voltage stability level when distributed photovoltaics are connected to the grid. As the key to the connection of distributed photovoltaic power sources to the grid, grid-related parameters play an important role in distributed photovoltaic power generation systems and need to have the ability to adapt to different operating conditions and system dynamic characteristics to achieve better performance and power quality.

[0003] Most of the research work at home and abroad is mainly aimed at modeling distributed photovoltaic power generation grid-related systems and studying the control methods of interface inverters, and there is relatively little research on stability analysis from the control perspective. Moreover, these studies do not consider the coupling relationship between the parameters of each controller in detail, but only analyze the impact of parameter changes on system stability, and fail to make the system parameters reach a relatively global optimum. Summary of the invention

[0004] The present invention provides a distributed photovoltaic system network-related parameter optimization method and system, which are used to solve the technical problem that the system parameters cannot reach a relatively global optimum.

[0005] In a first aspect, the present invention provides a method for optimizing grid-related parameters of a distributed photovoltaic system, comprising:

[0006] Construct a small signal mathematical model of distributed photovoltaic power generation system;

[0007] According to the small signal mathematical model, an eigenvalue analysis method is used to obtain the boundary values ​​of system parameters when each eigenvalue is stable;

[0008] Establishing a coordinated optimization objective function of small disturbance stability, damping ratio and stability margin according to the boundary values ​​of the system parameters;

[0009] The improved exponential triangular optimization algorithm is used to optimize the optimization objective function to obtain the optimal grid-related parameters of the distributed photovoltaic system.

[0010] In a second aspect, the present invention provides a distributed photovoltaic system network-related parameter optimization system, comprising:

[0011] A building module configured to build a small signal mathematical model of a distributed photovoltaic power generation system;

[0012] A determination module is configured to obtain the boundary values ​​of system parameters when each eigenvalue is stable by using an eigenvalue analysis method according to the small signal mathematical model;

[0013] An establishment module is configured to establish a coordinated optimization objective function of small disturbance stability, damping ratio and stability margin according to the boundary values ​​of the system parameters;

[0014] The optimization module is configured to optimize the optimization objective function using an improved exponential triangular optimization algorithm to obtain the optimal grid-related parameters of the distributed photovoltaic system.

[0015] According to a third aspect, an electronic device is provided, comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the steps of the method for optimizing grid-related parameters of a distributed photovoltaic system according to any embodiment of the present invention.

[0016] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor executes the steps of the method for optimizing grid-related parameters of a distributed photovoltaic system according to any embodiment of the present invention.

[0017] The distributed photovoltaic system grid-related parameter optimization method and system of the present application use an improved exponential triangle optimization algorithm to optimize the distributed photovoltaic grid-related parameters, and the obtained grid-related parameters effectively improve the voltage stability level. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0019] Figure 1 A flow chart of a method for optimizing grid-related parameters of a distributed photovoltaic system provided by an embodiment of the present invention;

[0020] Figure 2 A flowchart of an improved exponential triangular optimization algorithm provided by an embodiment of the present invention;

[0021] Figure 3 A comparison diagram of an improved exponential triangulation optimization algorithm provided by an embodiment of the present invention;

[0022] Figure 4 A structural block diagram of a distributed photovoltaic system grid-related parameter optimization system provided by an embodiment of the present invention;

[0023] Figure 5It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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 creative work are within the scope of protection of the present invention.

[0025] See also Figure 1 , which shows a flow chart of a method for optimizing grid-related parameters of a distributed photovoltaic system of the present application.

[0026] like Figure 1 As shown, the method for optimizing the grid-related parameters of a distributed photovoltaic system specifically includes the following steps:

[0027] Step S101, constructing a small signal mathematical model of a distributed photovoltaic power generation system.

[0028] In this step, the expression of the small signal mathematical model is:

[0029]

[0030] TT=t air +kS,

[0031]

[0032] U oc =U ocref [1-m(tt ref )](ρS+σ),

[0033]

[0034] U m =U mref [1-m(tt ref )](ρS+σ),

[0035]

[0036]

[0037] Where, I is the output current of the photovoltaic cell, I sc is the short-circuit current of the photovoltaic cell, C1 and C2 are constant coefficients, U is the output voltage of the photovoltaic cell, U oc is the open circuit voltage of the photovoltaic cell, TT is the cell temperature, tair is the ambient temperature, k is the temperature coefficient, S is the light intensity, I scref is the short-circuit reference current of the photovoltaic cell, l, m, n are compensation coefficients, t is the temperature of the photovoltaic cell under non-standard conditions, t ref is the reference temperature, S ref is the short-circuit reference light intensity of the photovoltaic cell, U ocref is the short-circuit reference voltage of the photovoltaic cell, ρ and σ are linear fitting parameters, I m is the current when the photovoltaic cell operates at the maximum power point, I mref is the reference current when the photovoltaic cell works at the maximum power point, U m is the voltage of the photovoltaic cell when it is operating at the maximum power point, U mref is the reference voltage when the photovoltaic cell works at the maximum power point, and e is the natural base;

[0038]

[0039] Where U DC is the DC voltage, C DC is the DC bus energy storage capacitor, u dg is the d-axis voltage of the photovoltaic grid, i dg is the d-axis current of the photovoltaic grid, XX is the equivalent reactance of the transformer and the transmission line, δ g is the voltage angle, R is the equivalent resistance of the transformer and the transmission line, i qg is the q-axis current of the photovoltaic grid, and K is the transformer ratio.

[0040] Step S102: According to the small signal mathematical model, an eigenvalue analysis method is used to obtain the boundary values ​​of system parameters when each eigenvalue is stable.

[0041] In this step, according to the small signal mathematical model, the eigenvalue analysis method is used to draw the eigenvalue root locus diagram corresponding to different system parameters for analysis, and the boundary values ​​of the system parameters under the condition of stability of each eigenvalue are obtained.

[0042] Step S103: establishing a coordinated optimization objective function of small disturbance stability, damping ratio and stability margin according to the system parameter boundary value.

[0043] In this step, the comprehensive optimization objective function containing the volatility, variability and multiple operation scenarios of the microgrid operation scenario is processed to obtain a coordinated optimization objective function, and the expression of the coordinated optimization objective function is:

[0044] min J=min(J1+J2+J3),

[0045] Where J is the coordinated optimization objective function, J1 is the objective function composed of the eigenvalues ​​with positive real parts in the distributed photovoltaic system, J2 is the objective function composed of the eigenvalues ​​with eigenvalue damping ratio less than the given damping ratio ξ0 in the distributed photovoltaic system, and J3 is the objective function composed of the eigenvalues ​​with real parts less than 0 and greater than the given real parts α0 in the eigenvalues ​​of the distributed photovoltaic system.

[0046] The expression for calculating the objective function of the eigenvalues ​​with positive real parts in the distributed photovoltaic system is:

[0047]

[0048] μ i =Lα i 2 ,

[0049] In the formula, α i is the real part of the eigenvalue whose real part is greater than 0 in the distributed photovoltaic system, μ i is the weight coefficient of the eigenvalue with the real part greater than 0 in the distributed photovoltaic system, and L is an empirical parameter greater than 0;

[0050] The expression of the objective function for calculating the eigenvalue damping ratio of the distributed photovoltaic system less than the given damping ratio ζ0 is:

[0051]

[0052] In the formula, ζ j is the damping ratio of the jth eigenvalue less than ξ0, μ j is the weight coefficient of the damping ratio of the jth eigenvalue less than ξ0, β j is the imaginary part of the jth eigenvalue;

[0053] The expression of the objective function of calculating the eigenvalues ​​of the distributed photovoltaic system whose real part is less than 0 and greater than the given real part α0 is:

[0054]

[0055] μ k =L(α k -α0) 2 ,

[0056] In the formula, μ k is the weight coefficient of the real part of the kth eigenvalue whose real part is less than 0 and greater than α0, α k is the real part of the kth eigenvalue whose real part is less than 0 and greater than α0.

[0057] Step S104, using the improved exponential triangular optimization algorithm to optimize the optimization objective function, to obtain the optimal grid-related parameters of the distributed photovoltaic system.

[0058] In this step, refer to Figure 2 The execution steps of the improved exponential triangular optimization algorithm include: first, setting the initial population and the maximum number of iterations, then using the constrained exploration method and determining the upper and lower bounds, then calculating the fitness, then updating the fitness according to the exploration mode and development model, and finally determining whether the maximum number of iterations has been reached. Specifically:

[0059] The iteration count is updated using a constrained search strategy, and the upper and lower limits of the updated search space are determined after the iteration count is updated, wherein the expression of the constrained search strategy is:

[0060] CE i+1 =floor[2-2×t×(MaxIter-CE i ×a)]+CE i ,

[0061]

[0062] In the formula, CE i+1 is the updated iteration count, CE i is the iteration count before updating, floor is the function that performs rounding operations in MATLAB, t is the current iteration number, MaxIter is the total iteration number, and a and b are adjustment coefficients;

[0063] The expressions for determining the upper and lower bounds of the update search space are:

[0064]

[0065] In the formula, Up i 、Low i are the upper and lower limits of the expected search space, r1 and r2 are random numbers between 0 and 1. is the jth position of the optimal solution obtained so far, is the position of the suboptimal solution at the jth index;

[0066] During the optimization process, an exploration mode and an exploitation mode are performed, and the exploration phase and the exploitation mode are converted by a conversion mechanism, wherein the exploration mode includes a first exploration phase and a second exploration phase, the exploitation mode includes a first exploitation phase and a second exploitation phase, and the transition between the first exploration phase and the second exploration phase is performed by The expression of control,conversion mechanism is:

[0067]

[0068] Where CM is the conversion factor, rand is a random value between 0 and 1, sqrt is the square root operator, and d1 and d2 are constants that change with the number of iterations. When CM is greater than 1, it is in exploration mode. On the contrary, when CM is not greater than 1, it switches to development mode.

[0069] Determine whether the current iteration has reached the maximum number of times. If not, continue the iteration; otherwise, stop the iteration and output the optimal individual, which is the optimal grid-related parameter of the distributed photovoltaic system.

[0070] It should be noted that, in view of the shortcomings of the exponential triangle optimization algorithm in the early iteration, the first exploration stage is improved, and the improved expression is:

[0071]

[0072] In the formula, is the jth position of the ith solution in the t+1th iteration, is the jth position of the ith solution in the tth iteration, q1 is a random number between 0 and 1, α1 is the first weight coefficient of the candidate solution, exp is a natural base, δ, d1 and d2 are constants that change with the number of iterations, t represents the current number of iterations, MaxIter represents the maximum number of iterations, represents the worst individual position at the tth iteration, represents the optimal individual position at the tth iteration;

[0073] In view of the insufficient search accuracy of the exponential triangular optimization algorithm itself, the elite error formula was introduced in the second development stage to improve it.

[0074]

[0075] In the formula, is the global worst individual, c is a constant coefficient, is the position of the suboptimal solution at the jth index, and α2 is the second weight coefficient of the candidate solution.

[0076] In view of the shortcoming that the exponential triangular optimization algorithm is prone to fall into the local optimal value in the later stage of iteration, the perturbation formula is introduced to make it jump out of the local optimal value. Its expression is:

[0077]

[0078] In the formula, fit i represents the fitness value of the i-th individual, fit pos Represents the fitness value of any individual in the population, fit min and fit maxare the worst fitness value and the best fitness respectively, R is the exploration range, represents the position of a random individual after t+1 iterations in the jth dimension, and represents the position of the i-th individual in the j-th dimension after t and t+1 iterations, r s is a random number between [0, 1]. Dis is the Euclidean distance between the i-th individual and the reference, s is a random unit vector, X best Indicates the best position currently searched, X mean Represents the average position in the current population, Levy represents Levy distribution, rand represents a random value between 0 and 1, t represents the current number of iterations, and MaxIter represents the maximum number of iterations.

[0079] In this embodiment, by Figure 3 By comparison, it is found that the fitness obtained by updating the improved exponential triangle optimization algorithm in this application is better.

[0080] In summary, the method of the present application first establishes a small signal mathematical model of a distributed photovoltaic power generation system of photovoltaic cells, grid-related inverters, transformers and power grids; secondly, the eigenvalue analysis method is used to obtain the boundary values ​​of system parameters under the condition of stability of each eigenvalue; then, the optimization objective function of small disturbance stability, damping ratio and stability margin is established; finally, the improved exponential triangular optimization algorithm is used to optimize the objective function, so as to determine the optimal grid-related parameters of the distributed photovoltaic system, which can significantly improve the grid-related stability of the distributed photovoltaic system, thereby making the distributed photovoltaic grid-related system operate stably.

[0081] See also Figure 4 , which shows a structural block diagram of a grid-related parameter optimization system of a distributed photovoltaic system of the present application.

[0082] like Figure 4 As shown, the network-related parameter optimization system 200 includes a construction module 210 , a determination module 220 , an establishment module 230 and an optimization module 240 .

[0083] Among them, the construction module 210 is configured to construct a small signal mathematical model of a distributed photovoltaic power generation system; the determination module 220 is configured to obtain the boundary values ​​of system parameters under the condition of stability of each eigenvalue using the eigenvalue analysis method based on the small signal mathematical model; the establishment module 230 is configured to establish a coordinated optimization objective function of small disturbance stability, damping ratio and stability margin based on the system parameter boundary values; the optimization module 240 is configured to use the improved exponential triangular optimization algorithm to optimize the optimization objective function to obtain the optimal grid-related parameters of the distributed photovoltaic system.

[0084] It should be understood that Figure 4 Modules and references documented in Figure 1 Therefore, the operations and features described above for the method and the corresponding technical effects are also applicable to Figure 4 The modules in it will not be described in detail here.

[0085] In some other embodiments, the embodiments of the present invention further provide a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor executes the method for optimizing grid-related parameters of a distributed photovoltaic system in any of the above method embodiments;

[0086] As an implementation mode, the computer-readable storage medium of the present invention stores computer-executable instructions, and the computer-executable instructions are configured as follows:

[0087] Construct a small signal mathematical model of distributed photovoltaic power generation system;

[0088] According to the small signal mathematical model, an eigenvalue analysis method is used to obtain the boundary values ​​of system parameters when each eigenvalue is stable;

[0089] Establishing a coordinated optimization objective function of small disturbance stability, damping ratio and stability margin according to the boundary values ​​of the system parameters;

[0090] The improved exponential triangular optimization algorithm is used to optimize the optimization objective function to obtain the optimal grid-related parameters of the distributed photovoltaic system.

[0091] The computer-readable storage medium may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created according to the use of the distributed photovoltaic system's network-related parameter optimization system, etc. In addition, the computer-readable storage medium may include a high-speed random access memory, and may also include a memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer-readable storage medium may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the distributed photovoltaic system's network-related parameter optimization system via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0092] Figure 5 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention, such as Figure 5As shown, the device includes: a processor 310 and a memory 320. The electronic device may also include: an input device 330 and an output device 340. The processor 310, the memory 320, the input device 330 and the output device 340 may be connected via a bus or other means. Figure 5 In the example, the connection through the bus is taken as an example. The memory 320 is the above-mentioned computer-readable storage medium. The processor 310 executes various functional applications and data processing of the server by running the non-volatile software programs, instructions and modules stored in the memory 320, that is, the grid-related parameter optimization method of the distributed photovoltaic system of the above-mentioned method embodiment is realized. The input device 330 can receive input digital or character information, and generate key signal input related to user settings and function control of the grid-related parameter optimization system of the distributed photovoltaic system. The output device 340 may include display devices such as display screens.

[0093] The electronic device can execute the method provided by the embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to the method provided by the embodiment of the present invention.

[0094] As an implementation mode, the electronic device is applied to a distributed photovoltaic system network-related parameter optimization system, and is used for a client, and includes: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can:

[0095] Construct a small signal mathematical model of distributed photovoltaic power generation system;

[0096] According to the small signal mathematical model, an eigenvalue analysis method is used to obtain the boundary values ​​of system parameters when each eigenvalue is stable;

[0097] Establishing a coordinated optimization objective function of small disturbance stability, damping ratio and stability margin according to the boundary values ​​of the system parameters;

[0098] The improved exponential triangular optimization algorithm is used to optimize the optimization objective function to obtain the optimal grid-related parameters of the distributed photovoltaic system.

[0099] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of each embodiment or some parts of the embodiment.

[0100] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing grid-related parameters of a distributed photovoltaic system, characterized in that: include: Construct a small signal mathematical model of distributed photovoltaic power generation system; According to the small signal mathematical model, an eigenvalue analysis method is used to obtain the boundary values ​​of system parameters when each eigenvalue is stable; Establishing a coordinated optimization objective function of small disturbance stability, damping ratio and stability margin according to the boundary values ​​of the system parameters; The improved exponential triangular optimization algorithm is used to optimize the optimization objective function to obtain the optimal grid-related parameters of the distributed photovoltaic system.

2. A distributed photovoltaic system grid-related parameter optimization method according to claim 1, characterized in that: The coordinated optimization objective function of establishing small disturbance stability, damping ratio and stability margin according to the system parameter boundary value includes: The comprehensive optimization objective function containing the volatility, variability and multiple operation scenarios of the microgrid operation scenario is processed to obtain the coordinated optimization objective function, and the expression of the coordinated optimization objective function is: min J=min(J1+J2+J3), Where J is the coordinated optimization objective function, J1 is the objective function composed of the eigenvalues ​​with positive real parts in the distributed photovoltaic system, J2 is the objective function composed of the eigenvalues ​​with eigenvalue damping ratio less than the given damping ratio ξ0 in the distributed photovoltaic system, and J3 is the objective function composed of the eigenvalues ​​with real parts less than 0 and greater than the given real parts α0 in the eigenvalues ​​of the distributed photovoltaic system.

3. A distributed photovoltaic system grid-related parameter optimization method according to claim 2, characterized in that: in, The expression for calculating the objective function of the eigenvalues ​​with positive real parts in the distributed photovoltaic system is: m i =La i 2 , In the formula, α i is the real part of the eigenvalue whose real part is greater than 0 in the distributed photovoltaic system, μ i is the weight coefficient of the eigenvalue with the real part greater than 0 in the distributed photovoltaic system, and L is an empirical parameter greater than 0; The expression of the objective function for calculating the eigenvalues ​​whose eigenvalue damping ratio in the distributed photovoltaic system is less than the given damping ratio ξ0 is: In the formula, ξ j is the damping ratio of the jth eigenvalue less than ξ0, μ′ j is the weight coefficient of the damping ratio of the jth eigenvalue less than ξ0, β j is the imaginary part of the jth eigenvalue; The expression of the objective function of calculating the eigenvalues ​​of the distributed photovoltaic system whose real part is less than 0 and greater than the given real part α0 is: m k =L(a k -a0) 2 , In the formula, μ k is the weight coefficient of the real part of the kth eigenvalue whose real part is less than 0 and greater than α0, α k is the real part of the kth eigenvalue whose real part is less than 0 and greater than α0.

4. A distributed photovoltaic system grid-related parameter optimization method according to claim 1, characterized in that: The improved exponential triangular optimization algorithm is used to optimize the optimization objective function to obtain the optimal grid-related parameters of the distributed photovoltaic system, including: In view of the shortcomings of the exponential triangle optimization algorithm in the early iteration, the first exploration stage is improved, and the improved expression is: In the formula, is the jth position of the ith solution in the t+1th iteration, is the jth position of the ith solution in the tth iteration, q1 is a random number between 0 and 1, α i is the first weight coefficient of the candidate solution, exp is a natural base, δ, d1 and d2 are constants that change with the number of iterations, t represents the current number of iterations, and MaxIter represents the maximum number of iterations. represents the worst individual position at the tth iteration, represents the optimal individual position at the tth iteration; In view of the insufficient search accuracy of the exponential triangular optimization algorithm itself, the elite error formula was introduced in the second development stage to improve it. In the formula, is the global worst individual, c is a constant coefficient, is the position of the suboptimal solution at the jth index, and α2 is the second weight coefficient of the candidate solution. In view of the shortcoming that the exponential triangular optimization algorithm is prone to fall into the local optimal value in the later stage of iteration, the perturbation formula is introduced to make it jump out of the local optimal value. Its expression is: In the formula, fit i represents the fitness value of the i-th individual, fit pos Represents the fitness value of any individual in the population, fit min and fit max are the worst fitness value and the best fitness respectively, R is the exploration range, represents the position of a random individual after t+1 iterations in the jth dimension, and represents the position of the i-th individual in the j-th dimension after t and t+1 iterations, r s is a random number between [0, 1]. Dis is the Euclidean distance between the i-th individual and the reference, s is a random unit vector, X best Indicates the best position currently searched, X mean Represents the average position in the current population, Levy represents Levy distribution, rand represents a random value between 0 and 1, t represents the current number of iterations, and MaxIter represents the maximum number of iterations.

5. A distributed photovoltaic system grid-related parameter optimization system, characterized in that: include: A building module configured to build a small signal mathematical model of a distributed photovoltaic power generation system; A determination module is configured to obtain the boundary values ​​of system parameters when each eigenvalue is stable by using an eigenvalue analysis method according to the small signal mathematical model; An establishment module is configured to establish a coordinated optimization objective function of small disturbance stability, damping ratio and stability margin according to the boundary values ​​of the system parameters; The optimization module is configured to optimize the optimization objective function using an improved exponential triangular optimization algorithm to obtain the optimal grid-related parameters of the distributed photovoltaic system.

6. An electronic device, characterized in that: include: At least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method described in any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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