New energy control parameter optimization method and system for short-circuit current control

Abstract

The invention discloses a new energy control parameter optimization method and system for short-circuit current control, and belongs to the technical field of power systems. The method comprises the following steps: establishing a short-circuit current calculation data model for a to-be-analyzed regional power grid containing a new energy station model; determining an objective function and a constraint condition of the short-circuit current calculation data model; solving the short-circuit current calculation data model by using a particle swarm algorithm to obtain an optimal solution; and adjusting control parameters of the regional power grid new energy based on the optimal solution so as to optimize the control parameters. The method can provide support for new energy power grid short-circuit current control and new energy station control strategy optimization, can provide a new technical means for new energy power grid short-circuit current control, and guarantees safe and stable operation of a high-proportion new energy power grid.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and more specifically, to a method and system for optimizing new energy control parameters for short-circuit current control. Background Art

[0002] In the operation of power systems, short-circuit faults are one of the most common faults, and the short-circuit current generated by them will have serious impacts and consequences on the safe and stable operation of power systems. At present, my country's receiving power grids generally face the risk of excessive short-circuit currents. When the overall short-circuit current level of the power grid is low, the main focus of new equipment connection is its impact on the stability characteristics of the power grid, and its effect on the short-circuit current only needs a simple evaluation.

[0003] After being connected to the grid, new energy power generation equipment such as double-fed, direct-drive wind turbines, and photovoltaic power generation equipment will present voltage-controlled current source characteristics to the power grid, which will increase the short-circuit current level of the new energy power grid to a certain extent. As the short-circuit current level of the power grid gradually approaches or reaches the rated interrupting capacity of the switch, the impact of new equipment, especially new energy power generation equipment, on the short-circuit current should be focused on after it is connected to the power grid. From the perspective of the fault ride-through characteristics and short-circuit current contribution characteristics of new energy equipment, the contribution of new energy to the short-circuit current of the AC power grid is closely related to its control strategy and fault electrical distance. In the context of the severe risk of excessive short-circuit current in new energy power grids, the flexible control capabilities of new energy equipment can be utilized to significantly reduce its short-circuit current contribution level by optimizing its control parameters related to short-circuit current contribution.

[0004] By judging the sensitivity index of different renewable energy stations to short-circuit current and adjusting the short-circuit current control parameters of renewable energy stations near the fault area, new technical means can be provided for short-circuit current control of renewable energy power grids, thus ensuring the safe and stable operation of power grids with a high proportion of renewable energy. Summary of the invention

[0005] In view of the above problems, the present invention proposes a method for optimizing new energy control parameters for short-circuit current control, comprising:

[0006] For the regional power grid to be analyzed that contains the new energy station model, a short-circuit current calculation data model is established;

[0007] Determining the objective function and constraint conditions of the short-circuit current calculation data model;

[0008] Using a particle swarm algorithm to solve the short-circuit current calculation data model to obtain an optimal solution;

[0009] Based on the optimal solution, control parameters of the renewable energy in the regional power grid are adjusted to optimize the control parameters.

[0010] Optionally, the objective function is formulated as follows:

[0011] max F=S PV1 +S PV2

[0012] Among them, F is the total capacity of new energy grid-connected, S PV1 and S PV2 The capacity for connecting new energy sources to the power grid.

[0013] Optionally, before determining the objective function and constraint conditions of the short-circuit current calculation data model, the method further includes:

[0014] Determine the key nodes of the regional power grid to be analyzed that includes the new energy station model.

[0015] Optionally, the key nodes include: a node near the AC source grid connection point and a node near the new energy power station outlet grid connection point.

[0016] Optional constraints include: the short-circuit current at key nodes does not exceed the standard and the new energy does not disconnect from the grid during low-voltage operation.

[0017] Optionally, the key node short-circuit current does not exceed the constraint conditions, as follows:

[0018]

[0019] Among them, I k1 is the short-circuit current of key node 1 in the regional power grid to be analyzed, I ki is the short-circuit current of the key node i in the regional power grid to be analyzed, I kn is the short-circuit current of the key node n in the regional power grid to be analyzed, S is the new energy access capacity of the regional power grid to be analyzed, k Lvrt is the reactive power support coefficient of each renewable energy station in the regional power grid to be analyzed, max Rated current for the station switch.

[0020] Optional constraints for new energy to be low-powered but not disconnected from the grid include:

[0021]

[0022] Among them, U kl is the post-fault voltage of the key node 1 in the regional power grid to be analyzed, U ki is the post-fault voltage of the key node i in the regional power grid to be analyzed, U kn is the post-fault voltage of the key node n in the regional power grid to be analyzed.

[0023] On the other hand, the present invention also proposes a system for optimizing new energy control parameters for short-circuit current control, comprising:

[0024] Establish a model unit for establishing a short-circuit current calculation data model for the regional power grid to be analyzed that includes a new energy station model;

[0025] A calculation unit, used to determine the objective function and constraint conditions of the short-circuit current calculation data model;

[0026] A solving unit, used for solving the short-circuit current calculation data model using a particle swarm algorithm to obtain an optimal solution;

[0027] An optimization unit is used to adjust the control parameters of the renewable energy in the regional power grid based on the optimal solution to optimize the control parameters.

[0028] Optionally, the objective function is formulated as follows:

[0029] max F=S PV1 +S PV2

[0030] Among them, F is the total capacity of new energy grid-connected, Sx V1 and S PV2 The capacity for connecting new energy sources to the power grid.

[0031] Optionally, before determining the objective function and constraint conditions of the short-circuit current calculation data model, the method further includes:

[0032] Determine the key nodes of the regional power grid to be analyzed that includes the new energy station model.

[0033] Optionally, the key nodes include: a node near the AC source grid connection point and a node near the new energy power station outlet grid connection point.

[0034] Optional constraints include: the short-circuit current at key nodes does not exceed the standard and the new energy does not disconnect from the grid during low-voltage operation.

[0035] Optionally, the key node short-circuit current does not exceed the constraint conditions, as follows:

[0036]

[0037] Among them, I k1 is the short-circuit current of key node 1 in the regional power grid to be analyzed, I ki is the short-circuit current of the key node i in the regional power grid to be analyzed, I kn is the short-circuit current of the key node n in the regional power grid to be analyzed, S is the new energy access capacity of the regional power grid to be analyzed, k Lvrt is the reactive power support coefficient of each renewable energy station in the regional power grid to be analyzed, max Rated current for the station switch.

[0038] Optional constraints for new energy to be low-powered but not disconnected from the grid include:

[0039]

[0040] Among them, U kl is the post-fault voltage of the key node 1 in the regional power grid to be analyzed, U ki is the post-fault voltage of the key node i in the regional power grid to be analyzed, U kn is the post-fault voltage of the key node n in the regional power grid to be analyzed.

[0041] In yet another aspect, the present invention further provides a computing device, comprising: one or more processors;

[0042] a processor for executing one or more programs;

[0043] When the one or more programs are executed by the one or more processors, the above-described method is implemented.

[0044] In yet another aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed, the method described above is implemented.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] The present invention provides a method for optimizing control parameters of new energy sources for short-circuit current control, including: establishing a short-circuit current calculation data model for a regional power grid to be analyzed that includes a new energy station model; determining the objective function and constraints of the short-circuit current calculation data model; solving the short-circuit current calculation data model using a particle swarm algorithm to obtain an optimal solution; and adjusting the control parameters of new energy sources in the regional power grid based on the optimal solution to optimize the control parameters. The present invention can provide support for short-circuit current control of new energy power grids and optimization of new energy station control strategies, and can provide new technical means for short-circuit current control of new energy power grids, thereby ensuring the safe and stable operation of power grids with a high proportion of new energy sources. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 is a flow chart of the method of the present invention;

[0048] Figure 2 is a flow chart of an embodiment of the method of the present invention;

[0049] Figure 3 It is a structural diagram of the system of the present invention. DETAILED DESCRIPTION

[0050] Now, exemplary embodiments of the present invention are described with reference to the accompanying drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. These embodiments are provided to disclose the present invention in detail and completely and to fully convey the scope of the present invention to those skilled in the art. The terms used in the exemplary embodiments shown in the accompanying drawings are not intended to limit the present invention. In the accompanying drawings, the same units / elements are marked with the same reference numerals.

[0051] Unless otherwise specified, the terms (including technical terms) used herein have the commonly understood meanings to those skilled in the art. In addition, it is understood that the terms defined in commonly used dictionaries should be understood to have the same meanings as those in the context of the relevant fields, and should not be understood as idealized or overly formal meanings.

[0052] Embodiment 1:

[0053] The present invention proposes a method for optimizing the control parameters of a new energy source for short-circuit current control, such as Figure 1 As shown, including:

[0054] Step 1: Establish a short-circuit current calculation data model for the regional power grid to be analyzed that includes a new energy station model;

[0055] Step 2: determining the objective function and constraint conditions of the short-circuit current calculation data model;

[0056] Step 3: Use a particle swarm algorithm to solve the short-circuit current calculation data model to obtain an optimal solution;

[0057] Step 4: Adjust the control parameters of the renewable energy in the regional power grid based on the optimal solution to optimize the control parameters.

[0058] Among them, the formula of the objective function is as follows:

[0059] max F=S PV1 +S PV2

[0060] Among them, F is the total capacity of new energy grid-connected, S PV1 and S PV2 The capacity for connecting new energy sources to the power grid.

[0061] Before determining the objective function and constraint conditions of the short-circuit current calculation data model, the method further includes:

[0062] Determine the key nodes of the regional power grid to be analyzed that includes the new energy station model.

[0063] Among them, the key nodes include: nodes near the AC source grid connection point and nodes near the new energy power station outlet grid connection point.

[0064] Among them, the constraints include: the short-circuit current at key nodes does not exceed the standard constraint and the new energy does not disconnect from the grid during low-voltage operation.

[0065] Among them, the constraints for the short-circuit current of key nodes not to exceed the standard are as follows:

[0066]

[0067] Among them, I k1 is the short-circuit current of key node 1 in the regional power grid to be analyzed, I ki is the short-circuit current of the key node i in the regional power grid to be analyzed, I kn is the short-circuit current of the key node n in the regional power grid to be analyzed, S is the new energy access capacity of the regional power grid to be analyzed, k Lvrt is the reactive power support coefficient of each renewable energy station in the regional power grid to be analyzed, max Rated current for the station switch.

[0068] Among them, the constraints for new energy to be low-powered but not disconnected from the grid include:

[0069]

[0070] Among them, U k1 is the post-fault voltage of the key node 1 in the regional power grid to be analyzed, Ux i is the post-fault voltage of the key node i in the regional power grid to be analyzed, U kn is the post-fault voltage of the key node n in the regional power grid to be analyzed.

[0071] The present invention is further described below in conjunction with specific implementation cases:

[0072] Specific case flow: Figure 2 As shown, specifically including:

[0073] Step 1: Establish a short-circuit current calculation data model for the power grid to be analyzed including a new energy station model;

[0074] Step 2: Determine the objective function and constraints for adjusting the control parameters of the new energy grid to be analyzed, including:

[0075] (1) Determination of objective function:

[0076] max F=S PV1 +S PV2

[0077] Among them, S pv1 and S pv2 The capacity of the new energy grid to be connected. That is, it can ensure the short-circuit current after all the new energy grids to be studied are connected;

[0078] (2) Determination of key nodes:

[0079] 1) The area near the grid connection point of AC source (generator, phase regulator, etc.);

[0080] When a system fault occurs, the closer the fault location is to the source, the smaller the short-circuit impedance and the larger the short-circuit current. At this time, if the impact of fault feed from new energy sources such as new energy power stations is added, the short-circuit current will exceed the standard.

[0081] 2) Areas near the outlet and grid connection points of new energy power stations;

[0082] When a new energy power station (wind power, photovoltaic, energy storage) fails at its outlet, the short-circuit current level is most affected by the new energy power station, and the degree of influence by the AC source mainly depends on the distance from the fault point to the AC source. When the capacity of the new energy power station is large and the distance between the new energy power station and the AC source is close, when a fault occurs at the outlet, the maximum short-circuit current may appear at the circuit breaker position of the new energy station outlet line.

[0083] (3) The short-circuit current of key nodes does not exceed the limit;

[0084] The short-circuit current of the key nodes selected above does not exceed the standard

[0085]

[0086] Among them, I k1 is the short-circuit current of node 1 in the power grid, I ki is the short-circuit current of node i in the power grid, I kn is the short-circuit current of node n in the power grid, S is the new energy access capacity of the power grid, k Lvrt It is the reactive power support coefficient of each renewable energy station in the power grid.

[0087] (4) New energy low penetration without disconnection from the grid constraints;

[0088] According to the low voltage ride-through strategy, in order to ensure that the new energy (wind point) station does not disconnect from the grid after a fault, the voltage at the wind power grid connection point should be greater than the rated voltage of 0.2pu. According to the short-circuit characteristics, the closer the fault location, the smaller the fault transition resistance and the greater the voltage drop. Therefore, in order to ensure that the voltage drop after a fault satisfies the wind farm station and does not disconnect from the grid, the voltage constraint condition at the wind power grid connection point is obtained as follows:

[0089]

[0090] Among them, U k1 is the post-fault voltage of node 1 in the power grid, U ki is the post-fault voltage of node i in the power grid, U knis the post-fault voltage of node n in the power grid. Photovoltaic and energy storage power stations can meet the requirement of not disconnecting from the grid when the voltage drops to zero, so this requirement is not set.

[0091] Step 3: Initialize the particle swarm algorithm. Generate a particle swarm randomly, assuming that the swarm contains K particles in total.

[0092]

[0093]

[0094] Among them, P and v represent the position and velocity of the particle respectively, both of which are vectors. Each particle is recorded as

[0095] Step 4: Obtain the fitness value of each particle through system-level short-circuit current analysis and objective function calculation. The particle fitness is obtained by bringing the individual particle speed and position parameters into the objective function, and taking the calculated value of the new energy access capacity in the objective function as the individual fitness value.

[0096] Step 5: Obtain the optimal individual and global solutions in the particle swarm. Compare the maximum (minimum) value of fitness in the individual, which represents the optimal position experienced so far, that is, pbest(t), and calculate the maximum (minimum) value of fitness in the entire population, which represents the optimal position experienced by all particles so far, that is, gbest(t).

[0097] Step 6: Calculate the correction value of each particle parameter, update the particle speed and position according to the following formula through learning and updating, and obtain the new generation of particle swarm X(t).

[0098] v id (t) = v id (t-1)+c1rand()[pbest id (t-1)-p id (t-1)]

[0099] +c2rand()[gbest id (t-1)-p id (t-1)]

[0100] The first term is the original flying speed of the particle, reflecting the inertia of the particle. The c1 and c2 in the last two terms are learning factors, also known as acceleration constants, which represent the driving factors for learning from its own experience and the experience of other individuals in the population. The overall search capability of PSO depends on the balanced effect of the three factors in the speed update formula: the first term of the speed update formula corresponds to the diversity tendency of the search; the last two terms correspond to the centralization tendency of the search.

[0101] p id (t) = p id(t-1)+v id (t)

[0102] Step 7: Based on the updated particle swarm parameters, recalculate the individual optimum and the global optimum.

[0103] Step 8: Iteration termination judgment: If the number of iterations reaches the set maximum number of iterations or the error of the algorithm is within the set error tolerance range, output gbest and go to step 9, otherwise go to step 6.

[0104] Step 9: Output gbest as the global optimal solution;

[0105] Step 10: The method ends.

[0106] The present invention takes a domestic high-proportion new energy access receiving-end power grid as an example to analyze the applicability of the method proposed in the present invention. In the calculation example, there are 5 new energy stations, and the installed capacities of the stations are 100MVA, 300MVA, 300MVA, 200MVA, and 100MVA respectively; there is 1 220kV over-standard station, the short-circuit current calculated value is 51.15kA, the switch capacity limit is 50kA,, the short-circuit current control target value is no more than 47KA, and the short-circuit current over-standard value I k '=1.15kA. The typical value of the reactive power support coefficient of new energy stations is 1.5, and the typical value of the reactive current limiting coefficient is 1.0pu.

[0107] Taking the short-circuit current calculation program SCCP as an example, the implementation process of this patent method with the help of mainstream engineering practical calculation software is explained. In the SCCP short-circuit current calculation stability data, the new energy station stability model low-voltage ride-through reactive current control card LQ is adjusted to modify the new energy reactive support coefficient k Lvrt , as shown in Table 1 below;

[0108] Table 1

[0109]

[0110] Through steps 1 to 9 of the present invention, the short-circuit current of the key nodes where the short-circuit current of the new energy access system exceeds the standard under different combinations of reactive support coefficients and startup capacity of new energy stations is calculated by the particle swarm optimization algorithm, and the combination results are iteratively optimized according to the total startup capacity of new energy and the calculated value of short-circuit current. It can be seen from the calculated value after iterative convergence (iteration 82 times) that after adopting the method of the present invention, the short-circuit current of the exceeding station is within the control target value, and the full output operation demand of the nearby new energy is guaranteed, and the safe and stable operation demand of the system and the optimal consumption demand of new energy are guaranteed, which proves the effectiveness of the regional power grid new energy control parameter adjustment optimization method based on short-circuit current constraint proposed by the present invention.

[0111] The present invention aims at the short-circuit current control problem of the power grid after large-scale access of new energy, and proposes a method for adjusting and optimizing the new energy control parameters of a regional power grid based on short-circuit current constraints, which provides support for the short-circuit current control of the new energy power grid and the optimization of the control strategy of new energy sites. It can provide a new technical means for the short-circuit current control of the new energy power grid and ensure the safe and stable operation of a power grid with a high proportion of new energy.

[0112] Embodiment 2:

[0113] The present invention also proposes a system 200 for optimizing new energy control parameters for short-circuit current control, such as Figure 3 As shown, including:

[0114] A model building unit 201 is used to build a short-circuit current calculation data model for the regional power grid to be analyzed including the new energy station model;

[0115] A calculation unit 202, used to determine the objective function and constraint conditions of the short-circuit current calculation data model;

[0116] A solving unit 203, configured to solve the short-circuit current calculation data model using a particle swarm algorithm to obtain an optimal solution;

[0117] The optimization unit 204 is used to adjust the control parameters of the renewable energy in the regional power grid based on the optimal solution to optimize the control parameters.

[0118] Among them, the formula of the objective function is as follows:

[0119] maxF=S PV1 +S PV2

[0120] Among them, F is the total capacity of new energy grid-connected, S PV1 and S PV2 The capacity for connecting new energy sources to the power grid.

[0121] Before determining the objective function and constraint conditions of the short-circuit current calculation data model, the method further includes:

[0122] Determine the key nodes of the regional power grid to be analyzed that includes the new energy station model.

[0123] Among them, the key nodes include: nodes near the AC source grid connection point and nodes near the new energy power station outlet grid connection point.

[0124] Among them, the constraints include: the short-circuit current at key nodes does not exceed the standard constraint and the new energy does not disconnect from the grid during low-voltage operation.

[0125] Among them, the constraints for the short-circuit current of key nodes not to exceed the standard are as follows:

[0126]

[0127] Among them, I k1 is the short-circuit current of key node 1 in the regional power grid to be analyzed, I ki is the short-circuit current of the key node i in the regional power grid to be analyzed, I kn is the short-circuit current of the key node n in the regional power grid to be analyzed, S is the new energy access capacity of the regional power grid to be analyzed, k Lvrt is the reactive power support coefficient of each renewable energy station in the regional power grid to be analyzed, max Rated current for the station switch.

[0128] Among them, the constraints for new energy to be low-powered but not disconnected from the grid include:

[0129]

[0130] Among them, U k1 is the post-fault voltage of the key node 1 in the regional power grid to be analyzed, U ki is the post-fault voltage of the key node i in the regional power grid to be analyzed, U kn is the post-fault voltage of the key node n in the regional power grid to be analyzed.

[0131] The present invention can provide support for short-circuit current control of new energy power grids and optimization of control strategies for new energy stations, and can provide new technical means for short-circuit current control of new energy power grids, thereby ensuring the safe and stable operation of high-proportion new energy power grids.

[0132] Embodiment 3:

[0133] Based on the same inventive concept, the present invention also provides a computer device, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding functions, so as to implement the steps of the method in the above embodiment.

[0134] Embodiment 4:

[0135] Based on the same inventive concept, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It is understandable that the computer-readable storage medium here can include both a built-in storage medium in a computer device and an extended storage medium supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of the method in the above embodiment.

[0136] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The schemes in the embodiments of the present invention may be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0137] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0138] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0139] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0140] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0141] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for optimizing new energy control parameters for short-circuit current control, characterized in that: include: For the regional power grid to be analyzed that contains the new energy station model, a short-circuit current calculation data model is established; Determining the objective function and constraint conditions of the short-circuit current calculation data model; Using a particle swarm algorithm to solve the short-circuit current calculation data model to obtain an optimal solution; Based on the optimal solution, control parameters of the renewable energy in the regional power grid are adjusted to optimize the control parameters.

2. The method according to claim 1, characterized in that The objective function formula is as follows: max F=S PV1 +S PV2 Among them, F is the total capacity of new energy grid-connected, S PV1 and S PV2 The capacity for connecting new energy sources to the power grid.

3. The method according to claim 1, characterized in that Before determining the objective function and constraint conditions of the short-circuit current calculation data model, the method further includes: Determine the key nodes of the regional power grid to be analyzed that includes the new energy station model.

4. The method according to claim 3, characterized in that The key nodes include: nodes near the AC source grid connection point and nodes near the new energy power station outlet grid connection point.

5. The method according to claim 1, characterized in that The constraints include: the short-circuit current at key nodes does not exceed the standard and the new energy does not disconnect from the grid during low-voltage operation.

6. The method according to claim 5, characterized in that The constraint condition that the short-circuit current of the key node does not exceed the standard is as follows: Among them, I k1 is the short-circuit current of key node 1 in the regional power grid to be analyzed, I ki is the short-circuit current of the key node i in the regional power grid to be analyzed, I kn is the short-circuit current of the key node n in the regional power grid to be analyzed, S is the new energy access capacity of the regional power grid to be analyzed, k Lvrt is the reactive power support coefficient of each renewable energy station in the regional power grid to be analyzed, I max Rated current for the station switch.

7. The method according to claim 5, characterized in that The constraints for new energy to be low-powered but not disconnected from the grid include: Among them, U k1 is the post-fault voltage of the key node 1 in the regional power grid to be analyzed, U ki is the post-fault voltage of the key node i in the regional power grid to be analyzed, U kn is the post-fault voltage of the key node n in the regional power grid to be analyzed.

8. A system for optimizing new energy control parameters for short-circuit current control, characterized in that: include: Establish a model unit for establishing a short-circuit current calculation data model for the regional power grid to be analyzed that includes a new energy station model; A calculation unit, used to determine the objective function and constraint conditions of the short-circuit current calculation data model; A solving unit, used for solving the short-circuit current calculation data model using a particle swarm algorithm to obtain an optimal solution; An optimization unit is used to adjust the control parameters of the renewable energy in the regional power grid based on the optimal solution to optimize the control parameters.

9. The system according to claim 8, characterized in that The objective function formula is as follows: max F=S PV1 +S PV2 Among them, F is the total capacity of new energy grid-connected, S PV1 and S PV2 The capacity for connecting new energy sources to the power grid.

10. The system according to claim 8, characterized in that Before determining the objective function and constraint conditions of the short-circuit current calculation data model, the method further includes: Determine the key nodes of the regional power grid to be analyzed that includes the new energy station model.

11. The system according to claim 10, characterized in that The key nodes include: nodes near the AC source grid connection point and nodes near the new energy power station outlet grid connection point.

12. The system according to claim 10, characterized in that The constraints include: the short-circuit current at key nodes does not exceed the standard and the new energy does not disconnect from the grid during low-voltage operation.

13. The system according to claim 12, characterized in that The constraint condition that the short-circuit current of the key node does not exceed the standard is as follows: Among them, I k1 is the short-circuit current of key node 1 in the regional power grid to be analyzed, I ki is the short-circuit current of the key node i in the regional power grid to be analyzed, I kn is the short-circuit current of the key node n in the regional power grid to be analyzed, S is the new energy access capacity of the regional power grid to be analyzed, k Lvrt is the reactive power support coefficient of each renewable energy station in the regional power grid to be analyzed, max Rated current for the station switch.

14. The system according to claim 12, characterized in that The constraints for new energy to be low-powered but not disconnected from the grid include: Among them, U k1 is the post-fault voltage of the key node 1 in the regional power grid to be analyzed, U ki is the post-fault voltage of the key node i in the regional power grid to be analyzed, U kn is the post-fault voltage of the key node n in the regional power grid to be analyzed.

15. A computer device, characterized in that: include: one or more processors; a processor for executing one or more programs; When the one or more programs are executed by the one or more processors, the method according to any one of claims 1 to 7 is implemented.

16. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed, the method according to any one of claims 1 to 7 is implemented.

Images