Power grid operation mode switching method, equipment and medium

Abstract

The invention relates to the field of power grid operation, and discloses a power grid operation mode switching method and device and a medium, and the method comprises the steps: obtaining the operation data of a power system, and constructing a full-order transient model according to the operation data of the power system; acquiring multi-working-condition data, and obtaining a state trajectory data set according to the multi-working-condition data and the full-order transient model; according to the state track data set, adopting a subspace dynamic mode decomposition algorithm to obtain an order reduction model; optimizing a preset power grid operation mode according to the reduced-order model by adopting a particle swarm optimization algorithm coordinated by various topological structures to obtain an optimal operation parameter of the power system; and switching the operation mode of the power grid according to the optimal operation parameters. According to the invention, multi-scene comprehensive coverage can be realized, diversified operation requirements of a power grid are met, and calculation efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of power grid operation, and in particular to a method, device and medium for switching power grid operation modes. Background Technology

[0002] In recent years, with the rapid development of the economy and society, electricity demand has continued to grow, the scale of the power grid has been expanding, and its structure has become increasingly complex. On the one hand, a large number of new energy power plants (such as photovoltaic power plants and wind farms) have been connected to the power grid, changing the power supply structure of the traditional power grid. The intermittency, randomness, and volatility of new energy power generation have brought new challenges to the stable operation of the power grid. On the other hand, the emergence of new power grid forms such as AC / DC hybrid power grids and distributed power grids has made the power grid topology more complex and variable. During power grid operation, switching operations such as tie line opening and closing, bus operation mode adjustment, power source start-up and shutdown, and output changes occur frequently. Traditional power grid cross-section transition simulation methods are difficult to accurately and quickly simulate the impact of these complex operations on the power grid operation status, and cannot provide reliable technical support for power grid dispatching decisions.

[0003] Traditional power grid cross-section transition simulation methods suffer from numerous shortcomings in data processing, model building, algorithm optimization, simulation verification, and application. In data processing, computational efficiency is low; during model building, the description of equipment characteristics is inaccurate, failing to adapt to complex operating conditions and extreme scenarios; the simulation verification process is incomplete, with limited application scenario coverage, and the simulation results lack visualization, making it difficult to meet the needs of modern power grid operation and management. Therefore, there is an urgent need to construct a power grid cross-section transition simulation method that can effectively process power grid data from multiple scenarios, improve computational efficiency, and possess strong global search and local optimization capabilities, thereby enhancing the intelligence level of power grid dispatching. Summary of the Invention

[0004] This invention aims to at least solve one of the technical problems existing in the prior art. To this end, this invention proposes a method for switching power grid operation modes, which can achieve comprehensive coverage of multiple scenarios, meet the diverse operational needs of the power grid, and improve computational efficiency.

[0005] The present invention also proposes equipment and media having the above-mentioned method for switching power grid operation modes.

[0006] A method for switching power grid operation modes according to a first aspect embodiment of the present invention, applied to a power system, includes: Acquire power system operation data and construct a full-order transient model based on the power system operation data; Acquire multi-condition data, and obtain a state trajectory dataset based on the multi-condition data and the full-order transient model; Based on the state trajectory dataset, a subspace dynamic mode decomposition algorithm is used to obtain a reduced-order model; A particle swarm optimization algorithm with multiple topologies is used to optimize the preset power grid operation mode based on the reduced-order model to obtain the optimal operating parameters of the power system. Based on the optimal operating parameters, switch the power grid operation mode.

[0007] According to an embodiment of the present invention, a method for switching power grid operation modes has at least the following beneficial effects: First, the present invention collects power system operation data, builds a full-order transient model, applies multiple operating conditions to the full-order model, generates multiple sets of state trajectory data, and uses a subspace dynamic mode decomposition algorithm to extract the dominant mode, constructing a reduced-order model to improve computational efficiency. Then, it uses a particle swarm optimization algorithm with multiple topologies for optimization, and applies the optimized operation mode switching scheme to the power grid. The present invention significantly shortens computation time and improves simulation efficiency, while more accurately finding the optimal scheme for switching power grid operation modes. Furthermore, it can achieve comprehensive coverage of multiple scenarios, whether for routine operation mode adjustments or for dealing with special situations such as new energy fluctuations and faults, meeting the diverse operational needs of the power grid.

[0008] According to some embodiments of the present invention, acquiring power system operating data and constructing a full-order transient model based on the power system operating data includes: Obtain operational data of the power system; The power system's operational data is cleaned and integrated sequentially. Based on the cleaned and integrated operating data, power simulation is performed to obtain a full-order transient model.

[0009] According to some embodiments of the present invention, the step of acquiring multi-condition data and obtaining a state trajectory dataset based on the multi-condition data and the full-order transient model includes: Acquire multi-condition data at a preset time step; wherein, the state trajectory dataset includes: multi-condition data; The multi-condition data is input into the full-order transient model to obtain the state variables output by the full-order transient model. The state trajectory dataset also includes the state variables output by the full-order transient model.

[0010] According to some embodiments of the present invention, obtaining a reduced-order model by employing a subspace dynamic mode decomposition algorithm based on the state trajectory dataset includes: The state trajectory data in the state trajectory dataset are standardized respectively; The standardized state trajectory data are arranged in chronological order. An input matrix is ​​constructed based on the multi-condition data in the state trajectory data, and an output matrix is ​​constructed based on the state variables output by the full-order transient model in the state trajectory data. Based on the input matrix and the output matrix, the subspace dynamic mode decomposition matrix is ​​obtained; For each row in the subspace dynamic mode decomposition matrix, the mode corresponding to the row whose amplitude and attenuation both meet the preset conditions is taken as the dominant mode; A reduced-order model is obtained by combining the dominant modes.

[0011] According to some embodiments of the present invention, obtaining the subspace dynamic mode decomposition matrix based on the input matrix and the output matrix includes: The input matrix is ​​subjected to singular value decomposition to obtain a first singular matrix, a second singular matrix, and a third singular matrix; The result of the vector product of the inverted matrix of the first singular matrix, the output matrix, the third singular matrix, and the inverse matrix of the second singular matrix is ​​used as the subspace dynamic mode decomposition matrix.

[0012] According to some embodiments of the present invention, the particle swarm optimization algorithm employing multiple topologies in coordination optimizes a preset power grid operation mode based on the reduced-order model to obtain the optimal operating parameters of the power system, including: Determine the decision variables in the operational data, and treat each power grid operation mode as a particle in the particle swarm optimization algorithm; By employing a variety of topological structure cooperative strategies, each particle is input into the reduced-order model for iterative calculation to obtain the local optimal solution corresponding to each particle. The local optimum with the highest fitness value among several local optima is taken as the global optimum, which is the optimal operating parameter of the power system.

[0013] According to some embodiments of the present invention, the step of employing multiple topological structure cooperative strategies to input each particle into the reduced-order model for iterative calculation to obtain the local optimal solution corresponding to each particle includes: The system employs a globally optimal topology and iterates to optimize decision variables. Switch to Feng Neumann topology replaces certain information in a particle to obtain the particle for the next iteration; Switch to a locally optimal topology, and the particles are updated within a preset range.

[0014] According to some embodiments of the present invention, the method further includes: after optimizing a preset power grid operation mode using a particle swarm optimization algorithm with multiple topologies based on the reduced-order model to obtain the optimal operating parameters of the power system, inputting the optimal operating parameters of the power system into the full-order transient model, and verifying the optimal operating parameters of the power system based on the deviation between the output of the full-order transient model and the output of the reduced-order model.

[0015] An electronic device according to a second aspect of the present invention includes: Memory, used to store programs; A processor for executing a program stored in the memory, wherein when the processor executes the program stored in the memory, the processor is configured to perform the method as described in any one of the first aspects.

[0016] According to a third aspect of the present invention, a storage medium stores computer-executable instructions for performing the method as described in any one of the first aspects.

[0017] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the description, claims, and drawings. Attached Figure Description

[0018] The accompanying drawings are provided to further understand the technical solutions of the present invention and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the technical solutions of the present invention, and do not constitute a limitation on the technical solutions of the present invention.

[0019] Figure 1 This is a flowchart of a method for switching power grid operation modes provided in an embodiment of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0021] It should be understood that in the description of the embodiments of the present invention, "multiple" (or "amounts") means two or more, "greater than," "less than," and "exceeding" are understood to exclude the stated number, while "above," "below," and "within" are understood to include the stated number. If "first," "second," etc., are used in the description, they are only for the purpose of distinguishing technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or implicitly indicating the order of the indicated technical features.

[0022] like Figure 1 As shown, this embodiment of the invention provides a method for switching power grid operation modes, applied to a power system, including: Step S100: Obtain the operating data of the power system and construct a full-order transient model based on the operating data of the power system; Step S200: Obtain multi-condition data; based on the multi-condition data and the full-order transient model, obtain the state trajectory dataset. Step S300: Based on the state trajectory dataset, a subspace dynamic mode decomposition algorithm is used to obtain a reduced-order model; Step S400: Using a particle swarm optimization algorithm with multiple topologies, optimize the preset power grid operation mode based on the reduced-order model to obtain the optimal operating parameters of the power system; Step S500: Switch the power grid operation mode according to the optimal operating parameters.

[0023] This invention first collects power system operation data, builds a full-order transient model, applies multiple operating conditions to the full-order model to generate multiple sets of state trajectory data, and uses a subspace dynamic mode decomposition algorithm to extract the dominant mode and construct a reduced-order model. Then, it uses a particle swarm optimization algorithm with multiple topologies for optimization, and applies the optimized operation mode switching scheme to the power grid. This invention significantly shortens the computation time and improves simulation efficiency, while more accurately finding the optimal scheme for power grid operation mode switching. In addition, it can achieve comprehensive coverage of multiple scenarios, whether for routine operation mode adjustments or to cope with special situations such as new energy fluctuations and faults, meeting the diverse operation needs of the power grid.

[0024] It is easy to understand that the power system includes the power grid.

[0025] In one embodiment, in step S100, acquiring the operating data of the power system and constructing a full-order transient model based on the operating data of the power system includes: Obtain operational data of the power system; The power system's operational data is cleaned and integrated sequentially. Based on the cleaned and integrated operating data, power simulation is performed to obtain a full-order transient model.

[0026] In one embodiment, the power system operation data is cleaned and integrated sequentially, specifically including: using the moving average method to remove noise from the operation data, and using the 3σ principle to identify and remove outliers to ensure data accuracy; The power system's operational data is integrated by equipment entity and time dimension; Power simulation was performed using PSASP or PSCAD / EMTDC platforms to construct full-order transient models including new energy power plants and AC / DC converter stations; In the full-order transient model, the Park equation is used to describe the dynamic process of the synchronous generator, the controlled source model simulates the photovoltaic / wind power output, and the node admittance matrix represents the grid topology, thus fully presenting the grid operating state before the switchover.

[0027] In one embodiment, in step S200, multi-condition data is acquired, and based on the multi-condition data and the full-order transient model, a state trajectory dataset is obtained, including: Acquire multi-condition data at a preset time step; the state trajectory dataset includes: multi-condition data; Multi-condition data is input into the full-order transient model to obtain the state variables output by the full-order transient model. The state trajectory dataset also includes the state variables output by the full-order transient model.

[0028] It should be noted that the state variables, including the voltage amplitude and phase of each node, line power flow, generator power angle and speed, and output power of new energy power plants, generate at least 1,000 time points of state data for each operating condition, forming multiple sets of state trajectory datasets to apply various perturbations to the full-order model; among them, the perturbation operating conditions refer to the operating conditions in the multi-operating condition data, specifically including: load change, short circuit fault, abnormal generator excitation regulation, and output fluctuation of new energy power plants.

[0029] In one embodiment, in step S100, the power system operation data includes: power system grid data, power source data, and operation status data.

[0030] In one embodiment, power system network data refers to the power grid topology, specifically including: line parameters (length, impedance, current carrying capacity), bus connection relationships, tie line configuration; transformer tap range, adjustment step size; The power data specifically includes: the rated power, moment of inertia, and excitation system parameters of synchronous generators; the component parameters and solar power curves of photovoltaic power plants; the turbine models and wind speed-power characteristics of wind farms; and the capacity and control strategy parameters of AC / DC converter stations, covering various types of power supply operating characteristic data.

[0031] The operational status data is real-time and historical power grid operational data obtained through the EMS system, specifically including: voltage amplitude and phase at each node, line power flow, generator output, load curves, etc., with a sampling frequency of no less than 1 time / second, providing a realistic operational background for the simulation.

[0032] In one embodiment, in step S300, the subspace dynamic mode decomposition algorithm is used to obtain a reduced-order model based on the state trajectory dataset, including: Standardize the state trajectory data in the state trajectory dataset; The standardized state trajectory data are arranged in chronological order. An input matrix is ​​constructed based on the multi-condition data in the state trajectory data, and an output matrix is ​​constructed based on the state variables output by the full-order transient model in the state trajectory data. Based on the input matrix and the output matrix, the subspace dynamic mode decomposition matrix is ​​obtained; For each row in the subspace dynamic mode decomposition matrix, the mode corresponding to the row whose amplitude and attenuation both meet the preset conditions is taken as the dominant mode; A reduced-order model is obtained by combining the dominant modes.

[0033] First, standardization is performed to map the values ​​of different physical quantities to a unified range. Then, input matrix X and output matrix Y are constructed. Where X=[x(1),x(2),...x(m)], Y=[y(1),y(2),...y(m)], and m is the number of time steps recorded under each working condition. Then, mode decomposition is performed to obtain the subspace dynamic mode decomposition matrix. Each row in the subspace dynamic mode decomposition matrix represents the change of different variables (i.e., modes). Eigenvalue decomposition is performed on the subspace dynamic mode decomposition matrix to obtain eigenvalues ​​and eigenvectors. Modes with larger eigenvalue amplitudes and slower decay are selected to ensure that the reduced-order model can retain the key dynamic characteristics of the system. The mode that plays a dominant role in the dynamic behavior of the system is selected as the dominant mode.

[0034] In one embodiment, obtaining the subspace dynamic mode decomposition matrix based on the input matrix and the output matrix includes: Perform singular value decomposition (SVD) on the input matrix to obtain the first singular matrix. Second singular matrix The third singular matrix ; The vector product of the inverted matrix of the first singular matrix, the output matrix, the third singular matrix, and the inverse matrix of the second singular matrix is ​​used as the subspace dynamic mode decomposition matrix, specifically: ,in, The subspace dynamic mode decomposition matrix is... It is the inverse of the first singular matrix. For the output matrix, It is the third singular matrix. It is the inverse of the second singular matrix.

[0035] In one embodiment, in step S400, a particle swarm optimization algorithm with multiple topologies is used to optimize a preset power grid operation mode based on a reduced-order model, resulting in the following optimal operating parameters for the power system: Determine the decision variables in the operational data and treat each power grid operation mode as a particle in the particle swarm algorithm; By employing a multi-topology collaborative strategy, each particle is input into a reduced-order model for iterative calculation, thereby obtaining the local optimal solution corresponding to each particle. The local optimum with the highest fitness value among several local optima is taken as the global optimum, which is the optimal operating parameter of the power system.

[0036] In one embodiment, the decision variables include: tie line opening and closing status, busbar operation mode, transformer tap position, power supply start / stop and output adjustment amount; The decision variables in the operational data are determined, and each power grid operation mode is treated as a particle in the particle swarm optimization algorithm. Specifically, the operation mode switching optimization model is constructed using tie line opening and closing status, bus operation mode, transformer tap position, power supply start-up and shutdown and output adjustment amount, etc. as decision variables.

[0037] It is easy to understand that optimizing the power grid operation mode actually refers to optimizing the decision variables. The preset power grid operation mode means that the decision variables have been set, but the specific values ​​of the decision variables need to be searched. That is, the optimal operating parameters of the power system are actually the values ​​of each decision variable.

[0038] In one embodiment, a multi-topology cooperative strategy is employed, and each particle is input into a reduced-order model for iterative calculation to obtain the local optimal solution corresponding to each particle, including: In the initial stage of iteration (0-30% of the iterations): the globally optimal topology is used to iterate with the goal of optimizing the decision variables; Mid-cycle (30%-70% of iterations): Switch to Feng Neumann topology replaces certain information in a particle to obtain the particle for the next iteration; Later stages of iteration (70%-100% of iterations): Switch to local optimal topology, and the particles are updated within a preset range. For example, if the length of a certain path of a particle in a certain iteration is 'a', then the length of that path of the particle in the next iteration will be 'a+0.01', which strengthens local optimization and improves the accuracy of the solution.

[0039] It's easy to understand that the particle swarm parameters are all preset values, including the number of particles, the maximum number of iterations, and velocity boundaries. Some information from the particles is used to replace the particles in the next iteration. For example, the line length of the particle in the nth iteration is L and the line impedance is R1. The line length of the particle in the (n+1)th iteration is L and the line impedance is R2, where the line impedance is replaced. When the particle swarm algorithm gets stuck in a local optimum (the optimal solution has not been updated for 5 consecutive iterations), a mutation operation is introduced or some particles are restarted to escape the local optimum and continue searching for the global optimum.

[0040] In one embodiment, the method further includes: after optimizing the preset power grid operation mode using a particle swarm optimization algorithm with multiple topologies according to the reduced-order model to obtain the optimal operating parameters of the power system, inputting the optimal operating parameters of the power system into the full-order transient model, and verifying the optimal operating parameters of the power system based on the deviation between the output of the full-order transient model and the output of the reduced-order model.

[0041] It should be noted that the output of the full-order transient model and the output of the reduced-order model are actually simulated power system operating data, which refer to the real-time operating data of the power system after adjusting the operating parameters of the power system (equivalent to the input of the model).

[0042] In one embodiment, verifying the optimal operating parameters of the power system based on the deviation between the output of the full-order transient model and the output of the reduced-order model includes: if the voltage deviation is ≤3% and the power flow deviation is ≤5%, then the verification of the optimal operating parameters of the power system is successful; otherwise, it is unsuccessful.

[0043] The method also includes: in the process of verifying the optimal operating parameters, setting up a variety of extreme scenarios (such as N-1 faults, large-scale power generation and shutdown of new energy sources) for the full-order transient model, performing robustness verification on the optimal operating parameters, and evaluating the adaptability of the scheme under complex working conditions.

[0044] This invention also provides an electronic device, which includes, but is not limited to: Memory, used to store programs; The processor is used to execute programs stored in memory. When the processor executes the programs stored in memory, it is used to execute one of the above-mentioned methods for switching power grid operation modes.

[0045] The processor and memory can be connected via a bus or other means.

[0046] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs, such as the method described in the embodiments of the present invention. The processor implements the above method by running the non-transitory software program and instructions stored in the memory.

[0047] The memory may include a program storage area and a data storage area, wherein the program storage area may store the operating system and application programs required for at least one function; the data storage area may store data for executing the methods described above. Furthermore, the memory may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory may optionally include memory remotely located relative to the processor, which can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0048] The non-transitory software program and instructions required to implement the above terminal selection method are stored in memory and are executed by one or more processors.

[0049] This invention also provides a storage medium storing computer-executable instructions for performing the above-described methods.

[0050] In one embodiment, the storage medium stores computer-executable instructions that are executed by one or more control processors.

[0051] The embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0052] It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically include computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0053] This document describes embodiments of the invention, including preferred embodiments known to the inventors for carrying out the invention. Variations of these embodiments will become apparent to those skilled in the art upon reading the foregoing description. The inventors encourage those skilled in the art to adopt such variations as appropriate, and the inventors intend to practice embodiments of the invention in ways other than those specifically described herein. Therefore, the scope of the invention includes all modifications and equivalents of the subject matter set forth in the appended claims, as permitted by applicable law. Furthermore, the scope of the invention covers any combination of the foregoing elements in all possible variations thereof, unless otherwise indicated herein or otherwise clearly contradicted by the context.

Claims

1. A method for switching power grid operation modes, applied to a power system, characterized in that, include: Acquire power system operation data and construct a full-order transient model based on the power system operation data; Acquire multi-condition data, and obtain a state trajectory dataset based on the multi-condition data and the full-order transient model; Based on the state trajectory dataset, a subspace dynamic mode decomposition algorithm is used to obtain a reduced-order model; A particle swarm optimization algorithm with multiple topologies is used to optimize the preset power grid operation mode based on the reduced-order model to obtain the optimal operating parameters of the power system. Based on the optimal operating parameters, switch the power grid operation mode.

2. The method for switching power grid operation modes according to claim 1, characterized in that, The acquisition of power system operation data and the construction of a full-order transient model based on the power system operation data include: Obtain operational data of the power system; The power system's operational data is cleaned and integrated sequentially. Based on the cleaned and integrated operating data, power simulation is performed to obtain a full-order transient model.

3. The method for switching power grid operation modes according to claim 1, characterized in that, The acquisition of multi-condition data, and the generation of a state trajectory dataset based on the multi-condition data and the full-order transient model, includes: Acquire multi-condition data at a preset time step; wherein, the state trajectory dataset includes: multi-condition data; The multi-condition data is input into the full-order transient model to obtain the state variables output by the full-order transient model. The state trajectory dataset also includes the state variables output by the full-order transient model.

4. The method for switching power grid operation modes according to claim 3, characterized in that, The step of obtaining a reduced-order model by using a subspace dynamic mode decomposition algorithm based on the state trajectory dataset includes: The state trajectory data in the state trajectory dataset are standardized respectively; The standardized state trajectory data are arranged in chronological order. An input matrix is ​​constructed based on the multi-condition data in the state trajectory data, and an output matrix is ​​constructed based on the state variables output by the full-order transient model in the state trajectory data. Based on the input matrix and the output matrix, the subspace dynamic mode decomposition matrix is ​​obtained; For each row in the subspace dynamic mode decomposition matrix, the mode corresponding to the row whose amplitude and attenuation both meet the preset conditions is taken as the dominant mode; A reduced-order model is obtained by combining the dominant modes.

5. The method for switching power grid operation modes according to claim 4, characterized in that, The step of obtaining the subspace dynamic mode decomposition matrix based on the input matrix and the output matrix includes: The input matrix is ​​subjected to singular value decomposition to obtain a first singular matrix, a second singular matrix, and a third singular matrix; The result of the vector product of the inverted matrix of the first singular matrix, the output matrix, the third singular matrix, and the inverse matrix of the second singular matrix is ​​used as the subspace dynamic mode decomposition matrix.

6. The method for switching power grid operation modes according to claim 1, characterized in that, The particle swarm optimization algorithm employing multiple topologies works in concert to optimize the preset power grid operation mode based on the reduced-order model, obtaining the optimal operating parameters of the power system, including: Determine the decision variables in the operational data, and treat each power grid operation mode as a particle in the particle swarm optimization algorithm; By employing a variety of topological structure cooperative strategies, each particle is input into the reduced-order model for iterative calculation to obtain the local optimal solution corresponding to each particle. The local optimum with the highest fitness value among several local optima is taken as the global optimum, which is the optimal operating parameter of the power system.

7. The method for switching power grid operation modes according to claim 6, characterized in that, The method employs a multi-topology collaborative strategy, inputting each particle into the reduced-order model for iterative calculation to obtain the local optimal solution corresponding to each particle, including: The system employs a globally optimal topology and iterates to optimize decision variables. Switch to Feng Neumann topology replaces certain information in a particle to obtain the particle for the next iteration; Switch to a locally optimal topology, and the particles are updated within a preset range.

8. The method for switching power grid operation modes according to claim 1, characterized in that, The method further includes: after optimizing the preset power grid operation mode using a particle swarm optimization algorithm with multiple topologies according to the reduced-order model to obtain the optimal operating parameters of the power system, the optimal operating parameters of the power system are input into the full-order transient model, and the optimal operating parameters of the power system are verified based on the deviation between the output of the full-order transient model and the output of the reduced-order model.

9. An electronic device, characterized in that, include: Memory, used to store programs; A processor for executing a program stored in the memory, wherein when the processor executes the program stored in the memory, the processor is configured to perform the method as described in any one of claims 1 to 8.

10. A storage medium, characterized in that, The device stores computer-executable instructions for performing the method as described in any one of claims 1 to 8.

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