CN-121984023-A - Distributed subnet virtual inertia generation method based on networking energy storage and related device

CN121984023ACN 121984023 ACN121984023 ACN 121984023ACN-121984023-A

Abstract

The application discloses a distributed sub-network virtual inertia generating method and a related device based on network formation energy storage, wherein the method comprises the steps of carrying out block coding on alternating current virtual inertia and direct current virtual inertia in a distributed sub-network to generate an initial particle swarm, wherein the initial particle swarm comprises an alternating current particle swarm and a direct current particle swarm; the method comprises the steps of constructing an objective function of a PSO algorithm based on frequency deviation, voltage deviation, power deviation and energy loss, configuring a plurality of constraint conditions to generate an improved PSO model, carrying out global and local optimization calculation on particle swarms of the improved PSO model according to a Gaussian-polynomial hybrid variation mechanism to generate optimized alternating current inertia and optimized direct current inertia, and distributing the optimized alternating current inertia and the optimized direct current inertia to each distributed sub-network for regulating stable operation of a system. The application can solve the technical problems that the dynamic distribution of virtual inertia among a plurality of sub-networks is difficult to realize, and the parameter optimization efficiency is low and is easy to fall into local optimum in the prior art.

Inventors

TANG QI
LI HUI
HU ZHIPENG
HE ZILAN
FU ZHENGXIN
ZENG QINGHUI
LI GUOWEI

Assignees

广东电网有限责任公司佛山供电局

Dates

Publication Date: 20260505
Application Date: 20260127

Claims (10)

1. The distributed subnet virtual inertia generation method based on the network formation energy storage is characterized by comprising the following steps of: s1, carrying out block coding on alternating current virtual inertia and direct current virtual inertia in a distributed sub-network to generate an initial particle swarm, wherein the initial particle swarm comprises an alternating current particle swarm and a direct current particle swarm; S2, constructing an objective function of a PSO algorithm based on frequency deviation, voltage deviation, power deviation and energy loss, and configuring a plurality of constraint conditions to generate an improved PSO model; S3, performing global and local optimization calculation on the particle swarm of the improved PSO model according to a Gaussian-polynomial mixed variation mechanism to generate optimized alternating current inertia and optimized direct current inertia; And S4, distributing the optimized alternating current inertia and the optimized direct current inertia to each distributed sub-network for regulating the stable operation of the system.
2. The method for generating virtual inertia of a distributed subnet based on network formation energy storage according to claim 1, wherein step S1 comprises: Performing block coding on alternating current virtual inertia and direct current virtual inertia in the distributed subnetwork to generate alternating current subnetwork vectors and direct current subnetwork vectors; Distributing dynamic inertia weight to the alternating current sub-network vector to generate an alternating current particle swarm; and distributing fixed weights to the direct current sub-network vectors to generate direct current particle swarms.
3. The method for generating virtual inertia of a distributed subnet based on network formation energy storage according to claim 1, wherein step S3 further comprises: calculating a sensitivity adjustment parameter according to the objective function and a preset gain coefficient; and dynamically correcting and distributing the optimized alternating-current inertia and the optimized direct-current inertia according to the sensitivity adjustment parameters.
4. The method for generating virtual inertia of a distributed subnet based on network formation energy storage according to claim 1, wherein step S4 further comprises: If the frequency deviation or the voltage deviation of any one of the distributed sub-networks exceeds the deviation threshold, returning to the step S2, updating the dynamic weight of the improved PSO model, and reallocating the virtual inertia.
5. Distributed subnet virtual inertia generating device based on network formation energy storage, which is characterized by comprising: the inertia coding unit is used for carrying out block coding on alternating current virtual inertia and direct current virtual inertia in the distributed sub-network to generate an initial particle swarm, wherein the initial particle swarm comprises an alternating current particle swarm and a direct current particle swarm; The model building unit is used for building an objective function of the PSO algorithm based on the frequency deviation, the voltage deviation, the power deviation and the energy loss, configuring a plurality of constraint conditions and generating an improved PSO model; The optimization calculation unit is used for carrying out global and local optimization calculation on the particle swarm of the improved PSO model according to a Gaussian-polynomial mixed variation mechanism to generate optimized alternating current inertia and optimized direct current inertia; And the inertia distribution unit is used for distributing the optimized alternating-current inertia and the optimized direct-current inertia to each distributed sub-network and is used for regulating the stable operation of the system.
6. The distributed subnet virtual inertia generating apparatus based on the network formation energy storage of claim 5, wherein the inertia encoding unit is specifically configured to: Performing block coding on alternating current virtual inertia and direct current virtual inertia in the distributed subnetwork to generate alternating current subnetwork vectors and direct current subnetwork vectors; Distributing dynamic inertia weight to the alternating current sub-network vector to generate an alternating current particle swarm; and distributing fixed weights to the direct current sub-network vectors to generate direct current particle swarms.
7. The distributed subnet virtual inertia generating apparatus based on the network formation energy storage of claim 5, further comprising: The parameter calculation unit is used for calculating a sensitivity adjustment parameter according to the objective function and a preset gain coefficient; And the correction distribution unit is used for dynamically correcting and distributing the optimized alternating-current inertia and the optimized direct-current inertia according to the sensitivity adjustment parameter.
8. The distributed subnet virtual inertia generating apparatus based on the network formation energy storage of claim 5, further comprising: And the updating distribution unit is used for triggering the model construction unit to update the dynamic weight of the improved PSO model and redistribute the virtual inertia if the frequency deviation or the voltage deviation of any one of the distributed sub-networks exceeds a deviation threshold value.
9. The distributed subnet virtual inertia generating device based on the networking energy storage is characterized by comprising a processor and a memory; the memory is used for storing program codes and transmitting the program codes to the processor; the processor is configured to execute the distributed subnet virtual inertia generating method based on the network-structured energy storage according to any one of claims 1 to 4 according to the instructions in the program code.
10. A computer readable storage medium for storing program code for performing the distributed subnet virtual inertia generation method based on the network formation energy storage of any one of claims 1 to 4.

Description

Distributed subnet virtual inertia generation method based on networking energy storage and related device Technical Field The application relates to the technical field of hybrid power grid control, in particular to a distributed sub-network virtual inertia generation method based on network formation energy storage and a related device. Background In the operation of an ac/dc series-parallel power grid or a multi-region series-parallel power grid, a conventional generator set provides moment of inertia to help maintain the stability of the power grid. However, due to the access of renewable energy sources and energy storage systems like wind power, photovoltaic and the like, the traditional generator sets gradually withdraw, resulting in a reduction of the moment of inertia of the power grid. Virtual moment of inertia generally refers to the simulation of the inertial response of a conventional synchronous generator by power electronics, helping the grid to remain stable as the frequency fluctuates. The ac-dc grid may comprise ac and dc sub-networks, such as a grid based on a voltage source converter HVDC transmission (Voltage Source Converter-High Voltage Direct Current, VSC-HVDC) control system. The grid-built energy storage system can be regarded as a system based on a voltage source converter (Voltage Source Converter, VSC), and can actively support and regulate the frequency and voltage of a power grid, and the power electronic equipment contained in the grid-built energy storage system can simulate the inertial response of a traditional synchronous generator, namely, provide virtual moment of inertia. Therefore, the network energy storage system can automatically adjust parameters, virtual rotational inertia is provided for the series-parallel power grid, and the virtual rotational inertia can be automatically distributed among different power grids, so that the running stability of the power grid in the distributed energy high-permeability area can be effectively improved. However, the existing virtual inertia control method is difficult to realize dynamic coordination distribution among multiple sub-networks in the hybrid power grid, and the high-dimensional parameter optimization efficiency is low, the local optimization is easy to fall into, and the actual virtual inertia control process lacks of high efficiency, accuracy and reliability. Disclosure of Invention The application provides a distributed subnet virtual inertia generation method based on network formation energy storage and a related device, which are used for solving the technical problems that dynamic coordination and distribution of virtual inertia among a plurality of distributed subnets are difficult to realize, parameter optimization efficiency is low, and local optimization is easy to fall into. In view of this, the first aspect of the present application provides a distributed subnet virtual inertia generating method based on network formation energy storage, including: s1, carrying out block coding on alternating current virtual inertia and direct current virtual inertia in a distributed sub-network to generate an initial particle swarm, wherein the initial particle swarm comprises an alternating current particle swarm and a direct current particle swarm; S2, constructing an objective function of a PSO algorithm based on frequency deviation, voltage deviation, power deviation and energy loss, and configuring a plurality of constraint conditions to generate an improved PSO model; S3, performing global and local optimization calculation on the particle swarm of the improved PSO model according to a Gaussian-polynomial mixed variation mechanism to generate optimized alternating current inertia and optimized direct current inertia; And S4, distributing the optimized alternating current inertia and the optimized direct current inertia to each distributed sub-network for regulating the stable operation of the system. Preferably, step S1 comprises: Performing block coding on alternating current virtual inertia and direct current virtual inertia in the distributed subnetwork to generate alternating current subnetwork vectors and direct current subnetwork vectors; Distributing dynamic inertia weight to the alternating current sub-network vector to generate an alternating current particle swarm; and distributing fixed weights to the direct current sub-network vectors to generate direct current particle swarms. Preferably, step S3 further comprises: calculating a sensitivity adjustment parameter according to the objective function and a preset gain coefficient; and dynamically correcting and distributing the optimized alternating-current inertia and the optimized direct-current inertia according to the sensitivity adjustment parameters. Preferably, step S4 further comprises: If the frequency deviation or the voltage deviation of any one of the distributed sub-networks exceeds the deviation threshold, returning to the step S2, updating